From 91392f39f2d0b35c0644a5865f44c14a0f5abebb Mon Sep 17 00:00:00 2001 From: sphynxlee Date: Wed, 27 Mar 2024 13:37:43 -0300 Subject: [PATCH 01/17] Created using Colaboratory --- LipNet.ipynb | 5064 ++++++++++++++++++++++++++++++++++++++------------ 1 file changed, 3847 insertions(+), 1217 deletions(-) diff --git a/LipNet.ipynb b/LipNet.ipynb index b34f4134..8bf7b07c 100644 --- a/LipNet.ipynb +++ b/LipNet.ipynb @@ -1,1220 +1,3850 @@ { - "cells": [ - { - "cell_type": "markdown", - "id": "a3573a47-3689-4668-b62f-5c8451b2b4e9", - "metadata": { - "tags": [] - }, - "source": [ - "# 0. Install and Import Dependencies" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ddfbccbe-41ae-4c23-98b1-a13868e2b499", - "metadata": { - "scrolled": true, - "tags": [] - }, - "outputs": [], - "source": [ - "!pip list" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "02f907ea-f669-46c7-adcf-7f257e663448", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "!pip install opencv-python matplotlib imageio gdown tensorflow" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b24af50c-20b8-409d-ad78-30a933fdd669", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "import os\n", - "import cv2\n", - "import tensorflow as tf\n", - "import numpy as np\n", - "from typing import List\n", - "from matplotlib import pyplot as plt\n", - "import imageio" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1e3db0b0-e559-4ad6-91fd-e7414b7d75e6", - "metadata": {}, - "outputs": [], - "source": [ - "tf.config.list_physical_devices('GPU')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "378d045a-3003-4f93-b7d2-a25a97774a68", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "physical_devices = tf.config.list_physical_devices('GPU')\n", - "try:\n", - " tf.config.experimental.set_memory_growth(physical_devices[0], True)\n", - "except:\n", - " pass" - ] - }, - { - "cell_type": "markdown", - "id": "7a19e88e-c7b9-45c1-ae1e-f2109329c71b", - "metadata": { - "tags": [] - }, - "source": [ - "# 1. Build Data Loading Functions" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8fb99c90-e05a-437f-839d-6e772f8c1dd5", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "import gdown" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c019e4c6-2af3-4160-99ea-5c8cb009f1a7", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "url = 'https://drive.google.com/uc?id=1YlvpDLix3S-U8fd-gqRwPcWXAXm8JwjL'\n", - "output = 'data.zip'\n", - "gdown.download(url, output, quiet=False)\n", - "gdown.extractall('data.zip')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8548cc59-6dfc-4acc-abc3-3e65212db02e", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "def load_video(path:str) -> List[float]: \n", - "\n", - " cap = cv2.VideoCapture(path)\n", - " frames = []\n", - " for _ in range(int(cap.get(cv2.CAP_PROP_FRAME_COUNT))): \n", - " ret, frame = cap.read()\n", - " frame = tf.image.rgb_to_grayscale(frame)\n", - " frames.append(frame[190:236,80:220,:])\n", - " cap.release()\n", - " \n", - " mean = tf.math.reduce_mean(frames)\n", - " std = tf.math.reduce_std(tf.cast(frames, tf.float32))\n", - " return tf.cast((frames - mean), tf.float32) / std" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ec735e0b-ec98-4eb0-8f49-c35527d6670a", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "vocab = [x for x in \"abcdefghijklmnopqrstuvwxyz'?!123456789 \"]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "be04e972-d7a5-4a72-82d8-a6bdde1f3ce6", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "char_to_num = tf.keras.layers.StringLookup(vocabulary=vocab, oov_token=\"\")\n", - "num_to_char = tf.keras.layers.StringLookup(\n", - " vocabulary=char_to_num.get_vocabulary(), oov_token=\"\", invert=True\n", - ")\n", - "\n", - "print(\n", - " f\"The vocabulary is: {char_to_num.get_vocabulary()} \"\n", - " f\"(size ={char_to_num.vocabulary_size()})\"\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "559f7420-6802-45fa-9ca0-b1ff209b461c", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "char_to_num.get_vocabulary()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "797ff78b-b48f-4e14-bb62-8cd0ebf9501a", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "char_to_num(['n','i','c','k'])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8cd7f4f4-ae77-4509-a4f4-c723787ebad1", - "metadata": {}, - "outputs": [], - "source": [ - "num_to_char([14, 9, 3, 11])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9491bab5-6a3c-4f79-879a-8f9fbe73ae2e", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "def load_alignments(path:str) -> List[str]: \n", - " with open(path, 'r') as f: \n", - " lines = f.readlines() \n", - " tokens = []\n", - " for line in lines:\n", - " line = line.split()\n", - " if line[2] != 'sil': \n", - " tokens = [*tokens,' ',line[2]]\n", - " return char_to_num(tf.reshape(tf.strings.unicode_split(tokens, input_encoding='UTF-8'), (-1)))[1:]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "dd01ca9f-77fb-4643-a2aa-47dd82c5d66b", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "def load_data(path: str): \n", - " path = bytes.decode(path.numpy())\n", - " #file_name = path.split('/')[-1].split('.')[0]\n", - " # File name splitting for windows\n", - " file_name = path.split('\\\\')[-1].split('.')[0]\n", - " video_path = os.path.join('data','s1',f'{file_name}.mpg')\n", - " alignment_path = os.path.join('data','alignments','s1',f'{file_name}.align')\n", - " frames = load_video(video_path) \n", - " alignments = load_alignments(alignment_path)\n", - " \n", - " return frames, alignments" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8cb7cc58-31ae-4904-a805-1177a82717d2", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "test_path = '.\\\\data\\\\s1\\\\bbal6n.mpg'" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "76aa964f-0c84-490d-897a-d00e3966e2c9", - "metadata": {}, - "outputs": [], - "source": [ - "tf.convert_to_tensor(test_path).numpy().decode('utf-8').split('\\\\')[-1].split('.')[0]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "eb602c71-8560-4f9e-b26b-08202febb937", - "metadata": { - "scrolled": true, - "tags": [] - }, - "outputs": [], - "source": [ - "frames, alignments = load_data(tf.convert_to_tensor(test_path))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0e3184a1-6b02-4b4f-84a8-a0a65f951ea2", - "metadata": {}, - "outputs": [], - "source": [ - "plt.imshow(frames[40])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d7ec0833-d54b-4073-84cf-92d011c60ec1", - "metadata": {}, - "outputs": [], - "source": [ - "alignments" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "fe1ad370-b287-4b46-85a2-7c45b0bd9b10", - "metadata": {}, - "outputs": [], - "source": [ - "tf.strings.reduce_join([bytes.decode(x) for x in num_to_char(alignments.numpy()).numpy()])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6871031a-b0ba-4c76-a852-f6329b0f2606", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "def mappable_function(path:str) ->List[str]:\n", - " result = tf.py_function(load_data, [path], (tf.float32, tf.int64))\n", - " return result" - ] - }, - { - "cell_type": "markdown", - "id": "c40a7eb4-0c3e-4eab-9291-5611cb68ce08", - "metadata": { - "tags": [] - }, - "source": [ - "# 2. Create Data Pipeline" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7686355d-45aa-4c85-ad9c-053e6a9b4d81", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "from matplotlib import pyplot as plt" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f066fea2-91b1-42ed-a67d-00566a1a53ff", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "data = tf.data.Dataset.list_files('./data/s1/*.mpg')\n", - "data = data.shuffle(500, reshuffle_each_iteration=False)\n", - "data = data.map(mappable_function)\n", - "data = data.padded_batch(2, padded_shapes=([75,None,None,None],[40]))\n", - "data = data.prefetch(tf.data.AUTOTUNE)\n", - "# Added for split \n", - "train = data.take(450)\n", - "test = data.skip(450)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6b1365bd-7742-41d1-95d4-247021751c3a", - "metadata": {}, - "outputs": [], - "source": [ - "len(test)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5281bde8-fdc8-4da1-bd55-5a7929a9e80c", - "metadata": {}, - "outputs": [], - "source": [ - "frames, alignments = data.as_numpy_iterator().next()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "cbebe683-6afd-47fd-bba4-c83b4b13bb32", - "metadata": {}, - "outputs": [], - "source": [ - "len(frames)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5cf2d676-93a9-434c-b3c7-bdcc2577b2e7", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "sample = data.as_numpy_iterator()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "efa6cd46-7079-46c0-b45b-832f339f6cb0", - "metadata": { - "scrolled": true, - "tags": [] - }, - "outputs": [], - "source": [ - "val = sample.next(); val[0]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "acf5eb4f-a0da-4a9a-bf24-af13e9cc2fbe", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "imageio.mimsave('./animation.gif', val[0][0], fps=10)" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "id": "c33a87a2-d5e0-4ec9-b174-73ebf41bf03a", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 34, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# 0:videos, 0: 1st video out of the batch, 0: return the first frame in the video \n", - "plt.imshow(val[0][0][35])" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "id": "84593332-133c-4205-b7a6-8e235d5e2b3b", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 35, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "tf.strings.reduce_join([num_to_char(word) for word in val[1][0]])" - ] - }, - { - "cell_type": "markdown", - "id": "0f47733c-83bc-465c-b118-b198b492ad37", - "metadata": { - "tags": [] - }, - "source": [ - "# 3. Design the Deep Neural Network" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "id": "d8e9a497-191b-4842-afbd-26f5e13c43ba", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "from tensorflow.keras.models import Sequential \n", - "from tensorflow.keras.layers import Conv3D, LSTM, Dense, Dropout, Bidirectional, MaxPool3D, Activation, Reshape, SpatialDropout3D, BatchNormalization, TimeDistributed, Flatten\n", - "from tensorflow.keras.optimizers import Adam\n", - "from tensorflow.keras.callbacks import ModelCheckpoint, LearningRateScheduler" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "id": "3f753ed2-70b9-4236-8c1c-08ca065dc8bf", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(75, 46, 140, 1)" - ] - }, - "execution_count": 37, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data.as_numpy_iterator().next()[0][0].shape" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "id": "f9171056-a352-491a-9ed9-92b28ced268e", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "model = Sequential()\n", - "model.add(Conv3D(128, 3, input_shape=(75,46,140,1), padding='same'))\n", - "model.add(Activation('relu'))\n", - "model.add(MaxPool3D((1,2,2)))\n", - "\n", - "model.add(Conv3D(256, 3, padding='same'))\n", - "model.add(Activation('relu'))\n", - "model.add(MaxPool3D((1,2,2)))\n", - "\n", - "model.add(Conv3D(75, 3, padding='same'))\n", - "model.add(Activation('relu'))\n", - "model.add(MaxPool3D((1,2,2)))\n", - "\n", - "model.add(TimeDistributed(Flatten()))\n", - "\n", - "model.add(Bidirectional(LSTM(128, kernel_initializer='Orthogonal', return_sequences=True)))\n", - "model.add(Dropout(.5))\n", - "\n", - "model.add(Bidirectional(LSTM(128, kernel_initializer='Orthogonal', return_sequences=True)))\n", - "model.add(Dropout(.5))\n", - "\n", - "model.add(Dense(char_to_num.vocabulary_size()+1, kernel_initializer='he_normal', activation='softmax'))" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "id": "78851825-2bcd-42a9-b7f2-28bb5a6bf43a", - "metadata": { - "collapsed": true, - "jupyter": { - "outputs_hidden": true - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Model: \"sequential\"\n", - "_________________________________________________________________\n", - " Layer (type) Output Shape Param # \n", - "=================================================================\n", - " conv3d (Conv3D) (None, 75, 46, 140, 128) 3584 \n", - " \n", - " activation (Activation) (None, 75, 46, 140, 128) 0 \n", - " \n", - " max_pooling3d (MaxPooling3D (None, 75, 23, 70, 128) 0 \n", - " ) \n", - " \n", - " conv3d_1 (Conv3D) (None, 75, 23, 70, 256) 884992 \n", - " \n", - " activation_1 (Activation) (None, 75, 23, 70, 256) 0 \n", - " \n", - " max_pooling3d_1 (MaxPooling (None, 75, 11, 35, 256) 0 \n", - " 3D) \n", - " \n", - " conv3d_2 (Conv3D) (None, 75, 11, 35, 75) 518475 \n", - " \n", - " activation_2 (Activation) (None, 75, 11, 35, 75) 0 \n", - " \n", - " max_pooling3d_2 (MaxPooling (None, 75, 5, 17, 75) 0 \n", - " 3D) \n", - " \n", - " time_distributed (TimeDistr (None, 75, 6375) 0 \n", - " ibuted) \n", - " \n", - " bidirectional (Bidirectiona (None, 75, 256) 6660096 \n", - " l) \n", - " \n", - " dropout (Dropout) (None, 75, 256) 0 \n", - " \n", - " bidirectional_1 (Bidirectio (None, 75, 256) 394240 \n", - " nal) \n", - " \n", - " dropout_1 (Dropout) (None, 75, 256) 0 \n", - " \n", - " dense (Dense) (None, 75, 41) 10537 \n", - " \n", - "=================================================================\n", - "Total params: 8,471,924\n", - "Trainable params: 8,471,924\n", - "Non-trainable params: 0\n", - "_________________________________________________________________\n" - ] - } - ], - "source": [ - "model.summary()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f4b4798c-a65a-4c47-9e2a-3b09dc98d320", - "metadata": {}, - "outputs": [], - "source": [ - "5*17*75" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "id": "e5c2eae0-c359-41a4-97a0-75c44dccb7d1", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1/1 [==============================] - 3s 3s/step\n" - ] - } - ], - "source": [ - "yhat = model.predict(val[0])" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "id": "ffdc7319-0d69-4f7e-a6d4-ce72deb81c0b", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 41, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "tf.strings.reduce_join([num_to_char(x) for x in tf.argmax(yhat[0],axis=1)])" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "id": "6ed47531-8317-4255-9a12-b757642258e6", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 42, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "tf.strings.reduce_join([num_to_char(tf.argmax(x)) for x in yhat[0]])" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "id": "7c37b9b9-5298-4038-9c33-5031d1b457f0", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(None, 75, 46, 140, 1)" - ] - }, - "execution_count": 43, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "model.input_shape" - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "id": "98b316a4-5322-4782-8e36-4b3c1a696d85", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(None, 75, 41)" - ] - }, - "execution_count": 44, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "model.output_shape" - ] - }, - { - "cell_type": "markdown", - "id": "2ec02176-5c26-46c3-aff7-8352e6563c7d", - "metadata": { - "tags": [] - }, - "source": [ - "# 4. Setup Training Options and Train" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "id": "ab015fd0-7fb4-4d5d-9fa2-30a05dbd515a", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "def scheduler(epoch, lr):\n", - " if epoch < 30:\n", - " return lr\n", - " else:\n", - " return lr * tf.math.exp(-0.1)" - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "id": "c564d5c9-db54-4e88-b311-9aeab7fb3e69", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "def CTCLoss(y_true, y_pred):\n", - " batch_len = tf.cast(tf.shape(y_true)[0], dtype=\"int64\")\n", - " input_length = tf.cast(tf.shape(y_pred)[1], dtype=\"int64\")\n", - " label_length = tf.cast(tf.shape(y_true)[1], dtype=\"int64\")\n", - "\n", - " input_length = input_length * tf.ones(shape=(batch_len, 1), dtype=\"int64\")\n", - " label_length = label_length * tf.ones(shape=(batch_len, 1), dtype=\"int64\")\n", - "\n", - " loss = tf.keras.backend.ctc_batch_cost(y_true, y_pred, input_length, label_length)\n", - " return loss" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "id": "a26dc3fc-a19c-4378-bd8c-e2b597a1d15c", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "class ProduceExample(tf.keras.callbacks.Callback): \n", - " def __init__(self, dataset) -> None: \n", - " self.dataset = dataset.as_numpy_iterator()\n", - " \n", - " def on_epoch_end(self, epoch, logs=None) -> None:\n", - " data = self.dataset.next()\n", - " yhat = self.model.predict(data[0])\n", - " decoded = tf.keras.backend.ctc_decode(yhat, [75,75], greedy=False)[0][0].numpy()\n", - " for x in range(len(yhat)): \n", - " print('Original:', tf.strings.reduce_join(num_to_char(data[1][x])).numpy().decode('utf-8'))\n", - " print('Prediction:', tf.strings.reduce_join(num_to_char(decoded[x])).numpy().decode('utf-8'))\n", - " print('~'*100)" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "id": "04be90d8-2482-46f9-b513-d5f4f8001c7e", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "model.compile(optimizer=Adam(learning_rate=0.0001), loss=CTCLoss)" - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "id": "eab49367-3f1e-4464-ae76-dbd07549d97e", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "checkpoint_callback = ModelCheckpoint(os.path.join('models','checkpoint'), monitor='loss', save_weights_only=True) " - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "id": "e085a632-d464-46ef-8777-959cad4adb2c", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "schedule_callback = LearningRateScheduler(scheduler)" - ] - }, - { - "cell_type": "code", - "execution_count": 51, - "id": "48eca991-90ab-4592-8a79-b50e9ca015b6", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "example_callback = ProduceExample(test)" - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "id": "8ffba483-aa61-4bbe-a15f-a73e1ddf097c", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 1/100\n", - " 2/450 [..............................] - ETA: 3:03 - loss: 213.9969 " - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n", - "KeyboardInterrupt\n", - "\n" - ] - } - ], - "source": [ - "model.fit(train, validation_data=test, epochs=100, callbacks=[checkpoint_callback, schedule_callback, example_callback])" - ] - }, - { - "cell_type": "markdown", - "id": "fa8ee94b-89f7-4733-8a0c-a86f86ff590a", - "metadata": { - "tags": [] - }, - "source": [ - "# 5. Make a Prediction " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "01fa7204-ce0e-49a8-8dbd-14fe5dfead40", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "url = 'https://drive.google.com/uc?id=1vWscXs4Vt0a_1IH1-ct2TCgXAZT-N3_Y'\n", - "output = 'checkpoints.zip'\n", - "gdown.download(url, output, quiet=False)\n", - "gdown.extractall('checkpoints.zip', 'models')" - ] - }, - { - "cell_type": "code", - "execution_count": 53, - "id": "247f664d-3c87-4e96-946e-930dad0e1c2c", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 53, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "model.load_weights('models/checkpoint')" - ] - }, - { - "cell_type": "code", - "execution_count": 54, - "id": "7f8d689f-b7bb-443c-9b88-e40c1d800828", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "test_data = test.as_numpy_iterator()" - ] - }, - { - "cell_type": "code", - "execution_count": 56, - "id": "38546dc2-bee9-4837-864b-8a884df40ad7", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "sample = test_data.next()" - ] - }, - { - "cell_type": "code", - "execution_count": 57, - "id": "a43621f0-229d-4c0d-9554-9c3a3da9c61a", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1/1 [==============================] - 1s 973ms/step\n" - ] - } - ], - "source": [ - "yhat = model.predict(sample[0])" - ] - }, - { - "cell_type": "code", - "execution_count": 58, - "id": "ea462999-f87e-4a7e-a057-5be7b6d8f7d5", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ REAL TEXT\n" - ] - }, - { - "data": { - "text/plain": [ - "[,\n", - " ]" - ] - }, - "execution_count": 58, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "print('~'*100, 'REAL TEXT')\n", - "[tf.strings.reduce_join([num_to_char(word) for word in sentence]) for sentence in sample[1]]" - ] - }, - { - "cell_type": "code", - "execution_count": 59, - "id": "82bd4c10-dd6e-411e-834b-2a3b43fd12c5", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "decoded = tf.keras.backend.ctc_decode(yhat, input_length=[75,75], greedy=True)[0][0].numpy()" - ] - }, - { - "cell_type": "code", - "execution_count": 60, - "id": "5d68ac46-c90b-4eab-a709-f19aee569ff5", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ PREDICTIONS\n" - ] - }, - { - "data": { - "text/plain": [ - "[,\n", - " ]" - ] - }, - "execution_count": 60, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "print('~'*100, 'PREDICTIONS')\n", - "[tf.strings.reduce_join([num_to_char(word) for word in sentence]) for sentence in decoded]" - ] - }, - { - "cell_type": "markdown", - "id": "64622f98-e99b-4fed-a2cc-f0da82eb5431", - "metadata": {}, - "source": [ - "# Test on a Video" - ] - }, - { - "cell_type": "code", - "execution_count": 61, - "id": "a8b0c4d0-2031-4331-b91d-d87b1ae6f6e2", - "metadata": {}, - "outputs": [], - "source": [ - "sample = load_data(tf.convert_to_tensor('.\\\\data\\\\s1\\\\bras9a.mpg'))" - ] - }, - { - "cell_type": "code", - "execution_count": 62, - "id": "0cca60e4-47a9-4683-8a75-48f4684f723d", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ REAL TEXT\n" - ] - }, - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 62, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "print('~'*100, 'REAL TEXT')\n", - "[tf.strings.reduce_join([num_to_char(word) for word in sentence]) for sentence in [sample[1]]]" - ] - }, - { - "cell_type": "code", - "execution_count": 63, - "id": "8cc5037c-1e32-435c-b0cc-01e1fb3b863c", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1/1 [==============================] - 1s 720ms/step\n" - ] - } - ], - "source": [ - "yhat = model.predict(tf.expand_dims(sample[0], axis=0))" - ] - }, - { - "cell_type": "code", - "execution_count": 64, - "id": "22c4f77d-715d-409f-bc5e-3ebe48704e8f", - "metadata": {}, - "outputs": [], - "source": [ - "decoded = tf.keras.backend.ctc_decode(yhat, input_length=[75], greedy=True)[0][0].numpy()" - ] - }, - { - "cell_type": "code", - "execution_count": 65, - "id": "e4d12ecc-b634-499e-a4bc-db9f010835fb", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ PREDICTIONS\n" - ] - }, - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 65, - "metadata": {}, - "output_type": "execute_result" + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "id": "a3573a47-3689-4668-b62f-5c8451b2b4e9", + "metadata": { + "tags": [], + "id": "a3573a47-3689-4668-b62f-5c8451b2b4e9" + }, + "source": [ + "# 0. Install and Import Dependencies" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "ddfbccbe-41ae-4c23-98b1-a13868e2b499", + "metadata": { + "scrolled": true, + "tags": [], + "id": "ddfbccbe-41ae-4c23-98b1-a13868e2b499", + "outputId": "964dae10-91f7-4769-b15c-4a00ca9db312", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Package Version\n", + "-------------------------------- ---------------------\n", + "absl-py 1.4.0\n", + "aiohttp 3.9.3\n", + "aiosignal 1.3.1\n", + "alabaster 0.7.16\n", + "albumentations 1.3.1\n", + "altair 4.2.2\n", + "annotated-types 0.6.0\n", + "anyio 3.7.1\n", + "appdirs 1.4.4\n", + "argon2-cffi 23.1.0\n", + "argon2-cffi-bindings 21.2.0\n", + "array-record 0.5.0\n", + "arviz 0.15.1\n", + "astropy 5.3.4\n", + "astunparse 1.6.3\n", + "async-timeout 4.0.3\n", + "atpublic 4.0\n", + "attrs 23.2.0\n", + "audioread 3.0.1\n", + "autograd 1.6.2\n", + "Babel 2.14.0\n", + "backcall 0.2.0\n", + "beautifulsoup4 4.12.3\n", + "bidict 0.23.1\n", + "bigframes 0.26.0\n", + "bleach 6.1.0\n", + "blinker 1.4\n", + "blis 0.7.11\n", + "blosc2 2.0.0\n", + "bokeh 3.3.4\n", + "bqplot 0.12.43\n", + "branca 0.7.1\n", + "build 1.1.1\n", + "CacheControl 0.14.0\n", + "cachetools 5.3.3\n", + "catalogue 2.0.10\n", + "certifi 2024.2.2\n", + "cffi 1.16.0\n", + "chardet 5.2.0\n", + "charset-normalizer 3.3.2\n", + "chex 0.1.86\n", + "click 8.1.7\n", + "click-plugins 1.1.1\n", + "cligj 0.7.2\n", + "cloudpathlib 0.16.0\n", + "cloudpickle 2.2.1\n", + "cmake 3.27.9\n", + "cmdstanpy 1.2.1\n", + "colorcet 3.1.0\n", + "colorlover 0.3.0\n", + "colour 0.1.5\n", + "community 1.0.0b1\n", + "confection 0.1.4\n", + "cons 0.4.6\n", + "contextlib2 21.6.0\n", + "contourpy 1.2.0\n", + "cryptography 42.0.5\n", + "cufflinks 0.17.3\n", + "cupy-cuda12x 12.2.0\n", + "cvxopt 1.3.2\n", + "cvxpy 1.3.3\n", + "cycler 0.12.1\n", + "cymem 2.0.8\n", + "Cython 3.0.9\n", + "dask 2023.8.1\n", + "datascience 0.17.6\n", + "db-dtypes 1.2.0\n", + "dbus-python 1.2.18\n", + "debugpy 1.6.6\n", + "decorator 4.4.2\n", + "defusedxml 0.7.1\n", + "distributed 2023.8.1\n", + "distro 1.7.0\n", + "dlib 19.24.2\n", + "dm-tree 0.1.8\n", + "docutils 0.18.1\n", + "dopamine-rl 4.0.6\n", + "duckdb 0.9.2\n", + "earthengine-api 0.1.394\n", + "easydict 1.13\n", + "ecos 2.0.13\n", + "editdistance 0.6.2\n", + "eerepr 0.0.4\n", + "en-core-web-sm 3.7.1\n", + "entrypoints 0.4\n", + "et-xmlfile 1.1.0\n", + "etils 1.7.0\n", + "etuples 0.3.9\n", + "exceptiongroup 1.2.0\n", + "fastai 2.7.14\n", + "fastcore 1.5.29\n", + "fastdownload 0.0.7\n", + "fastjsonschema 2.19.1\n", + "fastprogress 1.0.3\n", + "fastrlock 0.8.2\n", + "filelock 3.13.1\n", + "fiona 1.9.6\n", + "firebase-admin 5.3.0\n", + "Flask 2.2.5\n", + "flatbuffers 24.3.7\n", + "flax 0.8.2\n", + "folium 0.14.0\n", + "fonttools 4.50.0\n", + "frozendict 2.4.0\n", + "frozenlist 1.4.1\n", + "fsspec 2023.6.0\n", + "future 0.18.3\n", + "gast 0.5.4\n", + "gcsfs 2023.6.0\n", + "GDAL 3.6.4\n", + "gdown 4.7.3\n", + "geemap 0.32.0\n", + "gensim 4.3.2\n", + "geocoder 1.38.1\n", + "geographiclib 2.0\n", + "geopandas 0.13.2\n", + "geopy 2.3.0\n", + "gin-config 0.5.0\n", + "glob2 0.7\n", + "google 2.0.3\n", + "google-ai-generativelanguage 0.4.0\n", + "google-api-core 2.11.1\n", + "google-api-python-client 2.84.0\n", + "google-auth 2.27.0\n", + "google-auth-httplib2 0.1.1\n", + "google-auth-oauthlib 1.2.0\n", + "google-cloud-aiplatform 1.44.0\n", + "google-cloud-bigquery 3.12.0\n", + "google-cloud-bigquery-connection 1.12.1\n", + "google-cloud-bigquery-storage 2.24.0\n", + "google-cloud-core 2.3.3\n", + "google-cloud-datastore 2.15.2\n", + "google-cloud-firestore 2.11.1\n", + "google-cloud-functions 1.13.3\n", + "google-cloud-iam 2.14.3\n", + "google-cloud-language 2.13.3\n", + "google-cloud-resource-manager 1.12.3\n", + "google-cloud-storage 2.8.0\n", + "google-cloud-translate 3.11.3\n", + "google-colab 1.0.0\n", + "google-crc32c 1.5.0\n", + "google-generativeai 0.3.2\n", + "google-pasta 0.2.0\n", + "google-resumable-media 2.7.0\n", + "googleapis-common-protos 1.63.0\n", + "googledrivedownloader 0.4\n", + "graphviz 0.20.2\n", + "greenlet 3.0.3\n", + "grpc-google-iam-v1 0.13.0\n", + "grpcio 1.62.1\n", + "grpcio-status 1.48.2\n", + "gspread 3.4.2\n", + "gspread-dataframe 3.3.1\n", + "gym 0.25.2\n", + "gym-notices 0.0.8\n", + "h5netcdf 1.3.0\n", + "h5py 3.9.0\n", + "holidays 0.45\n", + "holoviews 1.17.1\n", + "html5lib 1.1\n", + "httpimport 1.3.1\n", + "httplib2 0.22.0\n", + "huggingface-hub 0.20.3\n", + "humanize 4.7.0\n", + "hyperopt 0.2.7\n", + "ibis-framework 8.0.0\n", + "idna 3.6\n", + "imageio 2.23.0\n", + "imageio-ffmpeg 0.4.9\n", + "imagesize 1.4.1\n", + "imbalanced-learn 0.10.1\n", + "imgaug 0.4.0\n", + "importlib_metadata 7.0.2\n", + "importlib_resources 6.3.2\n", + "imutils 0.5.4\n", + "inflect 7.0.0\n", + "iniconfig 2.0.0\n", + "intel-openmp 2023.2.4\n", + "ipyevents 2.0.2\n", + "ipyfilechooser 0.6.0\n", + 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3.6.6\n", + "wordcloud 1.9.3\n", + "wrapt 1.14.1\n", + "xarray 2023.7.0\n", + "xarray-einstats 0.7.0\n", + "xgboost 2.0.3\n", + "xlrd 2.0.1\n", + "xyzservices 2023.10.1\n", + "yarl 1.9.4\n", + "yellowbrick 1.5\n", + "yfinance 0.2.37\n", + "zict 3.0.0\n", + "zipp 3.18.1\n" + ] + } + ], + "source": [ + "!pip list" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "02f907ea-f669-46c7-adcf-7f257e663448", + "metadata": { + "tags": [], + "id": "02f907ea-f669-46c7-adcf-7f257e663448", + "outputId": "e444b2f9-189a-40bd-a2f9-925f13917c35", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Requirement already satisfied: opencv-python in /usr/local/lib/python3.10/dist-packages (4.8.0.76)\n", + "Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (3.7.1)\n", + "Requirement already satisfied: imageio in /usr/local/lib/python3.10/dist-packages (2.23.0)\n", + 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werkzeug>=1.0.1->tensorboard<2.16,>=2.15->tensorflow) (2.1.5)\n", + "Requirement already satisfied: pyasn1<0.6.0,>=0.4.6 in /usr/local/lib/python3.10/dist-packages (from pyasn1-modules>=0.2.1->google-auth<3,>=1.6.3->tensorboard<2.16,>=2.15->tensorflow) (0.5.1)\n", + "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.10/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<2,>=0.5->tensorboard<2.16,>=2.15->tensorflow) (3.2.2)\n" + ] + } + ], + "source": [ + "!pip install opencv-python matplotlib imageio gdown tensorflow" + ] + }, + { + "cell_type": "code", + "source": [ + "!pip install imageio==2.23.0" + ], + "metadata": { + "id": "AIsiftVmlrwN", + "outputId": "6bb43d43-d442-480d-c8f1-480a0403c717", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "id": "AIsiftVmlrwN", + "execution_count": 9, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Requirement already satisfied: imageio==2.23.0 in /usr/local/lib/python3.10/dist-packages (2.23.0)\n", + "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from imageio==2.23.0) (1.25.2)\n", + "Requirement already satisfied: pillow>=8.3.2 in /usr/local/lib/python3.10/dist-packages (from imageio==2.23.0) (9.4.0)\n" + ] + } + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "b24af50c-20b8-409d-ad78-30a933fdd669", + "metadata": { + "tags": [], + "id": "b24af50c-20b8-409d-ad78-30a933fdd669" + }, + "outputs": [], + "source": [ + "import os\n", + "import cv2\n", + "import tensorflow as tf\n", + "import numpy as np\n", + "from typing import List\n", + "from matplotlib import pyplot as plt\n", + "import imageio" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "1e3db0b0-e559-4ad6-91fd-e7414b7d75e6", + "metadata": { + "id": "1e3db0b0-e559-4ad6-91fd-e7414b7d75e6", + "outputId": "c1e41844-c8fd-4efc-eb22-11f3817b0c9f", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "[PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]" + ] + }, + "metadata": {}, + "execution_count": 11 + } + ], + "source": [ + "tf.config.list_physical_devices('GPU')" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "378d045a-3003-4f93-b7d2-a25a97774a68", + "metadata": { + "tags": [], + "id": "378d045a-3003-4f93-b7d2-a25a97774a68" + }, + "outputs": [], + "source": [ + "physical_devices = tf.config.list_physical_devices('GPU')\n", + "try:\n", + " tf.config.experimental.set_memory_growth(physical_devices[0], True)\n", + "except:\n", + " pass" + ] + }, + { + "cell_type": "markdown", + "id": "7a19e88e-c7b9-45c1-ae1e-f2109329c71b", + "metadata": { + "tags": [], + "id": "7a19e88e-c7b9-45c1-ae1e-f2109329c71b" + }, + "source": [ + "# 1. Build Data Loading Functions" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "8fb99c90-e05a-437f-839d-6e772f8c1dd5", + "metadata": { + "tags": [], + "id": "8fb99c90-e05a-437f-839d-6e772f8c1dd5" + }, + "outputs": [], + "source": [ + "import gdown" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "c019e4c6-2af3-4160-99ea-5c8cb009f1a7", + "metadata": { + "tags": [], + "id": "c019e4c6-2af3-4160-99ea-5c8cb009f1a7", + "outputId": "49a7c7ff-ab7d-480e-bcfd-ee652ad89af1", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "Downloading...\n", + "From (original): https://drive.google.com/uc?id=1YlvpDLix3S-U8fd-gqRwPcWXAXm8JwjL\n", + "From (redirected): https://drive.google.com/uc?id=1YlvpDLix3S-U8fd-gqRwPcWXAXm8JwjL&confirm=t&uuid=9de512c9-8ed3-42ac-ba38-5de21c84c9e9\n", + "To: /content/data.zip\n", + "100%|██████████| 423M/423M [00:03<00:00, 128MB/s]\n" + ] + }, + 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'data/alignments/s1/swbp1a.align',\n", + " 'data/alignments/s1/swbpzp.align',\n", + " 'data/alignments/s1/swbv2n.align',\n", + " 'data/alignments/s1/swbv3s.align',\n", + " 'data/alignments/s1/swbv4p.align',\n", + " 'data/alignments/s1/swbv5a.align',\n", + " 'data/alignments/s1/swib2n.align',\n", + " 'data/alignments/s1/swib3s.align',\n", + " 'data/alignments/s1/swib4p.align',\n", + " 'data/alignments/s1/swib5a.align',\n", + " 'data/alignments/s1/swih6n.align',\n", + " 'data/alignments/s1/swih7s.align',\n", + " 'data/alignments/s1/swih8p.align',\n", + " 'data/alignments/s1/swih9a.align',\n", + " 'data/alignments/s1/swio1s.align',\n", + " 'data/alignments/s1/swio2p.align',\n", + " 'data/alignments/s1/swio3a.align',\n", + " 'data/alignments/s1/swiozn.align',\n", + " 'data/alignments/s1/swiu4n.align',\n", + " 'data/alignments/s1/swiu5s.align',\n", + " 'data/alignments/s1/swiu6p.align',\n", + " 'data/alignments/s1/swiu7a.align',\n", + " 'data/alignments/s1/swwc4n.align',\n", + " 'data/alignments/s1/swwc5s.align',\n", + " 'data/alignments/s1/swwc6p.align',\n", + " 'data/alignments/s1/swwc7a.align',\n", + " 'data/alignments/s1/swwi8n.align',\n", + " 'data/alignments/s1/swwi9s.align',\n", + " 'data/alignments/s1/swwj1a.align',\n", + " 'data/alignments/s1/swwjzp.align',\n", + " 'data/alignments/s1/swwp2n.align',\n", + " 'data/alignments/s1/swwp3s.align',\n", + " 'data/alignments/s1/swwp4p.align',\n", + " 'data/alignments/s1/swwp5a.align',\n", + " 'data/alignments/s1/swwv6n.align',\n", + " ...]" + ] + }, + "metadata": {}, + "execution_count": 14 + } + ], + "source": [ + "url = 'https://drive.google.com/uc?id=1YlvpDLix3S-U8fd-gqRwPcWXAXm8JwjL'\n", + "output = 'data.zip'\n", + "gdown.download(url, output, quiet=False)\n", + "gdown.extractall('data.zip')" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "8548cc59-6dfc-4acc-abc3-3e65212db02e", + "metadata": { + "tags": [], + "id": "8548cc59-6dfc-4acc-abc3-3e65212db02e" + }, + "outputs": [], + "source": [ + "def load_video(path:str) -> List[float]:\n", + "\n", + " cap = cv2.VideoCapture(path)\n", + " frames = []\n", + " for _ in range(int(cap.get(cv2.CAP_PROP_FRAME_COUNT))):\n", + " ret, frame = cap.read()\n", + " frame = tf.image.rgb_to_grayscale(frame)\n", + " frames.append(frame[190:236,80:220,:])\n", + " cap.release()\n", + "\n", + " mean = tf.math.reduce_mean(frames)\n", + " std = tf.math.reduce_std(tf.cast(frames, tf.float32))\n", + " return tf.cast((frames - mean), tf.float32) / std" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "ec735e0b-ec98-4eb0-8f49-c35527d6670a", + "metadata": { + "tags": [], + "id": "ec735e0b-ec98-4eb0-8f49-c35527d6670a" + }, + "outputs": [], + "source": [ + "vocab = [x for x in \"abcdefghijklmnopqrstuvwxyz'?!123456789 \"]" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "be04e972-d7a5-4a72-82d8-a6bdde1f3ce6", + "metadata": { + "tags": [], + "id": "be04e972-d7a5-4a72-82d8-a6bdde1f3ce6", + "outputId": "01f490ee-dabf-4e94-c6ed-181e8c1ae2c4", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "The vocabulary is: ['', 'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z', \"'\", '?', '!', '1', '2', '3', '4', '5', '6', '7', '8', '9', ' '] (size =40)\n" + ] + } + ], + "source": [ + "char_to_num = tf.keras.layers.StringLookup(vocabulary=vocab, oov_token=\"\")\n", + "num_to_char = tf.keras.layers.StringLookup(\n", + " vocabulary=char_to_num.get_vocabulary(), oov_token=\"\", invert=True\n", + ")\n", + "\n", + "print(\n", + " f\"The vocabulary is: {char_to_num.get_vocabulary()} \"\n", + " f\"(size ={char_to_num.vocabulary_size()})\"\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "559f7420-6802-45fa-9ca0-b1ff209b461c", + "metadata": { + "tags": [], + "id": "559f7420-6802-45fa-9ca0-b1ff209b461c", + "outputId": "90f0547b-3308-478e-be4a-c89cef1d58e6", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "['',\n", + " 'a',\n", + " 'b',\n", + " 'c',\n", + " 'd',\n", + " 'e',\n", + " 'f',\n", + " 'g',\n", + " 'h',\n", + " 'i',\n", + " 'j',\n", + " 'k',\n", + " 'l',\n", + " 'm',\n", + " 'n',\n", + " 'o',\n", + " 'p',\n", + " 'q',\n", + " 'r',\n", + " 's',\n", + " 't',\n", + " 'u',\n", + " 'v',\n", + " 'w',\n", + " 'x',\n", + " 'y',\n", + " 'z',\n", + " \"'\",\n", + " '?',\n", + " '!',\n", + " '1',\n", + " '2',\n", + " '3',\n", + " '4',\n", + " '5',\n", + " '6',\n", + " '7',\n", + " '8',\n", + " '9',\n", + " ' ']" + ] + }, + "metadata": {}, + "execution_count": 18 + } + ], + "source": [ + "char_to_num.get_vocabulary()" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "797ff78b-b48f-4e14-bb62-8cd0ebf9501a", + "metadata": { + "tags": [], + "id": "797ff78b-b48f-4e14-bb62-8cd0ebf9501a", + "outputId": "cedf213c-95e4-439a-a871-641aec307517", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "execution_count": 19 + } + ], + "source": [ + "char_to_num(['n','i','c','k'])" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "8cd7f4f4-ae77-4509-a4f4-c723787ebad1", + "metadata": { + "id": "8cd7f4f4-ae77-4509-a4f4-c723787ebad1", + "outputId": "cb5428be-73ea-47b2-ea8e-4d2c4e3dcb00", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "execution_count": 20 + } + ], + "source": [ + "num_to_char([14, 9, 3, 11])" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "9491bab5-6a3c-4f79-879a-8f9fbe73ae2e", + "metadata": { + "tags": [], + "id": "9491bab5-6a3c-4f79-879a-8f9fbe73ae2e" + }, + "outputs": [], + "source": [ + "def load_alignments(path:str) -> List[str]:\n", + " with open(path, 'r') as f:\n", + " lines = f.readlines()\n", + " tokens = []\n", + " for line in lines:\n", + " line = line.split()\n", + " if line[2] != 'sil':\n", + " tokens = [*tokens,' ',line[2]]\n", + " return char_to_num(tf.reshape(tf.strings.unicode_split(tokens, input_encoding='UTF-8'), (-1)))[1:]" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "dd01ca9f-77fb-4643-a2aa-47dd82c5d66b", + "metadata": { + "tags": [], + "id": "dd01ca9f-77fb-4643-a2aa-47dd82c5d66b" + }, + "outputs": [], + "source": [ + "def load_data(path: str):\n", + " path = bytes.decode(path.numpy())\n", + " file_name = path.split('/')[-1].split('.')[0]\n", + " # File name splitting for windows\n", + " # file_name = path.split('\\\\')[-1].split('.')[0]\n", + " video_path = os.path.join('data','s1',f'{file_name}.mpg')\n", + " alignment_path = os.path.join('data','alignments','s1',f'{file_name}.align')\n", + " frames = load_video(video_path)\n", + " alignments = load_alignments(alignment_path)\n", + "\n", + " return frames, alignments" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "8cb7cc58-31ae-4904-a805-1177a82717d2", + "metadata": { + "tags": [], + "id": "8cb7cc58-31ae-4904-a805-1177a82717d2" + }, + "outputs": [], + "source": [ + "# test_path = '.\\\\data\\\\s1\\\\bbal6n.mpg'\n", + "test_path = './data/s1/bbal6n.mpg'" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "76aa964f-0c84-490d-897a-d00e3966e2c9", + "metadata": { + "id": "76aa964f-0c84-490d-897a-d00e3966e2c9", + "outputId": "d04d49ad-4eeb-4be1-ffa7-f2655aa86a7a", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 35 + } + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "'bbal6n'" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "string" + } + }, + "metadata": {}, + "execution_count": 24 + } + ], + "source": [ + "# tf.convert_to_tensor(test_path).numpy().decode('utf-8').split('\\\\')[-1].split('.')[0]\n", + "tf.convert_to_tensor(test_path).numpy().decode('utf-8').split('/')[-1].split('.')[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "eb602c71-8560-4f9e-b26b-08202febb937", + "metadata": { + "scrolled": true, + "tags": [], + "id": "eb602c71-8560-4f9e-b26b-08202febb937" + }, + "outputs": [], + "source": [ + "frames, alignments = load_data(tf.convert_to_tensor(test_path))" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "0e3184a1-6b02-4b4f-84a8-a0a65f951ea2", + "metadata": { + "id": "0e3184a1-6b02-4b4f-84a8-a0a65f951ea2", + "outputId": "ca65c601-b300-4a41-d8fe-7915a8e5a50b", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 244 + } + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "execution_count": 26 + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ], + "source": [ + "plt.imshow(frames[40])" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "d7ec0833-d54b-4073-84cf-92d011c60ec1", + "metadata": { + "id": "d7ec0833-d54b-4073-84cf-92d011c60ec1", + "outputId": "02abd7a1-113b-42b1-a6ba-9a458373b112", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "execution_count": 27 + } + ], + "source": [ + "alignments" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "fe1ad370-b287-4b46-85a2-7c45b0bd9b10", + "metadata": { + "id": "fe1ad370-b287-4b46-85a2-7c45b0bd9b10", + "outputId": "8910d788-545b-4e76-b154-4a90d8e63927", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "execution_count": 28 + } + ], + "source": [ + "tf.strings.reduce_join([bytes.decode(x) for x in num_to_char(alignments.numpy()).numpy()])" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "6871031a-b0ba-4c76-a852-f6329b0f2606", + "metadata": { + "tags": [], + "id": "6871031a-b0ba-4c76-a852-f6329b0f2606" + }, + "outputs": [], + "source": [ + "def mappable_function(path:str) ->List[str]:\n", + " result = tf.py_function(load_data, [path], (tf.float32, tf.int64))\n", + " return result" + ] + }, + { + "cell_type": "markdown", + "id": "c40a7eb4-0c3e-4eab-9291-5611cb68ce08", + "metadata": { + "tags": [], + "id": "c40a7eb4-0c3e-4eab-9291-5611cb68ce08" + }, + "source": [ + "# 2. Create Data Pipeline" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "7686355d-45aa-4c85-ad9c-053e6a9b4d81", + "metadata": { + "tags": [], + "id": "7686355d-45aa-4c85-ad9c-053e6a9b4d81" + }, + "outputs": [], + "source": [ + "from matplotlib import pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "f066fea2-91b1-42ed-a67d-00566a1a53ff", + "metadata": { + "tags": [], + "id": "f066fea2-91b1-42ed-a67d-00566a1a53ff" + }, + "outputs": [], + "source": [ + "data = tf.data.Dataset.list_files('./data/s1/*.mpg')\n", + "data = data.shuffle(500, reshuffle_each_iteration=False)\n", + "data = data.map(mappable_function)\n", + "data = data.padded_batch(2, padded_shapes=([75,None,None,None],[40]))\n", + "data = data.prefetch(tf.data.AUTOTUNE)\n", + "# Added for split\n", + "train = data.take(450)\n", + "test = data.skip(450)" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "6b1365bd-7742-41d1-95d4-247021751c3a", + "metadata": { + "id": "6b1365bd-7742-41d1-95d4-247021751c3a", + "outputId": "facfc0fa-bf61-4aff-b8ed-f1a42278a4b3", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "50" + ] + }, + "metadata": {}, + "execution_count": 32 + } + ], + "source": [ + "len(test)" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "5281bde8-fdc8-4da1-bd55-5a7929a9e80c", + "metadata": { + "id": "5281bde8-fdc8-4da1-bd55-5a7929a9e80c" + }, + "outputs": [], + "source": [ + "frames, alignments = data.as_numpy_iterator().next()" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "cbebe683-6afd-47fd-bba4-c83b4b13bb32", + "metadata": { + "id": "cbebe683-6afd-47fd-bba4-c83b4b13bb32", + "outputId": "8924019e-28cd-4bfa-9e1e-bb836d71e6f8", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "2" + ] + }, + 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Range [0.0, 9.52289867401123]. Convert image to uint8 prior to saving to suppress this warning.\n", + "WARNING:imageio:Lossy conversion from float32 to uint8. Range [0.0, 9.52289867401123]. Convert image to uint8 prior to saving to suppress this warning.\n", + "WARNING:imageio:Lossy conversion from float32 to uint8. Range [0.0, 9.52289867401123]. Convert image to uint8 prior to saving to suppress this warning.\n", + "WARNING:imageio:Lossy conversion from float32 to uint8. Range [0.0, 9.52289867401123]. Convert image to uint8 prior to saving to suppress this warning.\n", + "WARNING:imageio:Lossy conversion from float32 to uint8. Range [0.0, 9.52289867401123]. Convert image to uint8 prior to saving to suppress this warning.\n", + "WARNING:imageio:Lossy conversion from float32 to uint8. Range [0.0, 9.52289867401123]. Convert image to uint8 prior to saving to suppress this warning.\n", + "WARNING:imageio:Lossy conversion from float32 to uint8. Range [0.0, 9.52289867401123]. Convert image to uint8 prior to saving to suppress this warning.\n", + "WARNING:imageio:Lossy conversion from float32 to uint8. Range [0.0, 9.52289867401123]. Convert image to uint8 prior to saving to suppress this warning.\n", + "WARNING:imageio:Lossy conversion from float32 to uint8. Range [0.0, 9.52289867401123]. Convert image to uint8 prior to saving to suppress this warning.\n", + "WARNING:imageio:Lossy conversion from float32 to uint8. Range [0.0, 9.52289867401123]. Convert image to uint8 prior to saving to suppress this warning.\n", + "WARNING:imageio:Lossy conversion from float32 to uint8. Range [0.0, 9.52289867401123]. Convert image to uint8 prior to saving to suppress this warning.\n", + "WARNING:imageio:Lossy conversion from float32 to uint8. Range [0.0, 9.52289867401123]. Convert image to uint8 prior to saving to suppress this warning.\n", + "WARNING:imageio:Lossy conversion from float32 to uint8. Range [0.0, 9.52289867401123]. 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Convert image to uint8 prior to saving to suppress this warning.\n", + "WARNING:imageio:Lossy conversion from float32 to uint8. Range [0.0, 9.52289867401123]. Convert image to uint8 prior to saving to suppress this warning.\n", + "WARNING:imageio:Lossy conversion from float32 to uint8. Range [0.0, 9.52289867401123]. Convert image to uint8 prior to saving to suppress this warning.\n", + "WARNING:imageio:Lossy conversion from float32 to uint8. Range [0.0, 9.52289867401123]. Convert image to uint8 prior to saving to suppress this warning.\n", + "WARNING:imageio:Lossy conversion from float32 to uint8. Range [0.0, 9.52289867401123]. Convert image to uint8 prior to saving to suppress this warning.\n", + "WARNING:imageio:Lossy conversion from float32 to uint8. Range [0.0, 9.52289867401123]. Convert image to uint8 prior to saving to suppress this warning.\n", + "WARNING:imageio:Lossy conversion from float32 to uint8. Range [0.0, 9.52289867401123]. Convert image to uint8 prior to saving to suppress this warning.\n", + "WARNING:imageio:Lossy conversion from float32 to uint8. Range [0.0, 9.52289867401123]. Convert image to uint8 prior to saving to suppress this warning.\n", + "WARNING:imageio:Lossy conversion from float32 to uint8. Range [0.0, 9.52289867401123]. Convert image to uint8 prior to saving to suppress this warning.\n", + "WARNING:imageio:Lossy conversion from float32 to uint8. Range [0.0, 9.52289867401123]. Convert image to uint8 prior to saving to suppress this warning.\n", + "WARNING:imageio:Lossy conversion from float32 to uint8. Range [0.0, 9.52289867401123]. Convert image to uint8 prior to saving to suppress this warning.\n", + "WARNING:imageio:Lossy conversion from float32 to uint8. Range [0.0, 9.52289867401123]. Convert image to uint8 prior to saving to suppress this warning.\n", + "WARNING:imageio:Lossy conversion from float32 to uint8. Range [0.0, 9.52289867401123]. Convert image to uint8 prior to saving to suppress this warning.\n", + "WARNING:imageio:Lossy conversion from float32 to uint8. Range [0.0, 9.52289867401123]. Convert image to uint8 prior to saving to suppress this warning.\n", + "WARNING:imageio:Lossy conversion from float32 to uint8. Range [0.0, 9.52289867401123]. Convert image to uint8 prior to saving to suppress this warning.\n", + "WARNING:imageio:Lossy conversion from float32 to uint8. Range [0.0, 9.52289867401123]. Convert image to uint8 prior to saving to suppress this warning.\n", + "WARNING:imageio:Lossy conversion from float32 to uint8. Range [0.0, 9.52289867401123]. Convert image to uint8 prior to saving to suppress this warning.\n", + "WARNING:imageio:Lossy conversion from float32 to uint8. Range [0.0, 9.52289867401123]. Convert image to uint8 prior to saving to suppress this warning.\n", + "WARNING:imageio:Lossy conversion from float32 to uint8. Range [0.0, 9.52289867401123]. Convert image to uint8 prior to saving to suppress this warning.\n", + "WARNING:imageio:Lossy conversion from float32 to uint8. Range [0.0, 9.52289867401123]. Convert image to uint8 prior to saving to suppress this warning.\n", + "WARNING:imageio:Lossy conversion from float32 to uint8. Range [0.0, 9.52289867401123]. Convert image to uint8 prior to saving to suppress this warning.\n", + "WARNING:imageio:Lossy conversion from float32 to uint8. Range [0.0, 9.52289867401123]. Convert image to uint8 prior to saving to suppress this warning.\n", + "WARNING:imageio:Lossy conversion from float32 to uint8. Range [0.0, 9.52289867401123]. Convert image to uint8 prior to saving to suppress this warning.\n", + "WARNING:imageio:Lossy conversion from float32 to uint8. Range [0.0, 9.52289867401123]. Convert image to uint8 prior to saving to suppress this warning.\n", + "WARNING:imageio:Lossy conversion from float32 to uint8. Range [0.0, 9.52289867401123]. Convert image to uint8 prior to saving to suppress this warning.\n", + "WARNING:imageio:Lossy conversion from float32 to uint8. Range [0.0, 9.52289867401123]. Convert image to uint8 prior to saving to suppress this warning.\n", + "WARNING:imageio:Lossy conversion from float32 to uint8. Range [0.0, 9.52289867401123]. Convert image to uint8 prior to saving to suppress this warning.\n", + "WARNING:imageio:Lossy conversion from float32 to uint8. Range [0.0, 9.52289867401123]. Convert image to uint8 prior to saving to suppress this warning.\n", + "WARNING:imageio:Lossy conversion from float32 to uint8. Range [0.0, 9.52289867401123]. Convert image to uint8 prior to saving to suppress this warning.\n", + "WARNING:imageio:Lossy conversion from float32 to uint8. Range [0.0, 9.52289867401123]. Convert image to uint8 prior to saving to suppress this warning.\n", + "WARNING:imageio:Lossy conversion from float32 to uint8. Range [0.0, 9.52289867401123]. Convert image to uint8 prior to saving to suppress this warning.\n", + "WARNING:imageio:Lossy conversion from float32 to uint8. Range [0.0, 9.52289867401123]. Convert image to uint8 prior to saving to suppress this warning.\n", + "WARNING:imageio:Lossy conversion from float32 to uint8. Range [0.0, 9.52289867401123]. Convert image to uint8 prior to saving to suppress this warning.\n", + "WARNING:imageio:Lossy conversion from float32 to uint8. Range [0.0, 9.52289867401123]. Convert image to uint8 prior to saving to suppress this warning.\n", + "WARNING:imageio:Lossy conversion from float32 to uint8. Range [0.0, 9.52289867401123]. Convert image to uint8 prior to saving to suppress this warning.\n", + "WARNING:imageio:Lossy conversion from float32 to uint8. Range [0.0, 9.52289867401123]. Convert image to uint8 prior to saving to suppress this warning.\n", + "WARNING:imageio:Lossy conversion from float32 to uint8. Range [0.0, 9.52289867401123]. Convert image to uint8 prior to saving to suppress this warning.\n", + "WARNING:imageio:Lossy conversion from float32 to uint8. Range [0.0, 9.52289867401123]. Convert image to uint8 prior to saving to suppress this warning.\n", + "WARNING:imageio:Lossy conversion from float32 to uint8. Range [0.0, 9.52289867401123]. Convert image to uint8 prior to saving to suppress this warning.\n", + "WARNING:imageio:Lossy conversion from float32 to uint8. Range [0.0, 9.52289867401123]. Convert image to uint8 prior to saving to suppress this warning.\n", + "WARNING:imageio:Lossy conversion from float32 to uint8. Range [0.0, 9.52289867401123]. Convert image to uint8 prior to saving to suppress this warning.\n", + "WARNING:imageio:Lossy conversion from float32 to uint8. Range [0.0, 9.52289867401123]. Convert image to uint8 prior to saving to suppress this warning.\n", + "WARNING:imageio:Lossy conversion from float32 to uint8. Range [0.0, 9.52289867401123]. Convert image to uint8 prior to saving to suppress this warning.\n", + "WARNING:imageio:Lossy conversion from float32 to uint8. Range [0.0, 9.52289867401123]. Convert image to uint8 prior to saving to suppress this warning.\n", + "WARNING:imageio:Lossy conversion from float32 to uint8. Range [0.0, 9.52289867401123]. Convert image to uint8 prior to saving to suppress this warning.\n", + "WARNING:imageio:Lossy conversion from float32 to uint8. Range [0.0, 9.52289867401123]. Convert image to uint8 prior to saving to suppress this warning.\n", + "WARNING:imageio:Lossy conversion from float32 to uint8. Range [0.0, 9.52289867401123]. Convert image to uint8 prior to saving to suppress this warning.\n", + "WARNING:imageio:Lossy conversion from float32 to uint8. Range [0.0, 9.52289867401123]. Convert image to uint8 prior to saving to suppress this warning.\n", + "WARNING:imageio:Lossy conversion from float32 to uint8. Range [0.0, 9.52289867401123]. Convert image to uint8 prior to saving to suppress this warning.\n", + "WARNING:imageio:Lossy conversion from float32 to uint8. Range [0.0, 9.52289867401123]. Convert image to uint8 prior to saving to suppress this warning.\n", + "WARNING:imageio:Lossy conversion from float32 to uint8. Range [0.0, 9.52289867401123]. Convert image to uint8 prior to saving to suppress this warning.\n", + "WARNING:imageio:Lossy conversion from float32 to uint8. Range [0.0, 9.52289867401123]. Convert image to uint8 prior to saving to suppress this warning.\n", + "WARNING:imageio:Lossy conversion from float32 to uint8. Range [0.0, 9.52289867401123]. Convert image to uint8 prior to saving to suppress this warning.\n", + "WARNING:imageio:Lossy conversion from float32 to uint8. Range [0.0, 9.52289867401123]. Convert image to uint8 prior to saving to suppress this warning.\n", + "WARNING:imageio:Lossy conversion from float32 to uint8. Range [0.0, 9.52289867401123]. Convert image to uint8 prior to saving to suppress this warning.\n", + "WARNING:imageio:Lossy conversion from float32 to uint8. Range [0.0, 9.52289867401123]. Convert image to uint8 prior to saving to suppress this warning.\n", + "WARNING:imageio:Lossy conversion from float32 to uint8. Range [0.0, 9.52289867401123]. Convert image to uint8 prior to saving to suppress this warning.\n" + ] + } + ], + "source": [ + "imageio.mimsave('./animation.gif', val[0][0], fps=10)" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "c33a87a2-d5e0-4ec9-b174-73ebf41bf03a", + "metadata": { + "tags": [], + "id": "c33a87a2-d5e0-4ec9-b174-73ebf41bf03a", + "outputId": "1b7cc028-1990-4714-8d7e-fda85218c0d5", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 244 + } + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "execution_count": 38 + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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\n" + }, + "metadata": {} + } + ], + "source": [ + "# 0:videos, 0: 1st video out of the batch, 0: return the first frame in the video\n", + "plt.imshow(val[0][0][35])" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "84593332-133c-4205-b7a6-8e235d5e2b3b", + "metadata": { + "tags": [], + "id": "84593332-133c-4205-b7a6-8e235d5e2b3b", + "outputId": "69f24afc-2cb9-4d07-9d93-3b4e1fde5046", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "execution_count": 39 + } + ], + "source": [ + "tf.strings.reduce_join([num_to_char(word) for word in val[1][0]])" + ] + }, + { + "cell_type": "markdown", + "id": "0f47733c-83bc-465c-b118-b198b492ad37", + "metadata": { + "tags": [], + "id": "0f47733c-83bc-465c-b118-b198b492ad37" + }, + "source": [ + "# 3. Design the Deep Neural Network" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "d8e9a497-191b-4842-afbd-26f5e13c43ba", + "metadata": { + "tags": [], + "id": "d8e9a497-191b-4842-afbd-26f5e13c43ba" + }, + "outputs": [], + "source": [ + "from tensorflow.keras.models import Sequential\n", + "from tensorflow.keras.layers import Conv3D, LSTM, Dense, Dropout, Bidirectional, MaxPool3D, Activation, Reshape, SpatialDropout3D, BatchNormalization, TimeDistributed, Flatten\n", + "from tensorflow.keras.optimizers import Adam\n", + "from tensorflow.keras.callbacks import ModelCheckpoint, LearningRateScheduler" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "3f753ed2-70b9-4236-8c1c-08ca065dc8bf", + "metadata": { + "id": "3f753ed2-70b9-4236-8c1c-08ca065dc8bf", + "outputId": "b06442c3-cc68-4c88-d5e8-c9a915fc8b96", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "(75, 46, 140, 1)" + ] + }, + "metadata": {}, + "execution_count": 41 + } + ], + "source": [ + "data.as_numpy_iterator().next()[0][0].shape" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "f9171056-a352-491a-9ed9-92b28ced268e", + "metadata": { + "tags": [], + "id": "f9171056-a352-491a-9ed9-92b28ced268e" + }, + "outputs": [], + "source": [ + "model = Sequential()\n", + "model.add(Conv3D(128, 3, input_shape=(75,46,140,1), padding='same'))\n", + "model.add(Activation('relu'))\n", + "model.add(MaxPool3D((1,2,2)))\n", + "\n", + "model.add(Conv3D(256, 3, padding='same'))\n", + "model.add(Activation('relu'))\n", + "model.add(MaxPool3D((1,2,2)))\n", + "\n", + "model.add(Conv3D(75, 3, padding='same'))\n", + "model.add(Activation('relu'))\n", + "model.add(MaxPool3D((1,2,2)))\n", + "\n", + "model.add(TimeDistributed(Flatten()))\n", + "\n", + "model.add(Bidirectional(LSTM(128, kernel_initializer='Orthogonal', return_sequences=True)))\n", + "model.add(Dropout(.5))\n", + "\n", + "model.add(Bidirectional(LSTM(128, kernel_initializer='Orthogonal', return_sequences=True)))\n", + "model.add(Dropout(.5))\n", + "\n", + "model.add(Dense(char_to_num.vocabulary_size()+1, kernel_initializer='he_normal', activation='softmax'))" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "id": "78851825-2bcd-42a9-b7f2-28bb5a6bf43a", + "metadata": { + "collapsed": true, + "jupyter": { + "outputs_hidden": true + }, + "tags": [], + "id": "78851825-2bcd-42a9-b7f2-28bb5a6bf43a", + "outputId": "90ebbd81-10a8-4674-8551-7b3cd530381a", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Model: \"sequential\"\n", + "_________________________________________________________________\n", + " Layer (type) Output Shape Param # \n", + "=================================================================\n", + " conv3d (Conv3D) (None, 75, 46, 140, 128 3584 \n", + " ) \n", + " \n", + " activation (Activation) (None, 75, 46, 140, 128 0 \n", + " ) \n", + " \n", + " max_pooling3d (MaxPooling3 (None, 75, 23, 70, 128) 0 \n", + " D) \n", + " \n", + " conv3d_1 (Conv3D) (None, 75, 23, 70, 256) 884992 \n", + " \n", + " activation_1 (Activation) (None, 75, 23, 70, 256) 0 \n", + " \n", + " max_pooling3d_1 (MaxPoolin (None, 75, 11, 35, 256) 0 \n", + " g3D) \n", + " \n", + " conv3d_2 (Conv3D) (None, 75, 11, 35, 75) 518475 \n", + " \n", + " activation_2 (Activation) (None, 75, 11, 35, 75) 0 \n", + " \n", + " max_pooling3d_2 (MaxPoolin (None, 75, 5, 17, 75) 0 \n", + " g3D) \n", + " \n", + " time_distributed (TimeDist (None, 75, 6375) 0 \n", + " ributed) \n", + " \n", + " bidirectional (Bidirection (None, 75, 256) 6660096 \n", + " al) \n", + " \n", + " dropout (Dropout) (None, 75, 256) 0 \n", + " \n", + " bidirectional_1 (Bidirecti (None, 75, 256) 394240 \n", + " onal) \n", + " \n", + " dropout_1 (Dropout) (None, 75, 256) 0 \n", + " \n", + " dense (Dense) (None, 75, 41) 10537 \n", + " \n", + "=================================================================\n", + "Total params: 8471924 (32.32 MB)\n", + "Trainable params: 8471924 (32.32 MB)\n", + "Non-trainable params: 0 (0.00 Byte)\n", + "_________________________________________________________________\n" + ] + } + ], + "source": [ + "model.summary()" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "id": "f4b4798c-a65a-4c47-9e2a-3b09dc98d320", + "metadata": { + "id": "f4b4798c-a65a-4c47-9e2a-3b09dc98d320", + "outputId": "2a2066ab-f352-4b02-c64f-d0e6d139d7cf", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "6375" + ] + }, + "metadata": {}, + "execution_count": 44 + } + ], + "source": [ + "5*17*75" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "id": "e5c2eae0-c359-41a4-97a0-75c44dccb7d1", + "metadata": { + "tags": [], + "id": "e5c2eae0-c359-41a4-97a0-75c44dccb7d1", + "outputId": "02f5688b-3290-4eef-b0f9-bd61d8b5031b", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "1/1 [==============================] - 5s 5s/step\n" + ] + } + ], + "source": [ + "yhat = model.predict(val[0])" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "id": "ffdc7319-0d69-4f7e-a6d4-ce72deb81c0b", + "metadata": { + "id": "ffdc7319-0d69-4f7e-a6d4-ce72deb81c0b", + "outputId": "bf6148fc-b7fa-4914-860a-bed361d0be87", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "execution_count": 46 + } + ], + "source": [ + "tf.strings.reduce_join([num_to_char(x) for x in tf.argmax(yhat[0],axis=1)])" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "id": "6ed47531-8317-4255-9a12-b757642258e6", + "metadata": { + "tags": [], + "id": "6ed47531-8317-4255-9a12-b757642258e6", + "outputId": "5b755432-4136-4834-fd69-78aace01b57b", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "execution_count": 47 + } + ], + "source": [ + "tf.strings.reduce_join([num_to_char(tf.argmax(x)) for x in yhat[0]])" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "id": "7c37b9b9-5298-4038-9c33-5031d1b457f0", + "metadata": { + "tags": [], + "id": "7c37b9b9-5298-4038-9c33-5031d1b457f0", + "outputId": "fcdf9c94-35db-4aa0-fcf7-3029f32eac97", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "(None, 75, 46, 140, 1)" + ] + }, + "metadata": {}, + "execution_count": 48 + } + ], + "source": [ + "model.input_shape" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "id": "98b316a4-5322-4782-8e36-4b3c1a696d85", + "metadata": { + "tags": [], + "id": "98b316a4-5322-4782-8e36-4b3c1a696d85", + "outputId": "5f9ebad7-b4cc-4ac8-c5e6-f5471120f50f", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "(None, 75, 41)" + ] + }, + "metadata": {}, + "execution_count": 49 + } + ], + "source": [ + "model.output_shape" + ] + }, + { + "cell_type": "markdown", + "id": "2ec02176-5c26-46c3-aff7-8352e6563c7d", + "metadata": { + "tags": [], + "id": "2ec02176-5c26-46c3-aff7-8352e6563c7d" + }, + "source": [ + "# 4. Setup Training Options and Train" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "id": "ab015fd0-7fb4-4d5d-9fa2-30a05dbd515a", + "metadata": { + "tags": [], + "id": "ab015fd0-7fb4-4d5d-9fa2-30a05dbd515a" + }, + "outputs": [], + "source": [ + "def scheduler(epoch, lr):\n", + " if epoch < 30:\n", + " return lr\n", + " else:\n", + " return lr * tf.math.exp(-0.1)" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "id": "c564d5c9-db54-4e88-b311-9aeab7fb3e69", + "metadata": { + "tags": [], + "id": "c564d5c9-db54-4e88-b311-9aeab7fb3e69" + }, + "outputs": [], + "source": [ + "def CTCLoss(y_true, y_pred):\n", + " batch_len = tf.cast(tf.shape(y_true)[0], dtype=\"int64\")\n", + " input_length = tf.cast(tf.shape(y_pred)[1], dtype=\"int64\")\n", + " label_length = tf.cast(tf.shape(y_true)[1], dtype=\"int64\")\n", + "\n", + " input_length = input_length * tf.ones(shape=(batch_len, 1), dtype=\"int64\")\n", + " label_length = label_length * tf.ones(shape=(batch_len, 1), dtype=\"int64\")\n", + "\n", + " loss = tf.keras.backend.ctc_batch_cost(y_true, y_pred, input_length, label_length)\n", + " return loss" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "id": "a26dc3fc-a19c-4378-bd8c-e2b597a1d15c", + "metadata": { + "tags": [], + "id": "a26dc3fc-a19c-4378-bd8c-e2b597a1d15c" + }, + "outputs": [], + "source": [ + "class ProduceExample(tf.keras.callbacks.Callback):\n", + " def __init__(self, dataset) -> None:\n", + " self.dataset = dataset.as_numpy_iterator()\n", + "\n", + " def on_epoch_end(self, epoch, logs=None) -> None:\n", + " data = self.dataset.next()\n", + " yhat = self.model.predict(data[0])\n", + " decoded = tf.keras.backend.ctc_decode(yhat, [75,75], greedy=False)[0][0].numpy()\n", + " for x in range(len(yhat)):\n", + " print('Original:', tf.strings.reduce_join(num_to_char(data[1][x])).numpy().decode('utf-8'))\n", + " print('Prediction:', tf.strings.reduce_join(num_to_char(decoded[x])).numpy().decode('utf-8'))\n", + " print('~'*100)" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "id": "04be90d8-2482-46f9-b513-d5f4f8001c7e", + "metadata": { + "tags": [], + "id": "04be90d8-2482-46f9-b513-d5f4f8001c7e" + }, + "outputs": [], + "source": [ + "model.compile(optimizer=Adam(learning_rate=0.0001), loss=CTCLoss)" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "id": "eab49367-3f1e-4464-ae76-dbd07549d97e", + "metadata": { + "tags": [], + "id": "eab49367-3f1e-4464-ae76-dbd07549d97e" + }, + "outputs": [], + "source": [ + "checkpoint_callback = ModelCheckpoint(os.path.join('models','checkpoint'), monitor='loss', save_weights_only=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "id": "e085a632-d464-46ef-8777-959cad4adb2c", + "metadata": { + "tags": [], + "id": "e085a632-d464-46ef-8777-959cad4adb2c" + }, + "outputs": [], + "source": [ + "schedule_callback = LearningRateScheduler(scheduler)" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "id": "48eca991-90ab-4592-8a79-b50e9ca015b6", + "metadata": { + "tags": [], + "id": "48eca991-90ab-4592-8a79-b50e9ca015b6" + }, + "outputs": [], + "source": [ + "example_callback = ProduceExample(test)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8ffba483-aa61-4bbe-a15f-a73e1ddf097c", + "metadata": { + "tags": [], + "id": "8ffba483-aa61-4bbe-a15f-a73e1ddf097c", + "outputId": "5ac8771e-cd3b-4d4d-eaf5-15add3c1e8ae", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Epoch 1/100\n", + "1/1 [==============================] - 2s 2s/step\n", + "Original: bin blue with s eight now\n", + "Prediction: le e e e n\n", + "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n", + "Original: set blue by n seven soon\n", + "Prediction: le e e e n\n", + "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n", + "450/450 [==============================] - 909s 2s/step - loss: 85.6971 - val_loss: 71.1052 - lr: 1.0000e-04\n", + "Epoch 2/100\n", + "1/1 [==============================] - 0s 217ms/step\n", + "Original: bin red by t two please\n", + "Prediction: la e e e eo\n", + "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n", + "Original: place white in d zero please\n", + "Prediction: la e e e eo\n", + "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n", + "450/450 [==============================] - 667s 1s/step - loss: 70.9566 - val_loss: 65.3939 - lr: 1.0000e-04\n", + "Epoch 3/100\n", + "1/1 [==============================] - 0s 210ms/step\n", + "Original: lay white in y seven soon\n", + "Prediction: la e e i e eo\n", + "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n", + "Original: bin blue in l two now\n", + "Prediction: la e e i e eo\n", + "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n", + "450/450 [==============================] - 662s 1s/step - loss: 66.6268 - val_loss: 61.9794 - lr: 1.0000e-04\n", + "Epoch 4/100\n", + "1/1 [==============================] - 0s 216ms/step\n", + "Original: bin blue with g three again\n", + "Prediction: la e i e eo\n", + "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n", + "Original: bin red at s eight sp please\n", + "Prediction: la e t e eo\n", + "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n", + "450/450 [==============================] - 660s 1s/step - loss: 64.0591 - val_loss: 59.9585 - lr: 1.0000e-04\n", + "Epoch 5/100\n", + "141/450 [========>.....................] - ETA: 4:55 - loss: 62.9847" + ] + } + ], + "source": [ + "model.fit(train, validation_data=test, epochs=100, callbacks=[checkpoint_callback, schedule_callback, example_callback])" + ] + }, + { + "cell_type": "markdown", + "id": "fa8ee94b-89f7-4733-8a0c-a86f86ff590a", + "metadata": { + "tags": [], + "id": "fa8ee94b-89f7-4733-8a0c-a86f86ff590a" + }, + "source": [ + "# 5. Make a Prediction" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "01fa7204-ce0e-49a8-8dbd-14fe5dfead40", + "metadata": { + "tags": [], + "id": "01fa7204-ce0e-49a8-8dbd-14fe5dfead40" + }, + "outputs": [], + "source": [ + "url = 'https://drive.google.com/uc?id=1vWscXs4Vt0a_1IH1-ct2TCgXAZT-N3_Y'\n", + "output = 'checkpoints.zip'\n", + "gdown.download(url, output, quiet=False)\n", + "gdown.extractall('checkpoints.zip', 'models')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "247f664d-3c87-4e96-946e-930dad0e1c2c", + "metadata": { + "tags": [], + "id": "247f664d-3c87-4e96-946e-930dad0e1c2c" + }, + "outputs": [], + "source": [ + "model.load_weights('models/checkpoint')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7f8d689f-b7bb-443c-9b88-e40c1d800828", + "metadata": { + "tags": [], + "id": "7f8d689f-b7bb-443c-9b88-e40c1d800828" + }, + "outputs": [], + "source": [ + "test_data = test.as_numpy_iterator()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "38546dc2-bee9-4837-864b-8a884df40ad7", + "metadata": { + "tags": [], + "id": "38546dc2-bee9-4837-864b-8a884df40ad7" + }, + "outputs": [], + "source": [ + "sample = test_data.next()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a43621f0-229d-4c0d-9554-9c3a3da9c61a", + "metadata": { + "tags": [], + "id": "a43621f0-229d-4c0d-9554-9c3a3da9c61a" + }, + "outputs": [], + "source": [ + "yhat = model.predict(sample[0])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ea462999-f87e-4a7e-a057-5be7b6d8f7d5", + "metadata": { + "tags": [], + "id": "ea462999-f87e-4a7e-a057-5be7b6d8f7d5" + }, + "outputs": [], + "source": [ + "print('~'*100, 'REAL TEXT')\n", + "[tf.strings.reduce_join([num_to_char(word) for word in sentence]) for sentence in sample[1]]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "82bd4c10-dd6e-411e-834b-2a3b43fd12c5", + "metadata": { + "tags": [], + "id": "82bd4c10-dd6e-411e-834b-2a3b43fd12c5" + }, + "outputs": [], + "source": [ + "decoded = tf.keras.backend.ctc_decode(yhat, input_length=[75,75], greedy=True)[0][0].numpy()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5d68ac46-c90b-4eab-a709-f19aee569ff5", + "metadata": { + "tags": [], + "id": "5d68ac46-c90b-4eab-a709-f19aee569ff5" + }, + "outputs": [], + "source": [ + "print('~'*100, 'PREDICTIONS')\n", + "[tf.strings.reduce_join([num_to_char(word) for word in sentence]) for sentence in decoded]" + ] + }, + { + "cell_type": "markdown", + "id": "64622f98-e99b-4fed-a2cc-f0da82eb5431", + "metadata": { + "id": "64622f98-e99b-4fed-a2cc-f0da82eb5431" + }, + "source": [ + "# Test on a Video" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a8b0c4d0-2031-4331-b91d-d87b1ae6f6e2", + "metadata": { + "id": "a8b0c4d0-2031-4331-b91d-d87b1ae6f6e2" + }, + "outputs": [], + "source": [ + "sample = load_data(tf.convert_to_tensor('.\\\\data\\\\s1\\\\bras9a.mpg'))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0cca60e4-47a9-4683-8a75-48f4684f723d", + "metadata": { + "id": "0cca60e4-47a9-4683-8a75-48f4684f723d" + }, + "outputs": [], + "source": [ + "print('~'*100, 'REAL TEXT')\n", + "[tf.strings.reduce_join([num_to_char(word) for word in sentence]) for sentence in [sample[1]]]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8cc5037c-1e32-435c-b0cc-01e1fb3b863c", + "metadata": { + "id": "8cc5037c-1e32-435c-b0cc-01e1fb3b863c" + }, + "outputs": [], + "source": [ + "yhat = model.predict(tf.expand_dims(sample[0], axis=0))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "22c4f77d-715d-409f-bc5e-3ebe48704e8f", + "metadata": { + "id": "22c4f77d-715d-409f-bc5e-3ebe48704e8f" + }, + "outputs": [], + "source": [ + "decoded = tf.keras.backend.ctc_decode(yhat, input_length=[75], greedy=True)[0][0].numpy()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e4d12ecc-b634-499e-a4bc-db9f010835fb", + "metadata": { + "id": "e4d12ecc-b634-499e-a4bc-db9f010835fb" + }, + "outputs": [], + "source": [ + "print('~'*100, 'PREDICTIONS')\n", + "[tf.strings.reduce_join([num_to_char(word) for word in sentence]) for sentence in decoded]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "551dfea2-de6b-4400-b71a-a17631529e3f", + "metadata": { + "id": "551dfea2-de6b-4400-b71a-a17631529e3f" + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fa95863d-3832-47bf-8a77-ebaa38054ace", + "metadata": { + "id": "fa95863d-3832-47bf-8a77-ebaa38054ace" + }, + "outputs": [], + "source": [] } - ], - "source": [ - "print('~'*100, 'PREDICTIONS')\n", - "[tf.strings.reduce_join([num_to_char(word) for word in sentence]) for sentence in decoded]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "551dfea2-de6b-4400-b71a-a17631529e3f", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "fa95863d-3832-47bf-8a77-ebaa38054ace", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "lips", - "language": "python", - "name": "lips" + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "name": "python" + }, + "colab": { + "provenance": [], + "gpuType": "T4", + "include_colab_link": true + }, + "accelerator": "GPU" }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} + "nbformat": 4, + "nbformat_minor": 5 +} \ No newline at end of file From f123d9b51fbc5ea74000b4634e7622ee9c2ec41b Mon Sep 17 00:00:00 2001 From: "haidun.lhd" Date: Wed, 3 Apr 2024 12:08:53 -0300 Subject: [PATCH 02/17] Refactor modelutil.py: Import formatting and load_model function signature --- app/modelutil.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/app/modelutil.py b/app/modelutil.py index 7521a1e9..0c8e8a7d 100644 --- a/app/modelutil.py +++ b/app/modelutil.py @@ -1,8 +1,8 @@ -import os -from tensorflow.keras.models import Sequential +import os +from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Conv3D, LSTM, Dense, Dropout, Bidirectional, MaxPool3D, Activation, Reshape, SpatialDropout3D, BatchNormalization, TimeDistributed, Flatten -def load_model() -> Sequential: +def load_model() -> Sequential: model = Sequential() model.add(Conv3D(128, 3, input_shape=(75,46,140,1), padding='same')) From 3a6f5e5691bae8422c53058f392fa2d982f240f3 Mon Sep 17 00:00:00 2001 From: "haidun.li" Date: Wed, 10 Apr 2024 02:49:33 -0300 Subject: [PATCH 03/17] Refactor modelutil.py: Import Orthogonal initializer and update LSTM layers --- LipNet.ipynb | 2693 ++++++++++++++++++++----------------------- app/modelutil.py | 8 +- app/streamlitapp.py | 32 +- 3 files changed, 1255 insertions(+), 1478 deletions(-) diff --git a/LipNet.ipynb b/LipNet.ipynb index 8bf7b07c..a43422cc 100644 --- a/LipNet.ipynb +++ b/LipNet.ipynb @@ -3,8 +3,8 @@ { "cell_type": "markdown", "metadata": { - "id": "view-in-github", - "colab_type": "text" + "colab_type": "text", + "id": "view-in-github" }, "source": [ "\"Open" @@ -14,8 +14,8 @@ "cell_type": "markdown", "id": "a3573a47-3689-4668-b62f-5c8451b2b4e9", "metadata": { - "tags": [], - "id": "a3573a47-3689-4668-b62f-5c8451b2b4e9" + "id": "a3573a47-3689-4668-b62f-5c8451b2b4e9", + "tags": [] }, "source": [ "# 0. Install and Import Dependencies" @@ -23,502 +23,128 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 41, "id": "ddfbccbe-41ae-4c23-98b1-a13868e2b499", "metadata": { - "scrolled": true, - "tags": [], - "id": "ddfbccbe-41ae-4c23-98b1-a13868e2b499", - "outputId": "964dae10-91f7-4769-b15c-4a00ca9db312", "colab": { "base_uri": "https://localhost:8080/" - } + }, + "id": "ddfbccbe-41ae-4c23-98b1-a13868e2b499", + "outputId": "964dae10-91f7-4769-b15c-4a00ca9db312", + "scrolled": true, + "tags": [] }, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ - "Package Version\n", - "-------------------------------- ---------------------\n", - "absl-py 1.4.0\n", - "aiohttp 3.9.3\n", - "aiosignal 1.3.1\n", - "alabaster 0.7.16\n", - "albumentations 1.3.1\n", - "altair 4.2.2\n", - "annotated-types 0.6.0\n", - "anyio 3.7.1\n", - "appdirs 1.4.4\n", - "argon2-cffi 23.1.0\n", - "argon2-cffi-bindings 21.2.0\n", - "array-record 0.5.0\n", - "arviz 0.15.1\n", - "astropy 5.3.4\n", - "astunparse 1.6.3\n", - "async-timeout 4.0.3\n", - "atpublic 4.0\n", - "attrs 23.2.0\n", - "audioread 3.0.1\n", - "autograd 1.6.2\n", - "Babel 2.14.0\n", - "backcall 0.2.0\n", - "beautifulsoup4 4.12.3\n", - "bidict 0.23.1\n", - "bigframes 0.26.0\n", - "bleach 6.1.0\n", - "blinker 1.4\n", - "blis 0.7.11\n", - "blosc2 2.0.0\n", - "bokeh 3.3.4\n", - "bqplot 0.12.43\n", - "branca 0.7.1\n", - "build 1.1.1\n", - "CacheControl 0.14.0\n", - "cachetools 5.3.3\n", - "catalogue 2.0.10\n", - "certifi 2024.2.2\n", - "cffi 1.16.0\n", - "chardet 5.2.0\n", - "charset-normalizer 3.3.2\n", - "chex 0.1.86\n", - "click 8.1.7\n", - "click-plugins 1.1.1\n", - "cligj 0.7.2\n", - "cloudpathlib 0.16.0\n", - "cloudpickle 2.2.1\n", - "cmake 3.27.9\n", - "cmdstanpy 1.2.1\n", - "colorcet 3.1.0\n", - "colorlover 0.3.0\n", - "colour 0.1.5\n", - "community 1.0.0b1\n", - "confection 0.1.4\n", - "cons 0.4.6\n", - "contextlib2 21.6.0\n", - "contourpy 1.2.0\n", - "cryptography 42.0.5\n", - "cufflinks 0.17.3\n", - "cupy-cuda12x 12.2.0\n", - "cvxopt 1.3.2\n", - "cvxpy 1.3.3\n", - "cycler 0.12.1\n", - "cymem 2.0.8\n", - "Cython 3.0.9\n", - "dask 2023.8.1\n", - "datascience 0.17.6\n", - "db-dtypes 1.2.0\n", - "dbus-python 1.2.18\n", - "debugpy 1.6.6\n", - "decorator 4.4.2\n", - "defusedxml 0.7.1\n", - "distributed 2023.8.1\n", - "distro 1.7.0\n", - "dlib 19.24.2\n", - "dm-tree 0.1.8\n", - "docutils 0.18.1\n", - "dopamine-rl 4.0.6\n", - "duckdb 0.9.2\n", - "earthengine-api 0.1.394\n", - "easydict 1.13\n", - "ecos 2.0.13\n", - "editdistance 0.6.2\n", - "eerepr 0.0.4\n", - "en-core-web-sm 3.7.1\n", - "entrypoints 0.4\n", - "et-xmlfile 1.1.0\n", - "etils 1.7.0\n", - "etuples 0.3.9\n", - "exceptiongroup 1.2.0\n", - "fastai 2.7.14\n", - "fastcore 1.5.29\n", - "fastdownload 0.0.7\n", - "fastjsonschema 2.19.1\n", - "fastprogress 1.0.3\n", - "fastrlock 0.8.2\n", - "filelock 3.13.1\n", - "fiona 1.9.6\n", - "firebase-admin 5.3.0\n", - "Flask 2.2.5\n", - "flatbuffers 24.3.7\n", - "flax 0.8.2\n", - "folium 0.14.0\n", - "fonttools 4.50.0\n", - "frozendict 2.4.0\n", - "frozenlist 1.4.1\n", - "fsspec 2023.6.0\n", - "future 0.18.3\n", - "gast 0.5.4\n", - "gcsfs 2023.6.0\n", - "GDAL 3.6.4\n", - "gdown 4.7.3\n", - "geemap 0.32.0\n", - "gensim 4.3.2\n", - "geocoder 1.38.1\n", - "geographiclib 2.0\n", - "geopandas 0.13.2\n", - "geopy 2.3.0\n", - "gin-config 0.5.0\n", - "glob2 0.7\n", - "google 2.0.3\n", - "google-ai-generativelanguage 0.4.0\n", - "google-api-core 2.11.1\n", - "google-api-python-client 2.84.0\n", - "google-auth 2.27.0\n", - "google-auth-httplib2 0.1.1\n", - "google-auth-oauthlib 1.2.0\n", - "google-cloud-aiplatform 1.44.0\n", - "google-cloud-bigquery 3.12.0\n", - "google-cloud-bigquery-connection 1.12.1\n", - "google-cloud-bigquery-storage 2.24.0\n", - "google-cloud-core 2.3.3\n", - "google-cloud-datastore 2.15.2\n", - "google-cloud-firestore 2.11.1\n", - "google-cloud-functions 1.13.3\n", - "google-cloud-iam 2.14.3\n", - "google-cloud-language 2.13.3\n", - "google-cloud-resource-manager 1.12.3\n", - "google-cloud-storage 2.8.0\n", - "google-cloud-translate 3.11.3\n", - "google-colab 1.0.0\n", - "google-crc32c 1.5.0\n", - "google-generativeai 0.3.2\n", - "google-pasta 0.2.0\n", - "google-resumable-media 2.7.0\n", - "googleapis-common-protos 1.63.0\n", - "googledrivedownloader 0.4\n", - "graphviz 0.20.2\n", - "greenlet 3.0.3\n", - "grpc-google-iam-v1 0.13.0\n", - "grpcio 1.62.1\n", - "grpcio-status 1.48.2\n", - "gspread 3.4.2\n", - "gspread-dataframe 3.3.1\n", - "gym 0.25.2\n", - "gym-notices 0.0.8\n", - "h5netcdf 1.3.0\n", - "h5py 3.9.0\n", - "holidays 0.45\n", - "holoviews 1.17.1\n", - "html5lib 1.1\n", - "httpimport 1.3.1\n", - "httplib2 0.22.0\n", - "huggingface-hub 0.20.3\n", - "humanize 4.7.0\n", - "hyperopt 0.2.7\n", - "ibis-framework 8.0.0\n", - "idna 3.6\n", - "imageio 2.23.0\n", - "imageio-ffmpeg 0.4.9\n", - "imagesize 1.4.1\n", - "imbalanced-learn 0.10.1\n", - "imgaug 0.4.0\n", - "importlib_metadata 7.0.2\n", - "importlib_resources 6.3.2\n", - "imutils 0.5.4\n", - "inflect 7.0.0\n", - "iniconfig 2.0.0\n", - "intel-openmp 2023.2.4\n", - "ipyevents 2.0.2\n", - "ipyfilechooser 0.6.0\n", - "ipykernel 5.5.6\n", - "ipyleaflet 0.18.2\n", - "ipython 7.34.0\n", - "ipython-genutils 0.2.0\n", - "ipython-sql 0.5.0\n", - "ipytree 0.2.2\n", - "ipywidgets 7.7.1\n", - "itsdangerous 2.1.2\n", - "jax 0.4.23\n", - "jaxlib 0.4.23+cuda12.cudnn89\n", - "jeepney 0.7.1\n", - "jieba 0.42.1\n", - "Jinja2 3.1.3\n", - "joblib 1.3.2\n", - "jsonpickle 3.0.3\n", - "jsonschema 4.19.2\n", - "jsonschema-specifications 2023.12.1\n", - "jupyter-client 6.1.12\n", - "jupyter-console 6.1.0\n", - "jupyter_core 5.7.2\n", - "jupyter-server 1.24.0\n", - "jupyterlab_pygments 0.3.0\n", - "jupyterlab_widgets 3.0.10\n", - "kaggle 1.5.16\n", - "kagglehub 0.2.0\n", - "keras 2.15.0\n", - "keyring 23.5.0\n", - "kiwisolver 1.4.5\n", - "langcodes 3.3.0\n", - "launchpadlib 1.10.16\n", - "lazr.restfulclient 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3.2.1\n", - "nibabel 4.0.2\n", - "nltk 3.8.1\n", - "notebook 6.5.5\n", - "notebook_shim 0.2.4\n", - "numba 0.58.1\n", - "numexpr 2.9.0\n", - "numpy 1.25.2\n", - "oauth2client 4.1.3\n", - "oauthlib 3.2.2\n", - "opencv-contrib-python 4.8.0.76\n", - "opencv-python 4.8.0.76\n", - "opencv-python-headless 4.9.0.80\n", - "openpyxl 3.1.2\n", - "opt-einsum 3.3.0\n", - "optax 0.2.1\n", - "orbax-checkpoint 0.4.4\n", - "osqp 0.6.2.post8\n", - "packaging 24.0\n", - "pandas 1.5.3\n", - "pandas-datareader 0.10.0\n", - "pandas-gbq 0.19.2\n", - "pandas-stubs 1.5.3.230304\n", - "pandocfilters 1.5.1\n", - "panel 1.3.8\n", - "param 2.0.2\n", - "parso 0.8.3\n", - "parsy 2.1\n", - "partd 1.4.1\n", - "pathlib 1.0.1\n", - "patsy 0.5.6\n", - "peewee 3.17.1\n", - "pexpect 4.9.0\n", - "pickleshare 0.7.5\n", - "Pillow 9.4.0\n", - "pip 23.1.2\n", - "pip-tools 6.13.0\n", - "platformdirs 4.2.0\n", - "plotly 5.15.0\n", - "plotnine 0.12.4\n", - "pluggy 1.4.0\n", - "polars 0.20.2\n", - "pooch 1.8.1\n", - "portpicker 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"protobuf 4.25.3\n", + "psutil 5.9.8\n", + "pure-eval 0.2.2\n", + "pyarrow 15.0.2\n", + "pydeck 0.8.1b0\n", + "Pygments 2.17.2\n", + "pyparsing 3.1.2\n", + "PySocks 1.7.1\n", + "python-dateutil 2.9.0.post0\n", + "pytz 2024.1\n", + "pywin32 306\n", + "pyzmq 25.1.2\n", + "referencing 0.34.0\n", + "requests 2.31.0\n", + "rich 13.7.1\n", + "rpds-py 0.18.0\n", + "setuptools 69.2.0\n", + "six 1.16.0\n", + "smmap 5.0.1\n", + "soupsieve 2.5\n", + "stack-data 0.6.3\n", + "streamlit 1.33.0\n", + "tenacity 8.2.3\n", + "tensorboard 2.16.2\n", + "tensorboard-data-server 0.7.2\n", + "tensorflow 2.16.1\n", + "tensorflow-intel 2.16.1\n", + "termcolor 2.4.0\n", + "toml 0.10.2\n", + "toolz 0.12.1\n", + "tornado 6.4\n", + "tqdm 4.66.2\n", + "traitlets 5.14.2\n", + "typing_extensions 4.11.0\n", + "tzdata 2024.1\n", + "urllib3 2.2.1\n", + "watchdog 4.0.0\n", + "wcwidth 0.2.13\n", + "Werkzeug 3.0.2\n", + "wheel 0.43.0\n", + "wrapt 1.16.0\n" ] } ], @@ -528,78 +154,77 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 42, "id": "02f907ea-f669-46c7-adcf-7f257e663448", "metadata": { - "tags": [], - "id": "02f907ea-f669-46c7-adcf-7f257e663448", - "outputId": "e444b2f9-189a-40bd-a2f9-925f13917c35", "colab": { "base_uri": "https://localhost:8080/" - } + }, + "id": "02f907ea-f669-46c7-adcf-7f257e663448", + "outputId": "e444b2f9-189a-40bd-a2f9-925f13917c35", + "tags": [] }, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ - "Requirement already satisfied: opencv-python in /usr/local/lib/python3.10/dist-packages (4.8.0.76)\n", - "Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (3.7.1)\n", - "Requirement already satisfied: imageio in /usr/local/lib/python3.10/dist-packages (2.23.0)\n", - "Requirement already satisfied: gdown in /usr/local/lib/python3.10/dist-packages (4.7.3)\n", - "Requirement already satisfied: tensorflow in /usr/local/lib/python3.10/dist-packages (2.15.0)\n", - 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(1.25.2)\n", - "Requirement already satisfied: pillow>=8.3.2 in /usr/local/lib/python3.10/dist-packages (from imageio==2.23.0) (9.4.0)\n" + "Defaulting to user installation because normal site-packages is not writeable\n", + "Requirement already satisfied: imageio==2.23.0 in c:\\users\\lhdto\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.12_qbz5n2kfra8p0\\localcache\\local-packages\\python312\\site-packages (2.23.0)\n", + "Requirement already satisfied: numpy in c:\\users\\lhdto\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.12_qbz5n2kfra8p0\\localcache\\local-packages\\python312\\site-packages (from imageio==2.23.0) (1.26.4)\n", + "Requirement already satisfied: pillow>=8.3.2 in c:\\users\\lhdto\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.12_qbz5n2kfra8p0\\localcache\\local-packages\\python312\\site-packages (from imageio==2.23.0) (10.3.0)\n" ] } + ], + "source": [ + "!pip install imageio==2.23.0" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 44, "id": "b24af50c-20b8-409d-ad78-30a933fdd669", "metadata": { - "tags": [], - "id": "b24af50c-20b8-409d-ad78-30a933fdd669" + "id": "b24af50c-20b8-409d-ad78-30a933fdd669", + "tags": [] }, "outputs": [], "source": [ @@ -654,25 +280,25 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 45, "id": "1e3db0b0-e559-4ad6-91fd-e7414b7d75e6", "metadata": { - "id": "1e3db0b0-e559-4ad6-91fd-e7414b7d75e6", - "outputId": "c1e41844-c8fd-4efc-eb22-11f3817b0c9f", "colab": { "base_uri": "https://localhost:8080/" - } + }, + "id": "1e3db0b0-e559-4ad6-91fd-e7414b7d75e6", + "outputId": "c1e41844-c8fd-4efc-eb22-11f3817b0c9f" }, "outputs": [ { - "output_type": "execute_result", "data": { "text/plain": [ - "[PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]" + "[]" ] }, + "execution_count": 45, "metadata": {}, - "execution_count": 11 + "output_type": "execute_result" } ], "source": [ @@ -681,11 +307,11 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 46, "id": "378d045a-3003-4f93-b7d2-a25a97774a68", "metadata": { - "tags": [], - "id": "378d045a-3003-4f93-b7d2-a25a97774a68" + "id": "378d045a-3003-4f93-b7d2-a25a97774a68", + "tags": [] }, "outputs": [], "source": [ @@ -700,8 +326,8 @@ "cell_type": "markdown", "id": "7a19e88e-c7b9-45c1-ae1e-f2109329c71b", "metadata": { - "tags": [], - "id": "7a19e88e-c7b9-45c1-ae1e-f2109329c71b" + "id": "7a19e88e-c7b9-45c1-ae1e-f2109329c71b", + "tags": [] }, "source": [ "# 1. Build Data Loading Functions" @@ -709,11 +335,11 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 47, "id": "8fb99c90-e05a-437f-839d-6e772f8c1dd5", "metadata": { - "tags": [], - "id": "8fb99c90-e05a-437f-839d-6e772f8c1dd5" + "id": "8fb99c90-e05a-437f-839d-6e772f8c1dd5", + "tags": [] }, "outputs": [], "source": [ @@ -722,30 +348,29 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 48, "id": "c019e4c6-2af3-4160-99ea-5c8cb009f1a7", "metadata": { - "tags": [], - "id": "c019e4c6-2af3-4160-99ea-5c8cb009f1a7", - "outputId": "49a7c7ff-ab7d-480e-bcfd-ee652ad89af1", "colab": { "base_uri": "https://localhost:8080/" - } + }, + "id": "c019e4c6-2af3-4160-99ea-5c8cb009f1a7", + "outputId": "49a7c7ff-ab7d-480e-bcfd-ee652ad89af1", + "tags": [] }, "outputs": [ { - "output_type": "stream", "name": "stderr", + "output_type": "stream", "text": [ "Downloading...\n", "From (original): https://drive.google.com/uc?id=1YlvpDLix3S-U8fd-gqRwPcWXAXm8JwjL\n", - "From (redirected): https://drive.google.com/uc?id=1YlvpDLix3S-U8fd-gqRwPcWXAXm8JwjL&confirm=t&uuid=9de512c9-8ed3-42ac-ba38-5de21c84c9e9\n", - "To: /content/data.zip\n", - "100%|██████████| 423M/423M [00:03<00:00, 128MB/s]\n" + "From (redirected): https://drive.google.com/uc?id=1YlvpDLix3S-U8fd-gqRwPcWXAXm8JwjL&confirm=t&uuid=70f5926c-96a4-40bd-a64d-77761b61a3b6\n", + "To: c:\\haidun.li\\NSCC\\LipNet\\data.zip\n", + "100%|██████████| 423M/423M [00:10<00:00, 41.3MB/s] \n" ] }, { - "output_type": "execute_result", "data": { "text/plain": [ "['data/',\n", @@ -1751,8 +1376,9 @@ " ...]" ] }, + "execution_count": 48, "metadata": {}, - "execution_count": 14 + "output_type": "execute_result" } ], "source": [ @@ -1764,11 +1390,11 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 49, "id": "8548cc59-6dfc-4acc-abc3-3e65212db02e", "metadata": { - "tags": [], - "id": "8548cc59-6dfc-4acc-abc3-3e65212db02e" + "id": "8548cc59-6dfc-4acc-abc3-3e65212db02e", + "tags": [] }, "outputs": [], "source": [ @@ -1789,11 +1415,11 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 50, "id": "ec735e0b-ec98-4eb0-8f49-c35527d6670a", "metadata": { - "tags": [], - "id": "ec735e0b-ec98-4eb0-8f49-c35527d6670a" + "id": "ec735e0b-ec98-4eb0-8f49-c35527d6670a", + "tags": [] }, "outputs": [], "source": [ @@ -1802,20 +1428,20 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 51, "id": "be04e972-d7a5-4a72-82d8-a6bdde1f3ce6", "metadata": { - "tags": [], - "id": "be04e972-d7a5-4a72-82d8-a6bdde1f3ce6", - "outputId": "01f490ee-dabf-4e94-c6ed-181e8c1ae2c4", "colab": { "base_uri": "https://localhost:8080/" - } + }, + "id": "be04e972-d7a5-4a72-82d8-a6bdde1f3ce6", + "outputId": "01f490ee-dabf-4e94-c6ed-181e8c1ae2c4", + "tags": [] }, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "The vocabulary is: ['', 'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z', \"'\", '?', '!', '1', '2', '3', '4', '5', '6', '7', '8', '9', ' '] (size =40)\n" ] @@ -1835,19 +1461,18 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 52, "id": "559f7420-6802-45fa-9ca0-b1ff209b461c", "metadata": { - "tags": [], - "id": "559f7420-6802-45fa-9ca0-b1ff209b461c", - "outputId": "90f0547b-3308-478e-be4a-c89cef1d58e6", "colab": { "base_uri": "https://localhost:8080/" - } + }, + "id": "559f7420-6802-45fa-9ca0-b1ff209b461c", + "outputId": "90f0547b-3308-478e-be4a-c89cef1d58e6", + "tags": [] }, "outputs": [ { - "output_type": "execute_result", "data": { "text/plain": [ "['',\n", @@ -1892,8 +1517,9 @@ " ' ']" ] }, + "execution_count": 52, "metadata": {}, - "execution_count": 18 + "output_type": "execute_result" } ], "source": [ @@ -1902,26 +1528,26 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 53, "id": "797ff78b-b48f-4e14-bb62-8cd0ebf9501a", "metadata": { - "tags": [], - "id": "797ff78b-b48f-4e14-bb62-8cd0ebf9501a", - "outputId": "cedf213c-95e4-439a-a871-641aec307517", "colab": { "base_uri": "https://localhost:8080/" - } + }, + "id": "797ff78b-b48f-4e14-bb62-8cd0ebf9501a", + "outputId": "cedf213c-95e4-439a-a871-641aec307517", + "tags": [] }, "outputs": [ { - "output_type": "execute_result", "data": { "text/plain": [ - "" + "" ] }, + "execution_count": 53, "metadata": {}, - "execution_count": 19 + "output_type": "execute_result" } ], "source": [ @@ -1930,25 +1556,25 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 54, "id": "8cd7f4f4-ae77-4509-a4f4-c723787ebad1", "metadata": { - "id": "8cd7f4f4-ae77-4509-a4f4-c723787ebad1", - "outputId": "cb5428be-73ea-47b2-ea8e-4d2c4e3dcb00", "colab": { "base_uri": "https://localhost:8080/" - } + }, + "id": "8cd7f4f4-ae77-4509-a4f4-c723787ebad1", + "outputId": "cb5428be-73ea-47b2-ea8e-4d2c4e3dcb00" }, "outputs": [ { - "output_type": "execute_result", "data": { "text/plain": [ "" ] }, + "execution_count": 54, "metadata": {}, - "execution_count": 20 + "output_type": "execute_result" } ], "source": [ @@ -1957,11 +1583,11 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 55, "id": "9491bab5-6a3c-4f79-879a-8f9fbe73ae2e", "metadata": { - "tags": [], - "id": "9491bab5-6a3c-4f79-879a-8f9fbe73ae2e" + "id": "9491bab5-6a3c-4f79-879a-8f9fbe73ae2e", + "tags": [] }, "outputs": [], "source": [ @@ -1978,19 +1604,19 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 69, "id": "dd01ca9f-77fb-4643-a2aa-47dd82c5d66b", "metadata": { - "tags": [], - "id": "dd01ca9f-77fb-4643-a2aa-47dd82c5d66b" + "id": "dd01ca9f-77fb-4643-a2aa-47dd82c5d66b", + "tags": [] }, "outputs": [], "source": [ "def load_data(path: str):\n", " path = bytes.decode(path.numpy())\n", - " file_name = path.split('/')[-1].split('.')[0]\n", + " # file_name = path.split('/')[-1].split('.')[0]\n", " # File name splitting for windows\n", - " # file_name = path.split('\\\\')[-1].split('.')[0]\n", + " file_name = path.split('\\\\')[-1].split('.')[0]\n", " video_path = os.path.join('data','s1',f'{file_name}.mpg')\n", " alignment_path = os.path.join('data','alignments','s1',f'{file_name}.align')\n", " frames = load_video(video_path)\n", @@ -2001,43 +1627,40 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 73, "id": "8cb7cc58-31ae-4904-a805-1177a82717d2", "metadata": { - "tags": [], - "id": "8cb7cc58-31ae-4904-a805-1177a82717d2" + "id": "8cb7cc58-31ae-4904-a805-1177a82717d2", + "tags": [] }, "outputs": [], "source": [ - "# test_path = '.\\\\data\\\\s1\\\\bbal6n.mpg'\n", - "test_path = './data/s1/bbal6n.mpg'" + "test_path = '.\\\\data\\\\s1\\\\bbal6n.mpg'\n", + "# test_path = './data/s1/bbal6n.mpg'" ] }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 74, "id": "76aa964f-0c84-490d-897a-d00e3966e2c9", "metadata": { - "id": "76aa964f-0c84-490d-897a-d00e3966e2c9", - "outputId": "d04d49ad-4eeb-4be1-ffa7-f2655aa86a7a", "colab": { "base_uri": "https://localhost:8080/", "height": 35 - } + }, + "id": "76aa964f-0c84-490d-897a-d00e3966e2c9", + "outputId": "d04d49ad-4eeb-4be1-ffa7-f2655aa86a7a" }, "outputs": [ { - "output_type": "execute_result", "data": { "text/plain": [ - "'bbal6n'" - ], - "application/vnd.google.colaboratory.intrinsic+json": { - "type": "string" - } + "''" + ] }, + "execution_count": 74, "metadata": {}, - "execution_count": 24 + "output_type": "execute_result" } ], "source": [ @@ -2047,12 +1670,12 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 75, "id": "eb602c71-8560-4f9e-b26b-08202febb937", "metadata": { + "id": "eb602c71-8560-4f9e-b26b-08202febb937", "scrolled": true, - "tags": [], - "id": "eb602c71-8560-4f9e-b26b-08202febb937" + "tags": [] }, "outputs": [], "source": [ @@ -2061,36 +1684,36 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 76, "id": "0e3184a1-6b02-4b4f-84a8-a0a65f951ea2", "metadata": { - "id": "0e3184a1-6b02-4b4f-84a8-a0a65f951ea2", - "outputId": "ca65c601-b300-4a41-d8fe-7915a8e5a50b", "colab": { "base_uri": "https://localhost:8080/", "height": 244 - } + }, + "id": "0e3184a1-6b02-4b4f-84a8-a0a65f951ea2", + "outputId": "ca65c601-b300-4a41-d8fe-7915a8e5a50b" }, "outputs": [ { - "output_type": "execute_result", "data": { "text/plain": [ - "" + "" ] }, + "execution_count": 76, "metadata": {}, - "execution_count": 26 + "output_type": "execute_result" }, { - "output_type": "display_data", "data": { + "image/png": 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", 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\n" + ] }, - "metadata": {} + "metadata": {}, + "output_type": "display_data" } ], "source": [ @@ -2099,27 +1722,27 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 77, "id": "d7ec0833-d54b-4073-84cf-92d011c60ec1", "metadata": { - "id": "d7ec0833-d54b-4073-84cf-92d011c60ec1", - "outputId": "02abd7a1-113b-42b1-a6ba-9a458373b112", "colab": { "base_uri": "https://localhost:8080/" - } + }, + "id": "d7ec0833-d54b-4073-84cf-92d011c60ec1", + "outputId": "02abd7a1-113b-42b1-a6ba-9a458373b112" }, "outputs": [ { - "output_type": "execute_result", "data": { "text/plain": [ "" + " 39, 14, 15, 23], dtype=int64)>" ] }, + "execution_count": 77, "metadata": {}, - "execution_count": 27 + "output_type": "execute_result" } ], "source": [ @@ -2128,25 +1751,25 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 78, "id": "fe1ad370-b287-4b46-85a2-7c45b0bd9b10", "metadata": { - "id": "fe1ad370-b287-4b46-85a2-7c45b0bd9b10", - "outputId": "8910d788-545b-4e76-b154-4a90d8e63927", "colab": { "base_uri": "https://localhost:8080/" - } + }, + "id": "fe1ad370-b287-4b46-85a2-7c45b0bd9b10", + "outputId": "8910d788-545b-4e76-b154-4a90d8e63927" }, "outputs": [ { - "output_type": "execute_result", "data": { "text/plain": [ "" ] }, + "execution_count": 78, "metadata": {}, - "execution_count": 28 + "output_type": "execute_result" } ], "source": [ @@ -2155,11 +1778,11 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 79, "id": "6871031a-b0ba-4c76-a852-f6329b0f2606", "metadata": { - "tags": [], - "id": "6871031a-b0ba-4c76-a852-f6329b0f2606" + "id": "6871031a-b0ba-4c76-a852-f6329b0f2606", + "tags": [] }, "outputs": [], "source": [ @@ -2172,8 +1795,8 @@ "cell_type": "markdown", "id": "c40a7eb4-0c3e-4eab-9291-5611cb68ce08", "metadata": { - "tags": [], - "id": "c40a7eb4-0c3e-4eab-9291-5611cb68ce08" + "id": "c40a7eb4-0c3e-4eab-9291-5611cb68ce08", + "tags": [] }, "source": [ "# 2. Create Data Pipeline" @@ -2181,11 +1804,11 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 80, "id": "7686355d-45aa-4c85-ad9c-053e6a9b4d81", "metadata": { - "tags": [], - "id": "7686355d-45aa-4c85-ad9c-053e6a9b4d81" + "id": "7686355d-45aa-4c85-ad9c-053e6a9b4d81", + "tags": [] }, "outputs": [], "source": [ @@ -2194,11 +1817,11 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 81, "id": "f066fea2-91b1-42ed-a67d-00566a1a53ff", "metadata": { - "tags": [], - "id": "f066fea2-91b1-42ed-a67d-00566a1a53ff" + "id": "f066fea2-91b1-42ed-a67d-00566a1a53ff", + "tags": [] }, "outputs": [], "source": [ @@ -2214,25 +1837,25 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 82, "id": "6b1365bd-7742-41d1-95d4-247021751c3a", "metadata": { - "id": "6b1365bd-7742-41d1-95d4-247021751c3a", - "outputId": "facfc0fa-bf61-4aff-b8ed-f1a42278a4b3", "colab": { "base_uri": "https://localhost:8080/" - } + }, + "id": "6b1365bd-7742-41d1-95d4-247021751c3a", + "outputId": "facfc0fa-bf61-4aff-b8ed-f1a42278a4b3" }, "outputs": [ { - "output_type": "execute_result", "data": { "text/plain": [ "50" ] }, + "execution_count": 82, "metadata": {}, - "execution_count": 32 + "output_type": "execute_result" } ], "source": [ @@ -2241,7 +1864,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 83, "id": "5281bde8-fdc8-4da1-bd55-5a7929a9e80c", "metadata": { "id": "5281bde8-fdc8-4da1-bd55-5a7929a9e80c" @@ -2253,25 +1876,25 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 84, "id": "cbebe683-6afd-47fd-bba4-c83b4b13bb32", "metadata": { - "id": "cbebe683-6afd-47fd-bba4-c83b4b13bb32", - "outputId": "8924019e-28cd-4bfa-9e1e-bb836d71e6f8", "colab": { "base_uri": "https://localhost:8080/" - } + }, + "id": "cbebe683-6afd-47fd-bba4-c83b4b13bb32", + "outputId": "8924019e-28cd-4bfa-9e1e-bb836d71e6f8" }, "outputs": [ { - "output_type": "execute_result", "data": { "text/plain": [ "2" ] }, + "execution_count": 84, "metadata": {}, - "execution_count": 34 + "output_type": "execute_result" } ], "source": [ @@ -2280,11 +1903,11 @@ }, { "cell_type": "code", - 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"execution_count": 36 + "output_type": "execute_result" } ], "source": [ @@ -2938,96 +2561,102 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 87, "id": "acf5eb4f-a0da-4a9a-bf24-af13e9cc2fbe", "metadata": { - "tags": [], - "id": "acf5eb4f-a0da-4a9a-bf24-af13e9cc2fbe", - "outputId": "2b7ce709-6400-402c-ab9d-0e4c5739cafc", "colab": { "base_uri": "https://localhost:8080/" - } + }, + "id": "acf5eb4f-a0da-4a9a-bf24-af13e9cc2fbe", + "outputId": "2b7ce709-6400-402c-ab9d-0e4c5739cafc", + "tags": [] }, "outputs": [ { + "name": "stderr", "output_type": "stream", + "text": [ + "Lossy conversion from float32 to uint8. Range [0.0, 10.607527732849121]. Convert image to uint8 prior to saving to suppress this warning.\n", + "Lossy conversion from float32 to uint8. Range [0.0, 10.607527732849121]. Convert image to uint8 prior to saving to suppress this warning.\n", + "Lossy conversion from float32 to uint8. Range [0.0, 10.607527732849121]. Convert image to uint8 prior to saving to suppress this warning.\n", + "Lossy conversion from float32 to uint8. Range [0.0, 10.607527732849121]. Convert image to uint8 prior to saving to suppress this warning.\n", + "Lossy conversion from float32 to uint8. Range [0.0, 10.607527732849121]. Convert image to uint8 prior to saving to suppress this warning.\n", + "Lossy conversion from float32 to uint8. Range [0.0, 10.607527732849121]. Convert image to uint8 prior to saving to suppress this warning.\n", + "Lossy conversion from float32 to uint8. Range [0.0, 10.607527732849121]. Convert image to uint8 prior to saving to suppress this warning.\n", + "Lossy conversion from float32 to uint8. Range [0.0, 10.607527732849121]. Convert image to uint8 prior to saving to suppress this warning.\n", + "Lossy conversion from float32 to uint8. Range [0.0, 10.607527732849121]. Convert image to uint8 prior to saving to suppress this warning.\n", + "Lossy conversion from float32 to uint8. Range [0.0, 10.607527732849121]. Convert image to uint8 prior to saving to suppress this warning.\n", + "Lossy conversion from float32 to uint8. Range [0.0, 10.607527732849121]. Convert image to uint8 prior to saving to suppress this warning.\n", + "Lossy conversion from float32 to uint8. Range [0.0, 10.607527732849121]. Convert image to uint8 prior to saving to suppress this warning.\n", + "Lossy conversion from float32 to uint8. Range [0.0, 10.607527732849121]. Convert image to uint8 prior to saving to suppress this warning.\n", + "Lossy conversion from float32 to uint8. Range [0.0, 10.607527732849121]. Convert image to uint8 prior to saving to suppress this warning.\n", + "Lossy conversion from float32 to uint8. Range [0.0, 10.607527732849121]. Convert image to uint8 prior to saving to suppress this warning.\n", + "Lossy conversion from float32 to uint8. Range [0.0, 10.607527732849121]. Convert image to uint8 prior to saving to suppress this warning.\n", + "Lossy conversion from float32 to uint8. Range [0.0, 10.607527732849121]. Convert image to uint8 prior to saving to suppress this warning.\n", + "Lossy conversion from float32 to uint8. Range [0.0, 10.607527732849121]. Convert image to uint8 prior to saving to suppress this warning.\n", + "Lossy conversion from float32 to uint8. Range [0.0, 10.607527732849121]. Convert image to uint8 prior to saving to suppress this warning.\n", + "Lossy conversion from float32 to uint8. Range [0.0, 10.607527732849121]. Convert image to uint8 prior to saving to suppress this warning.\n", + "Lossy conversion from float32 to uint8. Range [0.0, 10.607527732849121]. Convert image to uint8 prior to saving to suppress this warning.\n", + "Lossy conversion from float32 to uint8. Range [0.0, 10.607527732849121]. Convert image to uint8 prior to saving to suppress this warning.\n", + "Lossy conversion from float32 to uint8. Range [0.0, 10.607527732849121]. Convert image to uint8 prior to saving to suppress this warning.\n", + "Lossy conversion from float32 to uint8. Range [0.0, 10.607527732849121]. Convert image to uint8 prior to saving to suppress this warning.\n" + ] + }, + { "name": "stderr", + "output_type": "stream", "text": [ - "WARNING:imageio:Lossy conversion from float32 to uint8. Range [0.0, 9.52289867401123]. Convert image to uint8 prior to saving to suppress this warning.\n", - "WARNING:imageio:Lossy conversion from float32 to uint8. Range [0.0, 9.52289867401123]. Convert image to uint8 prior to saving to suppress this warning.\n", - "WARNING:imageio:Lossy conversion from float32 to uint8. Range [0.0, 9.52289867401123]. Convert image to uint8 prior to saving to suppress this warning.\n", - "WARNING:imageio:Lossy conversion from float32 to uint8. Range [0.0, 9.52289867401123]. Convert image to uint8 prior to saving to suppress this warning.\n", - "WARNING:imageio:Lossy conversion from float32 to uint8. Range [0.0, 9.52289867401123]. Convert image to uint8 prior to saving to suppress this warning.\n", - "WARNING:imageio:Lossy conversion from float32 to uint8. Range [0.0, 9.52289867401123]. Convert image to uint8 prior to saving to suppress this warning.\n", - "WARNING:imageio:Lossy conversion from float32 to uint8. Range [0.0, 9.52289867401123]. Convert image to uint8 prior to saving to suppress this warning.\n", - "WARNING:imageio:Lossy conversion from float32 to uint8. Range [0.0, 9.52289867401123]. Convert image to uint8 prior to saving to suppress this warning.\n", - "WARNING:imageio:Lossy conversion from float32 to uint8. Range [0.0, 9.52289867401123]. Convert image to uint8 prior to saving to suppress this warning.\n", - "WARNING:imageio:Lossy conversion from float32 to uint8. Range [0.0, 9.52289867401123]. Convert image to uint8 prior to saving to suppress this warning.\n", - "WARNING:imageio:Lossy conversion from float32 to uint8. Range [0.0, 9.52289867401123]. Convert image to uint8 prior to saving to suppress this warning.\n", - "WARNING:imageio:Lossy conversion from float32 to uint8. Range [0.0, 9.52289867401123]. Convert image to uint8 prior to saving to suppress this warning.\n", - "WARNING:imageio:Lossy conversion from float32 to uint8. Range [0.0, 9.52289867401123]. Convert image to uint8 prior to saving to suppress this warning.\n", - "WARNING:imageio:Lossy conversion from float32 to uint8. Range [0.0, 9.52289867401123]. 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Convert image to uint8 prior to saving to suppress this warning.\n", - "WARNING:imageio:Lossy conversion from float32 to uint8. Range [0.0, 9.52289867401123]. Convert image to uint8 prior to saving to suppress this warning.\n", - "WARNING:imageio:Lossy conversion from float32 to uint8. Range [0.0, 9.52289867401123]. Convert image to uint8 prior to saving to suppress this warning.\n", - "WARNING:imageio:Lossy conversion from float32 to uint8. Range [0.0, 9.52289867401123]. Convert image to uint8 prior to saving to suppress this warning.\n", - "WARNING:imageio:Lossy conversion from float32 to uint8. Range [0.0, 9.52289867401123]. Convert image to uint8 prior to saving to suppress this warning.\n", - "WARNING:imageio:Lossy conversion from float32 to uint8. Range [0.0, 9.52289867401123]. Convert image to uint8 prior to saving to suppress this warning.\n", - "WARNING:imageio:Lossy conversion from float32 to uint8. Range [0.0, 9.52289867401123]. 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Convert image to uint8 prior to saving to suppress this warning.\n", - "WARNING:imageio:Lossy conversion from float32 to uint8. Range [0.0, 9.52289867401123]. Convert image to uint8 prior to saving to suppress this warning.\n", - "WARNING:imageio:Lossy conversion from float32 to uint8. Range [0.0, 9.52289867401123]. Convert image to uint8 prior to saving to suppress this warning.\n", - "WARNING:imageio:Lossy conversion from float32 to uint8. Range [0.0, 9.52289867401123]. Convert image to uint8 prior to saving to suppress this warning.\n", - "WARNING:imageio:Lossy conversion from float32 to uint8. Range [0.0, 9.52289867401123]. Convert image to uint8 prior to saving to suppress this warning.\n", - "WARNING:imageio:Lossy conversion from float32 to uint8. Range [0.0, 9.52289867401123]. Convert image to uint8 prior to saving to suppress this warning.\n", - "WARNING:imageio:Lossy conversion from float32 to uint8. Range [0.0, 9.52289867401123]. Convert image to uint8 prior to saving to suppress this warning.\n", - "WARNING:imageio:Lossy conversion from float32 to uint8. Range [0.0, 9.52289867401123]. Convert image to uint8 prior to saving to suppress this warning.\n", - "WARNING:imageio:Lossy conversion from float32 to uint8. Range [0.0, 9.52289867401123]. Convert image to uint8 prior to saving to suppress this warning.\n", - "WARNING:imageio:Lossy conversion from float32 to uint8. Range [0.0, 9.52289867401123]. Convert image to uint8 prior to saving to suppress this warning.\n", - "WARNING:imageio:Lossy conversion from float32 to uint8. Range [0.0, 9.52289867401123]. Convert image to uint8 prior to saving to suppress this warning.\n", - "WARNING:imageio:Lossy conversion from float32 to uint8. Range [0.0, 9.52289867401123]. Convert image to uint8 prior to saving to suppress this warning.\n", - "WARNING:imageio:Lossy conversion from float32 to uint8. Range [0.0, 9.52289867401123]. Convert image to uint8 prior to saving to suppress this warning.\n", - "WARNING:imageio:Lossy conversion from float32 to uint8. Range [0.0, 9.52289867401123]. Convert image to uint8 prior to saving to suppress this warning.\n", - "WARNING:imageio:Lossy conversion from float32 to uint8. Range [0.0, 9.52289867401123]. Convert image to uint8 prior to saving to suppress this warning.\n", - "WARNING:imageio:Lossy conversion from float32 to uint8. Range [0.0, 9.52289867401123]. Convert image to uint8 prior to saving to suppress this warning.\n", - "WARNING:imageio:Lossy conversion from float32 to uint8. Range [0.0, 9.52289867401123]. Convert image to uint8 prior to saving to suppress this warning.\n", - "WARNING:imageio:Lossy conversion from float32 to uint8. Range [0.0, 9.52289867401123]. Convert image to uint8 prior to saving to suppress this warning.\n", - "WARNING:imageio:Lossy conversion from float32 to uint8. Range [0.0, 9.52289867401123]. Convert image to uint8 prior to saving to suppress this warning.\n", - "WARNING:imageio:Lossy conversion from float32 to uint8. Range [0.0, 9.52289867401123]. Convert image to uint8 prior to saving to suppress this warning.\n", - "WARNING:imageio:Lossy conversion from float32 to uint8. Range [0.0, 9.52289867401123]. Convert image to uint8 prior to saving to suppress this warning.\n", - "WARNING:imageio:Lossy conversion from float32 to uint8. Range [0.0, 9.52289867401123]. Convert image to uint8 prior to saving to suppress this warning.\n", - "WARNING:imageio:Lossy conversion from float32 to uint8. Range [0.0, 9.52289867401123]. Convert image to uint8 prior to saving to suppress this warning.\n", - "WARNING:imageio:Lossy conversion from float32 to uint8. Range [0.0, 9.52289867401123]. Convert image to uint8 prior to saving to suppress this warning.\n", - "WARNING:imageio:Lossy conversion from float32 to uint8. Range [0.0, 9.52289867401123]. Convert image to uint8 prior to saving to suppress this warning.\n", - "WARNING:imageio:Lossy conversion from float32 to uint8. Range [0.0, 9.52289867401123]. Convert image to uint8 prior to saving to suppress this warning.\n", - "WARNING:imageio:Lossy conversion from float32 to uint8. Range [0.0, 9.52289867401123]. Convert image to uint8 prior to saving to suppress this warning.\n", - "WARNING:imageio:Lossy conversion from float32 to uint8. Range [0.0, 9.52289867401123]. Convert image to uint8 prior to saving to suppress this warning.\n", - "WARNING:imageio:Lossy conversion from float32 to uint8. Range [0.0, 9.52289867401123]. Convert image to uint8 prior to saving to suppress this warning.\n", - "WARNING:imageio:Lossy conversion from float32 to uint8. Range [0.0, 9.52289867401123]. Convert image to uint8 prior to saving to suppress this warning.\n", - "WARNING:imageio:Lossy conversion from float32 to uint8. Range [0.0, 9.52289867401123]. Convert image to uint8 prior to saving to suppress this warning.\n", - "WARNING:imageio:Lossy conversion from float32 to uint8. Range [0.0, 9.52289867401123]. Convert image to uint8 prior to saving to suppress this warning.\n", - "WARNING:imageio:Lossy conversion from float32 to uint8. Range [0.0, 9.52289867401123]. Convert image to uint8 prior to saving to suppress this warning.\n", - "WARNING:imageio:Lossy conversion from float32 to uint8. Range [0.0, 9.52289867401123]. Convert image to uint8 prior to saving to suppress this warning.\n", - "WARNING:imageio:Lossy conversion from float32 to uint8. Range [0.0, 9.52289867401123]. Convert image to uint8 prior to saving to suppress this warning.\n", - "WARNING:imageio:Lossy conversion from float32 to uint8. Range [0.0, 9.52289867401123]. Convert image to uint8 prior to saving to suppress this warning.\n", - "WARNING:imageio:Lossy conversion from float32 to uint8. Range [0.0, 9.52289867401123]. Convert image to uint8 prior to saving to suppress this warning.\n", - "WARNING:imageio:Lossy conversion from float32 to uint8. Range [0.0, 9.52289867401123]. Convert image to uint8 prior to saving to suppress this warning.\n", - "WARNING:imageio:Lossy conversion from float32 to uint8. Range [0.0, 9.52289867401123]. Convert image to uint8 prior to saving to suppress this warning.\n", - "WARNING:imageio:Lossy conversion from float32 to uint8. Range [0.0, 9.52289867401123]. Convert image to uint8 prior to saving to suppress this warning.\n", - "WARNING:imageio:Lossy conversion from float32 to uint8. Range [0.0, 9.52289867401123]. Convert image to uint8 prior to saving to suppress this warning.\n", - "WARNING:imageio:Lossy conversion from float32 to uint8. Range [0.0, 9.52289867401123]. Convert image to uint8 prior to saving to suppress this warning.\n", - "WARNING:imageio:Lossy conversion from float32 to uint8. Range [0.0, 9.52289867401123]. Convert image to uint8 prior to saving to suppress this warning.\n", - "WARNING:imageio:Lossy conversion from float32 to uint8. Range [0.0, 9.52289867401123]. Convert image to uint8 prior to saving to suppress this warning.\n" + "Lossy conversion from float32 to uint8. Range [0.0, 10.607527732849121]. Convert image to uint8 prior to saving to suppress this warning.\n", + "Lossy conversion from float32 to uint8. Range [0.0, 10.607527732849121]. Convert image to uint8 prior to saving to suppress this warning.\n", + "Lossy conversion from float32 to uint8. Range [0.0, 10.607527732849121]. Convert image to uint8 prior to saving to suppress this warning.\n", + "Lossy conversion from float32 to uint8. Range [0.0, 10.607527732849121]. Convert image to uint8 prior to saving to suppress this warning.\n", + "Lossy conversion from float32 to uint8. Range [0.0, 10.607527732849121]. Convert image to uint8 prior to saving to suppress this warning.\n", + "Lossy conversion from float32 to uint8. Range [0.0, 10.607527732849121]. Convert image to uint8 prior to saving to suppress this warning.\n", + "Lossy conversion from float32 to uint8. Range [0.0, 10.607527732849121]. Convert image to uint8 prior to saving to suppress this warning.\n", + "Lossy conversion from float32 to uint8. Range [0.0, 10.607527732849121]. Convert image to uint8 prior to saving to suppress this warning.\n", + "Lossy conversion from float32 to uint8. Range [0.0, 10.607527732849121]. Convert image to uint8 prior to saving to suppress this warning.\n", + "Lossy conversion from float32 to uint8. Range [0.0, 10.607527732849121]. Convert image to uint8 prior to saving to suppress this warning.\n", + "Lossy conversion from float32 to uint8. Range [0.0, 10.607527732849121]. Convert image to uint8 prior to saving to suppress this warning.\n", + "Lossy conversion from float32 to uint8. Range [0.0, 10.607527732849121]. Convert image to uint8 prior to saving to suppress this warning.\n", + "Lossy conversion from float32 to uint8. Range [0.0, 10.607527732849121]. Convert image to uint8 prior to saving to suppress this warning.\n", + "Lossy conversion from float32 to uint8. Range [0.0, 10.607527732849121]. Convert image to uint8 prior to saving to suppress this warning.\n", + "Lossy conversion from float32 to uint8. Range [0.0, 10.607527732849121]. Convert image to uint8 prior to saving to suppress this warning.\n", + "Lossy conversion from float32 to uint8. Range [0.0, 10.607527732849121]. Convert image to uint8 prior to saving to suppress this warning.\n", + "Lossy conversion from float32 to uint8. Range [0.0, 10.607527732849121]. Convert image to uint8 prior to saving to suppress this warning.\n", + "Lossy conversion from float32 to uint8. Range [0.0, 10.607527732849121]. Convert image to uint8 prior to saving to suppress this warning.\n", + "Lossy conversion from float32 to uint8. Range [0.0, 10.607527732849121]. Convert image to uint8 prior to saving to suppress this warning.\n", + "Lossy conversion from float32 to uint8. Range [0.0, 10.607527732849121]. Convert image to uint8 prior to saving to suppress this warning.\n", + "Lossy conversion from float32 to uint8. Range [0.0, 10.607527732849121]. Convert image to uint8 prior to saving to suppress this warning.\n", + "Lossy conversion from float32 to uint8. Range [0.0, 10.607527732849121]. Convert image to uint8 prior to saving to suppress this warning.\n", + "Lossy conversion from float32 to uint8. Range [0.0, 10.607527732849121]. Convert image to uint8 prior to saving to suppress this warning.\n", + "Lossy conversion from float32 to uint8. Range [0.0, 10.607527732849121]. Convert image to uint8 prior to saving to suppress this warning.\n", + "Lossy conversion from float32 to uint8. Range [0.0, 10.607527732849121]. Convert image to uint8 prior to saving to suppress this warning.\n", + "Lossy conversion from float32 to uint8. Range [0.0, 10.607527732849121]. Convert image to uint8 prior to saving to suppress this warning.\n", + "Lossy conversion from float32 to uint8. Range [0.0, 10.607527732849121]. Convert image to uint8 prior to saving to suppress this warning.\n", + "Lossy conversion from float32 to uint8. Range [0.0, 10.607527732849121]. Convert image to uint8 prior to saving to suppress this warning.\n", + "Lossy conversion from float32 to uint8. Range [0.0, 10.607527732849121]. Convert image to uint8 prior to saving to suppress this warning.\n", + "Lossy conversion from float32 to uint8. Range [0.0, 10.607527732849121]. Convert image to uint8 prior to saving to suppress this warning.\n", + "Lossy conversion from float32 to uint8. Range [0.0, 10.607527732849121]. Convert image to uint8 prior to saving to suppress this warning.\n", + "Lossy conversion from float32 to uint8. Range [0.0, 10.607527732849121]. Convert image to uint8 prior to saving to suppress this warning.\n", + "Lossy conversion from float32 to uint8. Range [0.0, 10.607527732849121]. Convert image to uint8 prior to saving to suppress this warning.\n", + "Lossy conversion from float32 to uint8. Range [0.0, 10.607527732849121]. Convert image to uint8 prior to saving to suppress this warning.\n", + "Lossy conversion from float32 to uint8. Range [0.0, 10.607527732849121]. Convert image to uint8 prior to saving to suppress this warning.\n", + "Lossy conversion from float32 to uint8. Range [0.0, 10.607527732849121]. Convert image to uint8 prior to saving to suppress this warning.\n", + "Lossy conversion from float32 to uint8. Range [0.0, 10.607527732849121]. Convert image to uint8 prior to saving to suppress this warning.\n", + "Lossy conversion from float32 to uint8. Range [0.0, 10.607527732849121]. Convert image to uint8 prior to saving to suppress this warning.\n", + "Lossy conversion from float32 to uint8. Range [0.0, 10.607527732849121]. Convert image to uint8 prior to saving to suppress this warning.\n", + "Lossy conversion from float32 to uint8. Range [0.0, 10.607527732849121]. Convert image to uint8 prior to saving to suppress this warning.\n", + "Lossy conversion from float32 to uint8. Range [0.0, 10.607527732849121]. Convert image to uint8 prior to saving to suppress this warning.\n", + "Lossy conversion from float32 to uint8. Range [0.0, 10.607527732849121]. Convert image to uint8 prior to saving to suppress this warning.\n", + "Lossy conversion from float32 to uint8. Range [0.0, 10.607527732849121]. Convert image to uint8 prior to saving to suppress this warning.\n", + "Lossy conversion from float32 to uint8. Range [0.0, 10.607527732849121]. Convert image to uint8 prior to saving to suppress this warning.\n", + "Lossy conversion from float32 to uint8. Range [0.0, 10.607527732849121]. Convert image to uint8 prior to saving to suppress this warning.\n", + "Lossy conversion from float32 to uint8. Range [0.0, 10.607527732849121]. Convert image to uint8 prior to saving to suppress this warning.\n", + "Lossy conversion from float32 to uint8. Range [0.0, 10.607527732849121]. Convert image to uint8 prior to saving to suppress this warning.\n", + "Lossy conversion from float32 to uint8. Range [0.0, 10.607527732849121]. Convert image to uint8 prior to saving to suppress this warning.\n", + "Lossy conversion from float32 to uint8. Range [0.0, 10.607527732849121]. Convert image to uint8 prior to saving to suppress this warning.\n", + "Lossy conversion from float32 to uint8. Range [0.0, 10.607527732849121]. Convert image to uint8 prior to saving to suppress this warning.\n", + "Lossy conversion from float32 to uint8. Range [0.0, 10.607527732849121]. Convert image to uint8 prior to saving to suppress this warning.\n" ] } ], @@ -3037,37 +2666,37 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 88, "id": "c33a87a2-d5e0-4ec9-b174-73ebf41bf03a", "metadata": { - "tags": [], - "id": "c33a87a2-d5e0-4ec9-b174-73ebf41bf03a", - "outputId": "1b7cc028-1990-4714-8d7e-fda85218c0d5", "colab": { "base_uri": "https://localhost:8080/", "height": 244 - } + }, + "id": "c33a87a2-d5e0-4ec9-b174-73ebf41bf03a", + "outputId": "1b7cc028-1990-4714-8d7e-fda85218c0d5", + "tags": [] }, "outputs": [ { - "output_type": "execute_result", "data": { "text/plain": [ - "" + "" ] }, + "execution_count": 88, "metadata": {}, - "execution_count": 38 + "output_type": "execute_result" }, { - "output_type": "display_data", "data": { + "image/png": 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", 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\n" + ] }, - "metadata": {} + "metadata": {}, + "output_type": "display_data" } ], "source": [ @@ -3077,26 +2706,26 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 89, "id": "84593332-133c-4205-b7a6-8e235d5e2b3b", "metadata": { - "tags": [], - "id": "84593332-133c-4205-b7a6-8e235d5e2b3b", - "outputId": "69f24afc-2cb9-4d07-9d93-3b4e1fde5046", "colab": { "base_uri": "https://localhost:8080/" - } + }, + "id": "84593332-133c-4205-b7a6-8e235d5e2b3b", + "outputId": "69f24afc-2cb9-4d07-9d93-3b4e1fde5046", + "tags": [] }, "outputs": [ { - "output_type": "execute_result", "data": { "text/plain": [ - "" + "" ] }, + "execution_count": 89, "metadata": {}, - "execution_count": 39 + "output_type": "execute_result" } ], "source": [ @@ -3107,8 +2736,8 @@ "cell_type": "markdown", "id": "0f47733c-83bc-465c-b118-b198b492ad37", "metadata": { - "tags": [], - "id": "0f47733c-83bc-465c-b118-b198b492ad37" + "id": "0f47733c-83bc-465c-b118-b198b492ad37", + "tags": [] }, "source": [ "# 3. Design the Deep Neural Network" @@ -3116,11 +2745,11 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 90, "id": "d8e9a497-191b-4842-afbd-26f5e13c43ba", "metadata": { - "tags": [], - "id": "d8e9a497-191b-4842-afbd-26f5e13c43ba" + "id": "d8e9a497-191b-4842-afbd-26f5e13c43ba", + "tags": [] }, "outputs": [], "source": [ @@ -3132,25 +2761,36 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 93, + "id": "5d376ebe", + "metadata": {}, + "outputs": [], + "source": [ + "from keras.layers import LSTM, Bidirectional\n", + "from keras.initializers import Orthogonal" + ] + }, + { + "cell_type": "code", + "execution_count": 94, "id": "3f753ed2-70b9-4236-8c1c-08ca065dc8bf", "metadata": { - "id": "3f753ed2-70b9-4236-8c1c-08ca065dc8bf", - "outputId": "b06442c3-cc68-4c88-d5e8-c9a915fc8b96", "colab": { "base_uri": "https://localhost:8080/" - } + }, + "id": "3f753ed2-70b9-4236-8c1c-08ca065dc8bf", + "outputId": "b06442c3-cc68-4c88-d5e8-c9a915fc8b96" }, "outputs": [ { - "output_type": "execute_result", "data": { "text/plain": [ "(75, 46, 140, 1)" ] }, + "execution_count": 94, "metadata": {}, - "execution_count": 41 + "output_type": "execute_result" } ], "source": [ @@ -3159,11 +2799,11 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 114, "id": "f9171056-a352-491a-9ed9-92b28ced268e", "metadata": { - "tags": [], - "id": "f9171056-a352-491a-9ed9-92b28ced268e" + "id": "f9171056-a352-491a-9ed9-92b28ced268e", + "tags": [] }, "outputs": [], "source": [ @@ -3180,12 +2820,17 @@ "model.add(Activation('relu'))\n", "model.add(MaxPool3D((1,2,2)))\n", "\n", + "\n", "model.add(TimeDistributed(Flatten()))\n", "\n", - "model.add(Bidirectional(LSTM(128, kernel_initializer='Orthogonal', return_sequences=True)))\n", + "# model.add(Bidirectional(LSTM(128, kernel_initializer='Orthogonal', return_sequences=True)))\n", + "# Now use the Orthogonal initializer directly when adding your layer\n", + "model.add(Bidirectional(LSTM(128, kernel_initializer=Orthogonal(), return_sequences=True)))\n", "model.add(Dropout(.5))\n", "\n", - "model.add(Bidirectional(LSTM(128, kernel_initializer='Orthogonal', return_sequences=True)))\n", + "# model.add(Bidirectional(LSTM(128, kernel_initializer='Orthogonal', return_sequences=True)))\n", + "# Now use the Orthogonal initializer directly when adding your layer\n", + "model.add(Bidirectional(LSTM(128, kernel_initializer=Orthogonal(), return_sequences=True)))\n", "model.add(Dropout(.5))\n", "\n", "model.add(Dense(char_to_num.vocabulary_size()+1, kernel_initializer='he_normal', activation='softmax'))" @@ -3193,73 +2838,163 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 115, "id": "78851825-2bcd-42a9-b7f2-28bb5a6bf43a", "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, "collapsed": true, + "id": "78851825-2bcd-42a9-b7f2-28bb5a6bf43a", "jupyter": { "outputs_hidden": true }, - "tags": [], - "id": "78851825-2bcd-42a9-b7f2-28bb5a6bf43a", "outputId": "90ebbd81-10a8-4674-8551-7b3cd530381a", - "colab": { - "base_uri": "https://localhost:8080/" - } + "tags": [] }, "outputs": [ { - "output_type": "stream", - "name": "stdout", - "text": [ - "Model: \"sequential\"\n", - "_________________________________________________________________\n", - " Layer (type) Output Shape Param # \n", - "=================================================================\n", - " conv3d (Conv3D) (None, 75, 46, 140, 128 3584 \n", - " ) \n", - " \n", - " activation (Activation) (None, 75, 46, 140, 128 0 \n", - " ) \n", - " \n", - " max_pooling3d (MaxPooling3 (None, 75, 23, 70, 128) 0 \n", - " D) \n", - " \n", - " conv3d_1 (Conv3D) (None, 75, 23, 70, 256) 884992 \n", - " \n", - " activation_1 (Activation) (None, 75, 23, 70, 256) 0 \n", - " \n", - " max_pooling3d_1 (MaxPoolin (None, 75, 11, 35, 256) 0 \n", - " g3D) \n", - " \n", - " conv3d_2 (Conv3D) (None, 75, 11, 35, 75) 518475 \n", - " \n", - " activation_2 (Activation) (None, 75, 11, 35, 75) 0 \n", - " \n", - " max_pooling3d_2 (MaxPoolin (None, 75, 5, 17, 75) 0 \n", - " g3D) \n", - " \n", - " time_distributed (TimeDist (None, 75, 6375) 0 \n", - " ributed) \n", - " \n", - " bidirectional (Bidirection (None, 75, 256) 6660096 \n", - " al) \n", - " \n", - " dropout (Dropout) (None, 75, 256) 0 \n", - " \n", - " bidirectional_1 (Bidirecti (None, 75, 256) 394240 \n", - " onal) \n", - " \n", - " dropout_1 (Dropout) (None, 75, 256) 0 \n", - " \n", - " dense (Dense) (None, 75, 41) 10537 \n", - " \n", - "=================================================================\n", - "Total params: 8471924 (32.32 MB)\n", - "Trainable params: 8471924 (32.32 MB)\n", - "Non-trainable params: 0 (0.00 Byte)\n", - "_________________________________________________________________\n" - ] + "data": { + "text/html": [ + "
Model: \"sequential_5\"\n",
+              "
\n" + ], + "text/plain": [ + "\u001b[1mModel: \"sequential_5\"\u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+              "┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃\n",
+              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+              "│ conv3d_15 (Conv3D)              │ (None, 75, 46, 140,    │         3,584 │\n",
+              "│                                 │ 128)                   │               │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ activation_15 (Activation)      │ (None, 75, 46, 140,    │             0 │\n",
+              "│                                 │ 128)                   │               │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ max_pooling3d_15 (MaxPooling3D) │ (None, 75, 23, 70,     │             0 │\n",
+              "│                                 │ 128)                   │               │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ conv3d_16 (Conv3D)              │ (None, 75, 23, 70,     │       884,992 │\n",
+              "│                                 │ 256)                   │               │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ activation_16 (Activation)      │ (None, 75, 23, 70,     │             0 │\n",
+              "│                                 │ 256)                   │               │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ max_pooling3d_16 (MaxPooling3D) │ (None, 75, 11, 35,     │             0 │\n",
+              "│                                 │ 256)                   │               │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ conv3d_17 (Conv3D)              │ (None, 75, 11, 35, 75) │       518,475 │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ activation_17 (Activation)      │ (None, 75, 11, 35, 75) │             0 │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ max_pooling3d_17 (MaxPooling3D) │ (None, 75, 5, 17, 75)  │             0 │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ time_distributed_4              │ (None, 75, 6375)       │             0 │\n",
+              "│ (TimeDistributed)               │                        │               │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ bidirectional_3 (Bidirectional) │ (None, 75, 256)        │     6,660,096 │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ dropout_3 (Dropout)             │ (None, 75, 256)        │             0 │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ bidirectional_4 (Bidirectional) │ (None, 75, 256)        │       394,240 │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ dropout_4 (Dropout)             │ (None, 75, 256)        │             0 │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ dense_1 (Dense)                 │ (None, 75, 41)         │        10,537 │\n",
+              "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
+              "
\n" + ], + "text/plain": [ + "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n", + "┃\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0m┃\n", + "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n", + "│ conv3d_15 (\u001b[38;5;33mConv3D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m46\u001b[0m, \u001b[38;5;34m140\u001b[0m, │ \u001b[38;5;34m3,584\u001b[0m │\n", + "│ │ \u001b[38;5;34m128\u001b[0m) │ │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ activation_15 (\u001b[38;5;33mActivation\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m46\u001b[0m, \u001b[38;5;34m140\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │\n", + "│ │ \u001b[38;5;34m128\u001b[0m) │ │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ max_pooling3d_15 (\u001b[38;5;33mMaxPooling3D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m23\u001b[0m, \u001b[38;5;34m70\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │\n", + "│ │ \u001b[38;5;34m128\u001b[0m) │ │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ conv3d_16 (\u001b[38;5;33mConv3D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m23\u001b[0m, \u001b[38;5;34m70\u001b[0m, │ \u001b[38;5;34m884,992\u001b[0m │\n", + "│ │ \u001b[38;5;34m256\u001b[0m) │ │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ activation_16 (\u001b[38;5;33mActivation\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m23\u001b[0m, \u001b[38;5;34m70\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │\n", + "│ │ \u001b[38;5;34m256\u001b[0m) │ │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ max_pooling3d_16 (\u001b[38;5;33mMaxPooling3D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m11\u001b[0m, \u001b[38;5;34m35\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │\n", + "│ │ \u001b[38;5;34m256\u001b[0m) │ │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ conv3d_17 (\u001b[38;5;33mConv3D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m11\u001b[0m, \u001b[38;5;34m35\u001b[0m, \u001b[38;5;34m75\u001b[0m) │ \u001b[38;5;34m518,475\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ activation_17 (\u001b[38;5;33mActivation\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m11\u001b[0m, \u001b[38;5;34m35\u001b[0m, \u001b[38;5;34m75\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ max_pooling3d_17 (\u001b[38;5;33mMaxPooling3D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m17\u001b[0m, \u001b[38;5;34m75\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ time_distributed_4 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m6375\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n", + "│ (\u001b[38;5;33mTimeDistributed\u001b[0m) │ │ │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ bidirectional_3 (\u001b[38;5;33mBidirectional\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m6,660,096\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ dropout_3 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ bidirectional_4 (\u001b[38;5;33mBidirectional\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m394,240\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ dropout_4 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ dense_1 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m41\u001b[0m) │ \u001b[38;5;34m10,537\u001b[0m │\n", + "└─────────────────────────────────┴────────────────────────┴───────────────┘\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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Setup Training Options and Train" @@ -3442,11 +3177,11 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 122, "id": "ab015fd0-7fb4-4d5d-9fa2-30a05dbd515a", "metadata": { - "tags": [], - "id": "ab015fd0-7fb4-4d5d-9fa2-30a05dbd515a" + "id": "ab015fd0-7fb4-4d5d-9fa2-30a05dbd515a", + "tags": [] }, "outputs": [], "source": [ @@ -3459,11 +3194,11 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 123, "id": "c564d5c9-db54-4e88-b311-9aeab7fb3e69", "metadata": { - "tags": [], - "id": "c564d5c9-db54-4e88-b311-9aeab7fb3e69" + "id": "c564d5c9-db54-4e88-b311-9aeab7fb3e69", + "tags": [] }, "outputs": [], "source": [ @@ -3481,11 +3216,11 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 124, "id": "a26dc3fc-a19c-4378-bd8c-e2b597a1d15c", "metadata": { - "tags": [], - "id": "a26dc3fc-a19c-4378-bd8c-e2b597a1d15c" + "id": "a26dc3fc-a19c-4378-bd8c-e2b597a1d15c", + "tags": [] }, "outputs": [], "source": [ @@ -3505,11 +3240,11 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 125, "id": "04be90d8-2482-46f9-b513-d5f4f8001c7e", "metadata": { - "tags": [], - "id": "04be90d8-2482-46f9-b513-d5f4f8001c7e" + "id": "04be90d8-2482-46f9-b513-d5f4f8001c7e", + "tags": [] }, "outputs": [], "source": [ @@ -3518,24 +3253,36 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": 126, "id": "eab49367-3f1e-4464-ae76-dbd07549d97e", "metadata": { - "tags": [], - "id": "eab49367-3f1e-4464-ae76-dbd07549d97e" + "id": "eab49367-3f1e-4464-ae76-dbd07549d97e", + "tags": [] }, "outputs": [], "source": [ - "checkpoint_callback = ModelCheckpoint(os.path.join('models','checkpoint'), monitor='loss', save_weights_only=True)" + "# checkpoint_callback = ModelCheckpoint(os.path.join('models','checkpoint'), monitor='loss', save_weights_only=True)\n", + "from keras.callbacks import ModelCheckpoint\n", + "import os\n", + "\n", + "# Adjust the file path to end with '.weights.h5'\n", + "checkpoint_path = os.path.join('models', 'checkpoint.weights.h5')\n", + "\n", + "# Create the ModelCheckpoint callback\n", + "checkpoint_callback = ModelCheckpoint(\n", + " filepath=checkpoint_path,\n", + " monitor='loss',\n", + " save_weights_only=True\n", + ")\n" ] }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 127, "id": "e085a632-d464-46ef-8777-959cad4adb2c", "metadata": { - "tags": [], - "id": "e085a632-d464-46ef-8777-959cad4adb2c" + "id": "e085a632-d464-46ef-8777-959cad4adb2c", + "tags": [] }, "outputs": [], "source": [ @@ -3544,11 +3291,11 @@ }, { "cell_type": "code", - "execution_count": 56, + "execution_count": 128, "id": "48eca991-90ab-4592-8a79-b50e9ca015b6", "metadata": { - "tags": [], - "id": "48eca991-90ab-4592-8a79-b50e9ca015b6" + "id": "48eca991-90ab-4592-8a79-b50e9ca015b6", + "tags": [] }, "outputs": [], "source": [ @@ -3557,59 +3304,35 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 129, "id": "8ffba483-aa61-4bbe-a15f-a73e1ddf097c", "metadata": { - "tags": [], - "id": "8ffba483-aa61-4bbe-a15f-a73e1ddf097c", - "outputId": "5ac8771e-cd3b-4d4d-eaf5-15add3c1e8ae", "colab": { "base_uri": "https://localhost:8080/" - } + }, + "id": "8ffba483-aa61-4bbe-a15f-a73e1ddf097c", + "outputId": "5ac8771e-cd3b-4d4d-eaf5-15add3c1e8ae", + "tags": [] }, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ - "Epoch 1/100\n", - "1/1 [==============================] - 2s 2s/step\n", - "Original: bin blue with s eight now\n", - "Prediction: le e e e n\n", - "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n", - "Original: set blue by n seven soon\n", - "Prediction: le e e e n\n", - "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n", - "450/450 [==============================] - 909s 2s/step - loss: 85.6971 - val_loss: 71.1052 - lr: 1.0000e-04\n", - "Epoch 2/100\n", - "1/1 [==============================] - 0s 217ms/step\n", - "Original: bin red by t two please\n", - "Prediction: la e e e eo\n", - "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n", - "Original: place white in d zero please\n", - "Prediction: la e e e eo\n", - "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n", - "450/450 [==============================] - 667s 1s/step - loss: 70.9566 - val_loss: 65.3939 - lr: 1.0000e-04\n", - "Epoch 3/100\n", - "1/1 [==============================] - 0s 210ms/step\n", - "Original: lay white in y seven soon\n", - "Prediction: la e e i e eo\n", - "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n", - "Original: bin blue in l two now\n", - "Prediction: la e e i e eo\n", - "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n", - "450/450 [==============================] - 662s 1s/step - loss: 66.6268 - val_loss: 61.9794 - lr: 1.0000e-04\n", - "Epoch 4/100\n", - "1/1 [==============================] - 0s 216ms/step\n", - "Original: bin blue with g three again\n", - "Prediction: la e i e eo\n", - "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n", - "Original: bin red at s eight sp please\n", - "Prediction: la e t e eo\n", - "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n", - "450/450 [==============================] - 660s 1s/step - loss: 64.0591 - val_loss: 59.9585 - lr: 1.0000e-04\n", - "Epoch 5/100\n", - "141/450 [========>.....................] - ETA: 4:55 - loss: 62.9847" + "Epoch 1/100\n" + ] + }, + { + "ename": "InvalidArgumentError", + "evalue": "Graph execution error:\n\nDetected at node sequential_5_1/time_distributed_4_1/Reshape_9 defined at (most recent call last):\n File \"\", line 198, in _run_module_as_main\n\n File \"\", line 88, in _run_code\n\n File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\ipykernel_launcher.py\", line 18, in \n\n File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\traitlets\\config\\application.py\", line 1075, in launch_instance\n\n File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\ipykernel\\kernelapp.py\", line 739, in start\n\n File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\tornado\\platform\\asyncio.py\", line 205, in start\n\n File \"C:\\Program Files\\WindowsApps\\PythonSoftwareFoundation.Python.3.12_3.12.752.0_x64__qbz5n2kfra8p0\\Lib\\asyncio\\base_events.py\", line 639, in run_forever\n\n File \"C:\\Program Files\\WindowsApps\\PythonSoftwareFoundation.Python.3.12_3.12.752.0_x64__qbz5n2kfra8p0\\Lib\\asyncio\\base_events.py\", line 1985, in _run_once\n\n File \"C:\\Program Files\\WindowsApps\\PythonSoftwareFoundation.Python.3.12_3.12.752.0_x64__qbz5n2kfra8p0\\Lib\\asyncio\\events.py\", line 88, in _run\n\n File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\ipykernel\\kernelbase.py\", line 545, in dispatch_queue\n\n File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\ipykernel\\kernelbase.py\", line 534, in process_one\n\n File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\ipykernel\\kernelbase.py\", line 437, in dispatch_shell\n\n File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\ipykernel\\ipkernel.py\", line 362, in execute_request\n\n File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\ipykernel\\kernelbase.py\", line 778, in execute_request\n\n File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\ipykernel\\ipkernel.py\", line 449, in do_execute\n\n File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\ipykernel\\zmqshell.py\", line 549, in run_cell\n\n File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\IPython\\core\\interactiveshell.py\", line 3075, in run_cell\n\n File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\IPython\\core\\interactiveshell.py\", line 3130, in _run_cell\n\n File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\IPython\\core\\async_helpers.py\", line 129, in _pseudo_sync_runner\n\n File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\IPython\\core\\interactiveshell.py\", line 3334, in run_cell_async\n\n File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\IPython\\core\\interactiveshell.py\", line 3517, in run_ast_nodes\n\n File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\IPython\\core\\interactiveshell.py\", line 3577, in run_code\n\n File \"C:\\Users\\lhdto\\AppData\\Local\\Temp\\ipykernel_15232\\3536403359.py\", line 1, in \n\n File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 117, in error_handler\n\n File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\backend\\tensorflow\\trainer.py\", line 329, in fit\n\n File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\backend\\tensorflow\\trainer.py\", line 122, in one_step_on_iterator\n\n File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\backend\\tensorflow\\trainer.py\", line 110, in one_step_on_data\n\n File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\backend\\tensorflow\\trainer.py\", line 57, in train_step\n\n File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 117, in error_handler\n\n File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\layers\\layer.py\", line 825, in __call__\n\n File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 117, in error_handler\n\n File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\ops\\operation.py\", line 48, in __call__\n\n File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 156, in error_handler\n\n File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\models\\sequential.py\", line 202, in call\n\n File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\models\\functional.py\", line 195, in call\n\n File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\ops\\function.py\", line 151, in _run_through_graph\n\n File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\models\\functional.py\", line 579, in call\n\n File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 117, in error_handler\n\n File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\layers\\layer.py\", line 825, in __call__\n\n File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 117, in error_handler\n\n File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\ops\\operation.py\", line 48, in __call__\n\n File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 156, in error_handler\n\n File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\layers\\rnn\\time_distributed.py\", line 108, in call\n\n File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\layers\\rnn\\time_distributed.py\", line 102, in step_function\n\n File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\layers\\reshaping\\flatten.py\", line 54, in call\n\n File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\ops\\numpy.py\", line 4522, in reshape\n\n File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\backend\\tensorflow\\numpy.py\", line 1618, in reshape\n\nOnly one input size may be -1, not both 0 and 1\n\t [[{{node sequential_5_1/time_distributed_4_1/Reshape_9}}]] [Op:__inference_one_step_on_iterator_65940]", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mInvalidArgumentError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[129], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtrain\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalidation_data\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtest\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mepochs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m100\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcallbacks\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[43mcheckpoint_callback\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mschedule_callback\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mexample_callback\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\utils\\traceback_utils.py:122\u001b[0m, in \u001b[0;36mfilter_traceback..error_handler\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m 119\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n\u001b[0;32m 120\u001b[0m \u001b[38;5;66;03m# To get the full stack trace, call:\u001b[39;00m\n\u001b[0;32m 121\u001b[0m \u001b[38;5;66;03m# `keras.config.disable_traceback_filtering()`\u001b[39;00m\n\u001b[1;32m--> 122\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m e\u001b[38;5;241m.\u001b[39mwith_traceback(filtered_tb) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m 123\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[0;32m 124\u001b[0m \u001b[38;5;28;01mdel\u001b[39;00m filtered_tb\n", + "File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\tensorflow\\python\\eager\\execute.py:53\u001b[0m, in \u001b[0;36mquick_execute\u001b[1;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[0;32m 51\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m 52\u001b[0m ctx\u001b[38;5;241m.\u001b[39mensure_initialized()\n\u001b[1;32m---> 53\u001b[0m tensors \u001b[38;5;241m=\u001b[39m pywrap_tfe\u001b[38;5;241m.\u001b[39mTFE_Py_Execute(ctx\u001b[38;5;241m.\u001b[39m_handle, device_name, op_name,\n\u001b[0;32m 54\u001b[0m inputs, attrs, num_outputs)\n\u001b[0;32m 55\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m core\u001b[38;5;241m.\u001b[39m_NotOkStatusException \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m 56\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m name \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", + "\u001b[1;31mInvalidArgumentError\u001b[0m: Graph execution error:\n\nDetected at node sequential_5_1/time_distributed_4_1/Reshape_9 defined at (most recent call last):\n File \"\", line 198, in _run_module_as_main\n\n File \"\", line 88, in _run_code\n\n File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\ipykernel_launcher.py\", line 18, in \n\n File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\traitlets\\config\\application.py\", line 1075, in launch_instance\n\n File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\ipykernel\\kernelapp.py\", line 739, in start\n\n File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\tornado\\platform\\asyncio.py\", line 205, in start\n\n File \"C:\\Program Files\\WindowsApps\\PythonSoftwareFoundation.Python.3.12_3.12.752.0_x64__qbz5n2kfra8p0\\Lib\\asyncio\\base_events.py\", line 639, in run_forever\n\n File \"C:\\Program Files\\WindowsApps\\PythonSoftwareFoundation.Python.3.12_3.12.752.0_x64__qbz5n2kfra8p0\\Lib\\asyncio\\base_events.py\", line 1985, in _run_once\n\n File \"C:\\Program Files\\WindowsApps\\PythonSoftwareFoundation.Python.3.12_3.12.752.0_x64__qbz5n2kfra8p0\\Lib\\asyncio\\events.py\", line 88, in _run\n\n File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\ipykernel\\kernelbase.py\", line 545, in dispatch_queue\n\n File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\ipykernel\\kernelbase.py\", line 534, in process_one\n\n File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\ipykernel\\kernelbase.py\", line 437, in dispatch_shell\n\n File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\ipykernel\\ipkernel.py\", line 362, in execute_request\n\n File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\ipykernel\\kernelbase.py\", line 778, in execute_request\n\n File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\ipykernel\\ipkernel.py\", line 449, in do_execute\n\n File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\ipykernel\\zmqshell.py\", line 549, in run_cell\n\n File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\IPython\\core\\interactiveshell.py\", line 3075, in run_cell\n\n File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\IPython\\core\\interactiveshell.py\", line 3130, in _run_cell\n\n File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\IPython\\core\\async_helpers.py\", line 129, in _pseudo_sync_runner\n\n File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\IPython\\core\\interactiveshell.py\", line 3334, in run_cell_async\n\n File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\IPython\\core\\interactiveshell.py\", line 3517, in run_ast_nodes\n\n File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\IPython\\core\\interactiveshell.py\", line 3577, in run_code\n\n File \"C:\\Users\\lhdto\\AppData\\Local\\Temp\\ipykernel_15232\\3536403359.py\", line 1, in \n\n File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 117, in error_handler\n\n File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\backend\\tensorflow\\trainer.py\", line 329, in fit\n\n File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\backend\\tensorflow\\trainer.py\", line 122, in one_step_on_iterator\n\n File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\backend\\tensorflow\\trainer.py\", line 110, in one_step_on_data\n\n File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\backend\\tensorflow\\trainer.py\", line 57, in train_step\n\n File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 117, in error_handler\n\n File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\layers\\layer.py\", line 825, in __call__\n\n File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 117, in error_handler\n\n File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\ops\\operation.py\", line 48, in __call__\n\n File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 156, in error_handler\n\n File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\models\\sequential.py\", line 202, in call\n\n File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\models\\functional.py\", line 195, in call\n\n File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\ops\\function.py\", line 151, in _run_through_graph\n\n File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\models\\functional.py\", line 579, in call\n\n File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 117, in error_handler\n\n File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\layers\\layer.py\", line 825, in __call__\n\n File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 117, in error_handler\n\n File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\ops\\operation.py\", line 48, in __call__\n\n File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 156, in error_handler\n\n File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\layers\\rnn\\time_distributed.py\", line 108, in call\n\n File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\layers\\rnn\\time_distributed.py\", line 102, in step_function\n\n File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\layers\\reshaping\\flatten.py\", line 54, in call\n\n File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\ops\\numpy.py\", line 4522, in reshape\n\n File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\backend\\tensorflow\\numpy.py\", line 1618, in reshape\n\nOnly one input size may be -1, not both 0 and 1\n\t [[{{node sequential_5_1/time_distributed_4_1/Reshape_9}}]] [Op:__inference_one_step_on_iterator_65940]" ] } ], @@ -3621,8 +3344,8 @@ "cell_type": "markdown", "id": "fa8ee94b-89f7-4733-8a0c-a86f86ff590a", "metadata": { - "tags": [], - "id": "fa8ee94b-89f7-4733-8a0c-a86f86ff590a" + "id": "fa8ee94b-89f7-4733-8a0c-a86f86ff590a", + "tags": [] }, "source": [ "# 5. Make a Prediction" @@ -3630,13 +3353,40 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 130, "id": "01fa7204-ce0e-49a8-8dbd-14fe5dfead40", "metadata": { - "tags": [], - "id": "01fa7204-ce0e-49a8-8dbd-14fe5dfead40" + "id": "01fa7204-ce0e-49a8-8dbd-14fe5dfead40", + "tags": [] }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Downloading...\n", + "From (original): https://drive.google.com/uc?id=1vWscXs4Vt0a_1IH1-ct2TCgXAZT-N3_Y\n", + "From (redirected): https://drive.google.com/uc?id=1vWscXs4Vt0a_1IH1-ct2TCgXAZT-N3_Y&confirm=t&uuid=7ea61f38-3bfe-46da-ac12-a2a31000febd\n", + "To: c:\\haidun.li\\NSCC\\LipNet\\checkpoints.zip\n", + "100%|██████████| 94.5M/94.5M [00:02<00:00, 41.0MB/s]\n" + ] + }, + { + "data": { + "text/plain": [ + "['models\\\\checkpoint.index',\n", + " 'models\\\\__MACOSX/._checkpoint.index',\n", + " 'models\\\\checkpoint.data-00000-of-00001',\n", + " 'models\\\\__MACOSX/._checkpoint.data-00000-of-00001',\n", + " 'models\\\\checkpoint',\n", + " 'models\\\\__MACOSX/._checkpoint']" + ] + }, + "execution_count": 130, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "url = 'https://drive.google.com/uc?id=1vWscXs4Vt0a_1IH1-ct2TCgXAZT-N3_Y'\n", "output = 'checkpoints.zip'\n", @@ -3646,13 +3396,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 131, "id": "247f664d-3c87-4e96-946e-930dad0e1c2c", "metadata": { - "tags": [], - "id": "247f664d-3c87-4e96-946e-930dad0e1c2c" + "id": "247f664d-3c87-4e96-946e-930dad0e1c2c", + "tags": [] }, - "outputs": [], + "outputs": [ + { + "ename": "ValueError", + "evalue": "File format not supported: filepath=models/checkpoint. Keras 3 only supports V3 `.keras` and `.weights.h5` files, or legacy V1/V2 `.h5` files.", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[131], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mload_weights\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mmodels/checkpoint\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n", + "File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\utils\\traceback_utils.py:122\u001b[0m, in \u001b[0;36mfilter_traceback..error_handler\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m 119\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n\u001b[0;32m 120\u001b[0m \u001b[38;5;66;03m# To get the full stack trace, call:\u001b[39;00m\n\u001b[0;32m 121\u001b[0m \u001b[38;5;66;03m# `keras.config.disable_traceback_filtering()`\u001b[39;00m\n\u001b[1;32m--> 122\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m e\u001b[38;5;241m.\u001b[39mwith_traceback(filtered_tb) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m 123\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[0;32m 124\u001b[0m \u001b[38;5;28;01mdel\u001b[39;00m filtered_tb\n", + "File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\saving\\saving_api.py:260\u001b[0m, in \u001b[0;36mload_weights\u001b[1;34m(model, filepath, skip_mismatch, **kwargs)\u001b[0m\n\u001b[0;32m 258\u001b[0m legacy_h5_format\u001b[38;5;241m.\u001b[39mload_weights_from_hdf5_group(f, model)\n\u001b[0;32m 259\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 260\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m 261\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFile format not supported: filepath=\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfilepath\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m. \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 262\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mKeras 3 only supports V3 `.keras` and `.weights.h5` \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 263\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfiles, or legacy V1/V2 `.h5` files.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 264\u001b[0m )\n", + "\u001b[1;31mValueError\u001b[0m: File format not supported: filepath=models/checkpoint. Keras 3 only supports V3 `.keras` and `.weights.h5` files, or legacy V1/V2 `.h5` files." + ] + } + ], "source": [ "model.load_weights('models/checkpoint')" ] @@ -3662,8 +3426,8 @@ "execution_count": null, "id": "7f8d689f-b7bb-443c-9b88-e40c1d800828", "metadata": { - "tags": [], - "id": "7f8d689f-b7bb-443c-9b88-e40c1d800828" + "id": "7f8d689f-b7bb-443c-9b88-e40c1d800828", + "tags": [] }, "outputs": [], "source": [ @@ -3675,8 +3439,8 @@ "execution_count": null, "id": "38546dc2-bee9-4837-864b-8a884df40ad7", "metadata": { - "tags": [], - "id": "38546dc2-bee9-4837-864b-8a884df40ad7" + "id": "38546dc2-bee9-4837-864b-8a884df40ad7", + "tags": [] }, "outputs": [], "source": [ @@ -3688,8 +3452,8 @@ "execution_count": null, "id": "a43621f0-229d-4c0d-9554-9c3a3da9c61a", "metadata": { - "tags": [], - "id": "a43621f0-229d-4c0d-9554-9c3a3da9c61a" + "id": "a43621f0-229d-4c0d-9554-9c3a3da9c61a", + "tags": [] }, "outputs": [], "source": [ @@ -3701,8 +3465,8 @@ "execution_count": null, "id": "ea462999-f87e-4a7e-a057-5be7b6d8f7d5", "metadata": { - "tags": [], - "id": "ea462999-f87e-4a7e-a057-5be7b6d8f7d5" + "id": "ea462999-f87e-4a7e-a057-5be7b6d8f7d5", + "tags": [] }, "outputs": [], "source": [ @@ -3715,8 +3479,8 @@ "execution_count": null, "id": "82bd4c10-dd6e-411e-834b-2a3b43fd12c5", "metadata": { - "tags": [], - "id": "82bd4c10-dd6e-411e-834b-2a3b43fd12c5" + "id": "82bd4c10-dd6e-411e-834b-2a3b43fd12c5", + "tags": [] }, "outputs": [], "source": [ @@ -3728,8 +3492,8 @@ "execution_count": null, "id": "5d68ac46-c90b-4eab-a709-f19aee569ff5", "metadata": { - "tags": [], - "id": "5d68ac46-c90b-4eab-a709-f19aee569ff5" + "id": "5d68ac46-c90b-4eab-a709-f19aee569ff5", + "tags": [] }, "outputs": [], "source": [ @@ -3831,20 +3595,29 @@ } ], "metadata": { + "accelerator": "GPU", + "colab": { + "gpuType": "T4", + "include_colab_link": true, + "provenance": [] + }, "kernelspec": { "display_name": "Python 3", "name": "python3" }, "language_info": { - "name": "python" - }, - "colab": { - "provenance": [], - "gpuType": "T4", - "include_colab_link": true - }, - "accelerator": "GPU" + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.2" + } }, "nbformat": 4, "nbformat_minor": 5 -} \ No newline at end of file +} diff --git a/app/modelutil.py b/app/modelutil.py index 0c8e8a7d..71a5d715 100644 --- a/app/modelutil.py +++ b/app/modelutil.py @@ -1,6 +1,8 @@ import os from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Conv3D, LSTM, Dense, Dropout, Bidirectional, MaxPool3D, Activation, Reshape, SpatialDropout3D, BatchNormalization, TimeDistributed, Flatten +from keras.layers import LSTM, Bidirectional +from keras.initializers import Orthogonal def load_model() -> Sequential: model = Sequential() @@ -19,10 +21,12 @@ def load_model() -> Sequential: model.add(TimeDistributed(Flatten())) - model.add(Bidirectional(LSTM(128, kernel_initializer='Orthogonal', return_sequences=True))) + # model.add(Bidirectional(LSTM(128, kernel_initializer='Orthogonal', return_sequences=True))) + model.add(Bidirectional(LSTM(128, kernel_initializer=Orthogonal(), return_sequences=True))) model.add(Dropout(.5)) - model.add(Bidirectional(LSTM(128, kernel_initializer='Orthogonal', return_sequences=True))) + # model.add(Bidirectional(LSTM(128, kernel_initializer='Orthogonal', return_sequences=True))) + model.add(Bidirectional(LSTM(128, kernel_initializer=Orthogonal(), return_sequences=True))) model.add(Dropout(.5)) model.add(Dense(41, kernel_initializer='he_normal', activation='softmax')) diff --git a/app/streamlitapp.py b/app/streamlitapp.py index 3ce027c4..0381680b 100644 --- a/app/streamlitapp.py +++ b/app/streamlitapp.py @@ -1,48 +1,48 @@ # Import all of the dependencies import streamlit as st -import os -import imageio +import os +import imageio -import tensorflow as tf +import tensorflow as tf from utils import load_data, num_to_char from modelutil import load_model -# Set the layout to the streamlit app as wide +# Set the layout to the streamlit app as wide st.set_page_config(layout='wide') # Setup the sidebar -with st.sidebar: +with st.sidebar: st.image('https://www.onepointltd.com/wp-content/uploads/2020/03/inno2.png') st.title('LipBuddy') st.info('This application is originally developed from the LipNet deep learning model.') -st.title('LipNet Full Stack App') -# Generating a list of options or videos +st.title('LipNet Full Stack App') +# Generating a list of options or videos options = os.listdir(os.path.join('..', 'data', 's1')) selected_video = st.selectbox('Choose video', options) -# Generate two columns +# Generate two columns col1, col2 = st.columns(2) -if options: +if options: - # Rendering the video - with col1: + # Rendering the video + with col1: st.info('The video below displays the converted video in mp4 format') file_path = os.path.join('..','data','s1', selected_video) os.system(f'ffmpeg -i {file_path} -vcodec libx264 test_video.mp4 -y') # Rendering inside of the app - video = open('test_video.mp4', 'rb') - video_bytes = video.read() + video = open('test_video.mp4', 'rb') + video_bytes = video.read() st.video(video_bytes) - with col2: + with col2: st.info('This is all the machine learning model sees when making a prediction') video, annotations = load_data(tf.convert_to_tensor(file_path)) imageio.mimsave('animation.gif', video, fps=10) - st.image('animation.gif', width=400) + st.image('animation.gif', width=400) st.info('This is the output of the machine learning model as tokens') model = load_model() @@ -54,4 +54,4 @@ st.info('Decode the raw tokens into words') converted_prediction = tf.strings.reduce_join(num_to_char(decoder)).numpy().decode('utf-8') st.text(converted_prediction) - + From 746373862fa57c93113900b9a48b7f254b7c6499 Mon Sep 17 00:00:00 2001 From: "haidun.li" Date: Wed, 10 Apr 2024 03:26:54 -0300 Subject: [PATCH 04/17] Refactor modelutil.py: Import Keras layers and models, update load_model function --- .gitignore | 177 +++++++ LipNet.ipynb | 1283 +++++++++++++++++++++++---------------------- animation.gif | Bin 0 -> 448311 bytes app/animation.gif | Bin 442479 -> 430386 bytes app/modelutil.py | 24 +- 5 files changed, 850 insertions(+), 634 deletions(-) create mode 100644 .gitignore create mode 100644 animation.gif diff --git a/.gitignore b/.gitignore new file mode 100644 index 00000000..7a957989 --- /dev/null +++ b/.gitignore @@ -0,0 +1,177 @@ +.vscode +node_modules +dist +yarn-error.log +yarn.lock +package-lock.json +**/__pycache__/ +**/*.pyc +**/*.pyo +**/*.pyd +.DS_Store + +# LipNet +data/ +models/ + + +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ +cover/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +.pybuilder/ +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# IPython +profile_default/ +ipython_config.py + +# pyenv +# For a library or package, you might want to ignore these files since the code is +# intended to run in multiple environments; otherwise, check them in: +# .python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +#Pipfile.lock + +# poetry +# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. +# This is especially recommended for binary packages to ensure reproducibility, and is more +# commonly ignored for libraries. +# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control +#poetry.lock + +# pdm +# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control. +#pdm.lock +# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it +# in version control. +# https://pdm.fming.dev/#use-with-ide +.pdm.toml + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm +__pypackages__/ + +# Celery stuff +celerybeat-schedule +celerybeat.pid + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ + +# pytype static type analyzer +.pytype/ + +# Cython debug symbols +cython_debug/ + +# PyCharm +# JetBrains specific template is maintained in a separate JetBrains.gitignore that can +# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore +# and can be added to the global gitignore or merged into this file. For a more nuclear +# option (not recommended) you can uncomment the following to ignore the entire idea folder. +#.idea/ diff --git a/LipNet.ipynb b/LipNet.ipynb index a43422cc..58d4a872 100644 --- a/LipNet.ipynb +++ b/LipNet.ipynb @@ -23,7 +23,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 1, "id": "ddfbccbe-41ae-4c23-98b1-a13868e2b499", "metadata": { "colab": { @@ -154,7 +154,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 2, "id": "02f907ea-f669-46c7-adcf-7f257e663448", "metadata": { "colab": { @@ -234,7 +234,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 3, "id": "AIsiftVmlrwN", "metadata": { "colab": { @@ -261,7 +261,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 4, "id": "b24af50c-20b8-409d-ad78-30a933fdd669", "metadata": { "id": "b24af50c-20b8-409d-ad78-30a933fdd669", @@ -280,7 +280,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 5, "id": "1e3db0b0-e559-4ad6-91fd-e7414b7d75e6", "metadata": { "colab": { @@ -296,7 +296,7 @@ "[]" ] }, - "execution_count": 45, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -307,7 +307,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 6, "id": "378d045a-3003-4f93-b7d2-a25a97774a68", "metadata": { "id": "378d045a-3003-4f93-b7d2-a25a97774a68", @@ -335,7 +335,7 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 7, "id": "8fb99c90-e05a-437f-839d-6e772f8c1dd5", "metadata": { "id": "8fb99c90-e05a-437f-839d-6e772f8c1dd5", @@ -348,7 +348,7 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 8, "id": "c019e4c6-2af3-4160-99ea-5c8cb009f1a7", "metadata": { "colab": { @@ -365,9 +365,9 @@ "text": [ "Downloading...\n", "From (original): https://drive.google.com/uc?id=1YlvpDLix3S-U8fd-gqRwPcWXAXm8JwjL\n", - "From (redirected): https://drive.google.com/uc?id=1YlvpDLix3S-U8fd-gqRwPcWXAXm8JwjL&confirm=t&uuid=70f5926c-96a4-40bd-a64d-77761b61a3b6\n", + "From (redirected): https://drive.google.com/uc?id=1YlvpDLix3S-U8fd-gqRwPcWXAXm8JwjL&confirm=t&uuid=2e632692-442f-4cf0-9357-ba800700a97e\n", "To: c:\\haidun.li\\NSCC\\LipNet\\data.zip\n", - "100%|██████████| 423M/423M [00:10<00:00, 41.3MB/s] \n" + "100%|██████████| 423M/423M [00:10<00:00, 41.1MB/s] \n" ] }, { @@ -1376,7 +1376,7 @@ " ...]" ] }, - "execution_count": 48, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -1390,7 +1390,7 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 9, "id": "8548cc59-6dfc-4acc-abc3-3e65212db02e", "metadata": { "id": "8548cc59-6dfc-4acc-abc3-3e65212db02e", @@ -1415,7 +1415,7 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 10, "id": "ec735e0b-ec98-4eb0-8f49-c35527d6670a", "metadata": { "id": "ec735e0b-ec98-4eb0-8f49-c35527d6670a", @@ -1428,7 +1428,7 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 11, "id": "be04e972-d7a5-4a72-82d8-a6bdde1f3ce6", "metadata": { "colab": { @@ -1461,7 +1461,7 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 12, "id": "559f7420-6802-45fa-9ca0-b1ff209b461c", "metadata": { "colab": { @@ -1517,7 +1517,7 @@ " ' ']" ] }, - "execution_count": 52, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -1528,7 +1528,7 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 13, "id": "797ff78b-b48f-4e14-bb62-8cd0ebf9501a", "metadata": { "colab": { @@ -1545,7 +1545,7 @@ "" ] }, - "execution_count": 53, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -1556,7 +1556,7 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": 14, "id": "8cd7f4f4-ae77-4509-a4f4-c723787ebad1", "metadata": { "colab": { @@ -1572,7 +1572,7 @@ "" ] }, - "execution_count": 54, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -1583,7 +1583,7 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 15, "id": "9491bab5-6a3c-4f79-879a-8f9fbe73ae2e", "metadata": { "id": "9491bab5-6a3c-4f79-879a-8f9fbe73ae2e", @@ -1604,7 +1604,7 @@ }, { "cell_type": "code", - "execution_count": 69, + "execution_count": 16, "id": "dd01ca9f-77fb-4643-a2aa-47dd82c5d66b", "metadata": { "id": "dd01ca9f-77fb-4643-a2aa-47dd82c5d66b", @@ -1627,7 +1627,7 @@ }, { "cell_type": "code", - "execution_count": 73, + "execution_count": 17, "id": "8cb7cc58-31ae-4904-a805-1177a82717d2", "metadata": { "id": "8cb7cc58-31ae-4904-a805-1177a82717d2", @@ -1641,7 +1641,7 @@ }, { "cell_type": "code", - "execution_count": 74, + "execution_count": 18, "id": "76aa964f-0c84-490d-897a-d00e3966e2c9", "metadata": { "colab": { @@ -1658,7 +1658,7 @@ "''" ] }, - "execution_count": 74, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -1670,7 +1670,7 @@ }, { "cell_type": "code", - "execution_count": 75, + "execution_count": 19, "id": "eb602c71-8560-4f9e-b26b-08202febb937", "metadata": { "id": "eb602c71-8560-4f9e-b26b-08202febb937", @@ -1684,7 +1684,7 @@ }, { "cell_type": "code", - "execution_count": 76, + "execution_count": 20, "id": "0e3184a1-6b02-4b4f-84a8-a0a65f951ea2", "metadata": { "colab": { @@ -1698,10 +1698,10 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 76, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" }, @@ -1722,7 +1722,7 @@ }, { "cell_type": "code", - "execution_count": 77, + "execution_count": 21, "id": "d7ec0833-d54b-4073-84cf-92d011c60ec1", "metadata": { "colab": { @@ -1740,7 +1740,7 @@ " 39, 14, 15, 23], dtype=int64)>" ] }, - "execution_count": 77, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -1751,7 +1751,7 @@ }, { "cell_type": "code", - "execution_count": 78, + "execution_count": 22, "id": "fe1ad370-b287-4b46-85a2-7c45b0bd9b10", "metadata": { "colab": { @@ -1767,7 +1767,7 @@ "" ] }, - "execution_count": 78, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -1778,7 +1778,7 @@ }, { "cell_type": "code", - "execution_count": 79, + "execution_count": 23, "id": "6871031a-b0ba-4c76-a852-f6329b0f2606", "metadata": { "id": "6871031a-b0ba-4c76-a852-f6329b0f2606", @@ -1804,7 +1804,7 @@ }, { "cell_type": "code", - "execution_count": 80, + "execution_count": 24, "id": "7686355d-45aa-4c85-ad9c-053e6a9b4d81", "metadata": { "id": "7686355d-45aa-4c85-ad9c-053e6a9b4d81", @@ -1817,7 +1817,7 @@ }, { "cell_type": "code", - "execution_count": 81, + "execution_count": 25, "id": "f066fea2-91b1-42ed-a67d-00566a1a53ff", "metadata": { "id": "f066fea2-91b1-42ed-a67d-00566a1a53ff", @@ -1837,7 +1837,7 @@ }, { "cell_type": "code", - "execution_count": 82, + "execution_count": 26, "id": "6b1365bd-7742-41d1-95d4-247021751c3a", "metadata": { "colab": { @@ -1853,7 +1853,7 @@ "50" ] }, - "execution_count": 82, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" } @@ -1864,7 +1864,7 @@ }, { "cell_type": "code", - "execution_count": 83, + "execution_count": 27, "id": "5281bde8-fdc8-4da1-bd55-5a7929a9e80c", "metadata": { "id": "5281bde8-fdc8-4da1-bd55-5a7929a9e80c" @@ -1876,7 +1876,7 @@ }, { "cell_type": "code", - "execution_count": 84, + "execution_count": 28, "id": "cbebe683-6afd-47fd-bba4-c83b4b13bb32", "metadata": { "colab": { @@ -1892,7 +1892,7 @@ "2" ] }, - "execution_count": 84, + "execution_count": 28, "metadata": {}, "output_type": "execute_result" } @@ -1903,7 +1903,7 @@ }, { "cell_type": "code", - 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" [[ 1.146332 ],\n", - " [ 1.1053916 ],\n", - " [ 1.0644511 ],\n", + " [[ 0.8738314 ],\n", + " [ 0.8738314 ],\n", + " [ 0.8738314 ],\n", " ...,\n", - " [10.439809 ],\n", - " [10.439809 ],\n", - " [10.439809 ]],\n", + " [10.049061 ],\n", + " [10.049061 ],\n", + " [10.009341 ]],\n", "\n", - " [[ 1.1053916 ],\n", - " [ 1.1053916 ],\n", - " [ 1.0644511 ],\n", + " [[ 0.8738314 ],\n", + " [ 0.8738314 ],\n", + " [ 0.8738314 ],\n", " ...,\n", - " [10.439809 ],\n", - " [10.439809 ],\n", - " [10.439809 ]],\n", + " [10.049061 ],\n", + " [10.009341 ],\n", + " [10.009341 ]],\n", "\n", - " [[ 1.1053916 ],\n", - " [ 1.1053916 ],\n", - " [ 1.0644511 ],\n", + " [[ 0.8738314 ],\n", + " [ 0.8738314 ],\n", + " [ 0.8738314 ],\n", " ...,\n", - " [10.439809 ],\n", - " [10.439809 ],\n", - " [10.398869 ]]]]], dtype=float32)" + " [10.009341 ],\n", + " [10.009341 ],\n", + " [10.009341 ]]]]], dtype=float32)" ] }, - "execution_count": 86, + "execution_count": 30, "metadata": {}, "output_type": "execute_result" } @@ -2561,7 +2561,7 @@ }, { "cell_type": "code", - "execution_count": 87, + "execution_count": 31, "id": "acf5eb4f-a0da-4a9a-bf24-af13e9cc2fbe", "metadata": { "colab": { @@ -2576,87 +2576,87 @@ "name": "stderr", "output_type": "stream", "text": [ - "Lossy conversion from float32 to uint8. Range [0.0, 10.607527732849121]. Convert image to uint8 prior to saving to suppress this warning.\n", - "Lossy conversion from float32 to uint8. Range [0.0, 10.607527732849121]. Convert image to uint8 prior to saving to suppress this warning.\n", - "Lossy conversion from float32 to uint8. Range [0.0, 10.607527732849121]. Convert image to uint8 prior to saving to suppress this warning.\n", - "Lossy conversion from float32 to uint8. Range [0.0, 10.607527732849121]. Convert image to uint8 prior to saving to suppress this warning.\n", - "Lossy conversion from float32 to uint8. Range [0.0, 10.607527732849121]. Convert image to uint8 prior to saving to suppress this warning.\n", - "Lossy conversion from float32 to uint8. Range [0.0, 10.607527732849121]. Convert image to uint8 prior to saving to suppress this warning.\n", - "Lossy conversion from float32 to uint8. Range [0.0, 10.607527732849121]. Convert image to uint8 prior to saving to suppress this warning.\n", - "Lossy conversion from float32 to uint8. Range [0.0, 10.607527732849121]. Convert image to uint8 prior to saving to suppress this warning.\n", - "Lossy conversion from float32 to uint8. Range [0.0, 10.607527732849121]. Convert image to uint8 prior to saving to suppress this warning.\n", - "Lossy conversion from float32 to uint8. Range [0.0, 10.607527732849121]. Convert image to uint8 prior to saving to suppress this warning.\n", - "Lossy conversion from float32 to uint8. Range [0.0, 10.607527732849121]. Convert image to uint8 prior to saving to suppress this warning.\n", - "Lossy conversion from float32 to uint8. Range [0.0, 10.607527732849121]. Convert image to uint8 prior to saving to suppress this warning.\n", - "Lossy conversion from float32 to uint8. Range [0.0, 10.607527732849121]. Convert image to uint8 prior to saving to suppress this warning.\n", - "Lossy conversion from float32 to uint8. Range [0.0, 10.607527732849121]. Convert image to uint8 prior to saving to suppress this warning.\n", - "Lossy conversion from float32 to uint8. Range [0.0, 10.607527732849121]. Convert image to uint8 prior to saving to suppress this warning.\n", - "Lossy conversion from float32 to uint8. Range [0.0, 10.607527732849121]. Convert image to uint8 prior to saving to suppress this warning.\n", - "Lossy conversion from float32 to uint8. Range [0.0, 10.607527732849121]. Convert image to uint8 prior to saving to suppress this warning.\n", - "Lossy conversion from float32 to uint8. Range [0.0, 10.607527732849121]. Convert image to uint8 prior to saving to suppress this warning.\n", - "Lossy conversion from float32 to uint8. Range [0.0, 10.607527732849121]. Convert image to uint8 prior to saving to suppress this warning.\n", - "Lossy conversion from float32 to uint8. Range [0.0, 10.607527732849121]. Convert image to uint8 prior to saving to suppress this warning.\n", - "Lossy conversion from float32 to uint8. Range [0.0, 10.607527732849121]. Convert image to uint8 prior to saving to suppress this warning.\n", - "Lossy conversion from float32 to uint8. Range [0.0, 10.607527732849121]. Convert image to uint8 prior to saving to suppress this warning.\n", - "Lossy conversion from float32 to uint8. Range [0.0, 10.607527732849121]. Convert image to uint8 prior to saving to suppress this warning.\n", - "Lossy conversion from float32 to uint8. Range [0.0, 10.607527732849121]. Convert image to uint8 prior to saving to suppress this warning.\n" + "Lossy conversion from float32 to uint8. Range [0.0, 9.344405174255371]. Convert image to uint8 prior to saving to suppress this warning.\n", + "Lossy conversion from float32 to uint8. Range [0.0, 9.344405174255371]. Convert image to uint8 prior to saving to suppress this warning.\n", + "Lossy conversion from float32 to uint8. Range [0.0, 9.344405174255371]. Convert image to uint8 prior to saving to suppress this warning.\n", + "Lossy conversion from float32 to uint8. Range [0.0, 9.344405174255371]. Convert image to uint8 prior to saving to suppress this warning.\n", + "Lossy conversion from float32 to uint8. Range [0.0, 9.344405174255371]. Convert image to uint8 prior to saving to suppress this warning.\n", + "Lossy conversion from float32 to uint8. Range [0.0, 9.344405174255371]. Convert image to uint8 prior to saving to suppress this warning.\n", + "Lossy conversion from float32 to uint8. Range [0.0, 9.344405174255371]. Convert image to uint8 prior to saving to suppress this warning.\n", + "Lossy conversion from float32 to uint8. Range [0.0, 9.344405174255371]. Convert image to uint8 prior to saving to suppress this warning.\n", + "Lossy conversion from float32 to uint8. Range [0.0, 9.344405174255371]. Convert image to uint8 prior to saving to suppress this warning.\n", + "Lossy conversion from float32 to uint8. Range [0.0, 9.344405174255371]. Convert image to uint8 prior to saving to suppress this warning.\n", + "Lossy conversion from float32 to uint8. Range [0.0, 9.344405174255371]. Convert image to uint8 prior to saving to suppress this warning.\n", + "Lossy conversion from float32 to uint8. Range [0.0, 9.344405174255371]. Convert image to uint8 prior to saving to suppress this warning.\n", + "Lossy conversion from float32 to uint8. Range [0.0, 9.344405174255371]. Convert image to uint8 prior to saving to suppress this warning.\n", + "Lossy conversion from float32 to uint8. Range [0.0, 9.344405174255371]. Convert image to uint8 prior to saving to suppress this warning.\n", + "Lossy conversion from float32 to uint8. Range [0.0, 9.344405174255371]. Convert image to uint8 prior to saving to suppress this warning.\n", + "Lossy conversion from float32 to uint8. Range [0.0, 9.344405174255371]. Convert image to uint8 prior to saving to suppress this warning.\n", + "Lossy conversion from float32 to uint8. Range [0.0, 9.344405174255371]. Convert image to uint8 prior to saving to suppress this warning.\n", + "Lossy conversion from float32 to uint8. Range [0.0, 9.344405174255371]. Convert image to uint8 prior to saving to suppress this warning.\n", + "Lossy conversion from float32 to uint8. Range [0.0, 9.344405174255371]. Convert image to uint8 prior to saving to suppress this warning.\n", + "Lossy conversion from float32 to uint8. Range [0.0, 9.344405174255371]. Convert image to uint8 prior to saving to suppress this warning.\n", + "Lossy conversion from float32 to uint8. Range [0.0, 9.344405174255371]. Convert image to uint8 prior to saving to suppress this warning.\n", + "Lossy conversion from float32 to uint8. Range [0.0, 9.344405174255371]. Convert image to uint8 prior to saving to suppress this warning.\n", + "Lossy conversion from float32 to uint8. Range [0.0, 9.344405174255371]. Convert image to uint8 prior to saving to suppress this warning.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "Lossy conversion from float32 to uint8. Range [0.0, 10.607527732849121]. Convert image to uint8 prior to saving to suppress this warning.\n", - "Lossy conversion from float32 to uint8. Range [0.0, 10.607527732849121]. Convert image to uint8 prior to saving to suppress this warning.\n", - "Lossy conversion from float32 to uint8. Range [0.0, 10.607527732849121]. Convert image to uint8 prior to saving to suppress this warning.\n", - "Lossy conversion from float32 to uint8. Range [0.0, 10.607527732849121]. Convert image to uint8 prior to saving to suppress this warning.\n", - "Lossy conversion from float32 to uint8. Range [0.0, 10.607527732849121]. Convert image to uint8 prior to saving to suppress this warning.\n", - "Lossy conversion from float32 to uint8. Range [0.0, 10.607527732849121]. Convert image to uint8 prior to saving to suppress this warning.\n", - "Lossy conversion from float32 to uint8. Range [0.0, 10.607527732849121]. Convert image to uint8 prior to saving to suppress this warning.\n", - "Lossy conversion from float32 to uint8. Range [0.0, 10.607527732849121]. Convert image to uint8 prior to saving to suppress this warning.\n", - "Lossy conversion from float32 to uint8. Range [0.0, 10.607527732849121]. Convert image to uint8 prior to saving to suppress this warning.\n", - "Lossy conversion from float32 to uint8. Range [0.0, 10.607527732849121]. Convert image to uint8 prior to saving to suppress this warning.\n", - "Lossy conversion from float32 to uint8. Range [0.0, 10.607527732849121]. Convert image to uint8 prior to saving to suppress this warning.\n", - "Lossy conversion from float32 to uint8. Range [0.0, 10.607527732849121]. Convert image to uint8 prior to saving to suppress this warning.\n", - "Lossy conversion from float32 to uint8. Range [0.0, 10.607527732849121]. Convert image to uint8 prior to saving to suppress this warning.\n", - "Lossy conversion from float32 to uint8. Range [0.0, 10.607527732849121]. Convert image to uint8 prior to saving to suppress this warning.\n", - "Lossy conversion from float32 to uint8. Range [0.0, 10.607527732849121]. Convert image to uint8 prior to saving to suppress this warning.\n", - "Lossy conversion from float32 to uint8. Range [0.0, 10.607527732849121]. Convert image to uint8 prior to saving to suppress this warning.\n", - "Lossy conversion from float32 to uint8. Range [0.0, 10.607527732849121]. Convert image to uint8 prior to saving to suppress this warning.\n", - "Lossy conversion from float32 to uint8. Range [0.0, 10.607527732849121]. Convert image to uint8 prior to saving to suppress this warning.\n", - "Lossy conversion from float32 to uint8. Range [0.0, 10.607527732849121]. Convert image to uint8 prior to saving to suppress this warning.\n", - "Lossy conversion from float32 to uint8. Range [0.0, 10.607527732849121]. Convert image to uint8 prior to saving to suppress this warning.\n", - "Lossy conversion from float32 to uint8. Range [0.0, 10.607527732849121]. Convert image to uint8 prior to saving to suppress this warning.\n", - "Lossy conversion from float32 to uint8. Range [0.0, 10.607527732849121]. Convert image to uint8 prior to saving to suppress this warning.\n", - "Lossy conversion from float32 to uint8. Range [0.0, 10.607527732849121]. Convert image to uint8 prior to saving to suppress this warning.\n", - "Lossy conversion from float32 to uint8. Range [0.0, 10.607527732849121]. Convert image to uint8 prior to saving to suppress this warning.\n", - "Lossy conversion from float32 to uint8. Range [0.0, 10.607527732849121]. Convert image to uint8 prior to saving to suppress this warning.\n", - "Lossy conversion from float32 to uint8. Range [0.0, 10.607527732849121]. Convert image to uint8 prior to saving to suppress this warning.\n", - "Lossy conversion from float32 to uint8. Range [0.0, 10.607527732849121]. Convert image to uint8 prior to saving to suppress this warning.\n", - "Lossy conversion from float32 to uint8. Range [0.0, 10.607527732849121]. Convert image to uint8 prior to saving to suppress this warning.\n", - "Lossy conversion from float32 to uint8. Range [0.0, 10.607527732849121]. Convert image to uint8 prior to saving to suppress this warning.\n", - "Lossy conversion from float32 to uint8. Range [0.0, 10.607527732849121]. Convert image to uint8 prior to saving to suppress this warning.\n", - "Lossy conversion from float32 to uint8. Range [0.0, 10.607527732849121]. Convert image to uint8 prior to saving to suppress this warning.\n", - "Lossy conversion from float32 to uint8. Range [0.0, 10.607527732849121]. Convert image to uint8 prior to saving to suppress this warning.\n", - "Lossy conversion from float32 to uint8. Range [0.0, 10.607527732849121]. Convert image to uint8 prior to saving to suppress this warning.\n", - "Lossy conversion from float32 to uint8. Range [0.0, 10.607527732849121]. Convert image to uint8 prior to saving to suppress this warning.\n", - "Lossy conversion from float32 to uint8. Range [0.0, 10.607527732849121]. Convert image to uint8 prior to saving to suppress this warning.\n", - "Lossy conversion from float32 to uint8. Range [0.0, 10.607527732849121]. Convert image to uint8 prior to saving to suppress this warning.\n", - "Lossy conversion from float32 to uint8. Range [0.0, 10.607527732849121]. Convert image to uint8 prior to saving to suppress this warning.\n", - "Lossy conversion from float32 to uint8. Range [0.0, 10.607527732849121]. Convert image to uint8 prior to saving to suppress this warning.\n", - "Lossy conversion from float32 to uint8. Range [0.0, 10.607527732849121]. Convert image to uint8 prior to saving to suppress this warning.\n", - "Lossy conversion from float32 to uint8. Range [0.0, 10.607527732849121]. Convert image to uint8 prior to saving to suppress this warning.\n", - "Lossy conversion from float32 to uint8. Range [0.0, 10.607527732849121]. Convert image to uint8 prior to saving to suppress this warning.\n", - "Lossy conversion from float32 to uint8. Range [0.0, 10.607527732849121]. Convert image to uint8 prior to saving to suppress this warning.\n", - "Lossy conversion from float32 to uint8. Range [0.0, 10.607527732849121]. Convert image to uint8 prior to saving to suppress this warning.\n", - "Lossy conversion from float32 to uint8. Range [0.0, 10.607527732849121]. Convert image to uint8 prior to saving to suppress this warning.\n", - "Lossy conversion from float32 to uint8. Range [0.0, 10.607527732849121]. Convert image to uint8 prior to saving to suppress this warning.\n", - "Lossy conversion from float32 to uint8. Range [0.0, 10.607527732849121]. Convert image to uint8 prior to saving to suppress this warning.\n", - "Lossy conversion from float32 to uint8. Range [0.0, 10.607527732849121]. Convert image to uint8 prior to saving to suppress this warning.\n", - "Lossy conversion from float32 to uint8. Range [0.0, 10.607527732849121]. Convert image to uint8 prior to saving to suppress this warning.\n", - "Lossy conversion from float32 to uint8. Range [0.0, 10.607527732849121]. Convert image to uint8 prior to saving to suppress this warning.\n", - "Lossy conversion from float32 to uint8. Range [0.0, 10.607527732849121]. Convert image to uint8 prior to saving to suppress this warning.\n", - "Lossy conversion from float32 to uint8. Range [0.0, 10.607527732849121]. Convert image to uint8 prior to saving to suppress this warning.\n" + "Lossy conversion from float32 to uint8. Range [0.0, 9.344405174255371]. Convert image to uint8 prior to saving to suppress this warning.\n", + "Lossy conversion from float32 to uint8. Range [0.0, 9.344405174255371]. Convert image to uint8 prior to saving to suppress this warning.\n", + "Lossy conversion from float32 to uint8. Range [0.0, 9.344405174255371]. Convert image to uint8 prior to saving to suppress this warning.\n", + "Lossy conversion from float32 to uint8. Range [0.0, 9.344405174255371]. Convert image to uint8 prior to saving to suppress this warning.\n", + "Lossy conversion from float32 to uint8. Range [0.0, 9.344405174255371]. Convert image to uint8 prior to saving to suppress this warning.\n", + "Lossy conversion from float32 to uint8. Range [0.0, 9.344405174255371]. Convert image to uint8 prior to saving to suppress this warning.\n", + "Lossy conversion from float32 to uint8. Range [0.0, 9.344405174255371]. Convert image to uint8 prior to saving to suppress this warning.\n", + "Lossy conversion from float32 to uint8. Range [0.0, 9.344405174255371]. Convert image to uint8 prior to saving to suppress this warning.\n", + "Lossy conversion from float32 to uint8. Range [0.0, 9.344405174255371]. Convert image to uint8 prior to saving to suppress this warning.\n", + "Lossy conversion from float32 to uint8. Range [0.0, 9.344405174255371]. Convert image to uint8 prior to saving to suppress this warning.\n", + "Lossy conversion from float32 to uint8. Range [0.0, 9.344405174255371]. Convert image to uint8 prior to saving to suppress this warning.\n", + "Lossy conversion from float32 to uint8. Range [0.0, 9.344405174255371]. Convert image to uint8 prior to saving to suppress this warning.\n", + "Lossy conversion from float32 to uint8. Range [0.0, 9.344405174255371]. Convert image to uint8 prior to saving to suppress this warning.\n", + "Lossy conversion from float32 to uint8. Range [0.0, 9.344405174255371]. Convert image to uint8 prior to saving to suppress this warning.\n", + "Lossy conversion from float32 to uint8. Range [0.0, 9.344405174255371]. Convert image to uint8 prior to saving to suppress this warning.\n", + "Lossy conversion from float32 to uint8. Range [0.0, 9.344405174255371]. Convert image to uint8 prior to saving to suppress this warning.\n", + "Lossy conversion from float32 to uint8. Range [0.0, 9.344405174255371]. Convert image to uint8 prior to saving to suppress this warning.\n", + "Lossy conversion from float32 to uint8. Range [0.0, 9.344405174255371]. Convert image to uint8 prior to saving to suppress this warning.\n", + "Lossy conversion from float32 to uint8. Range [0.0, 9.344405174255371]. Convert image to uint8 prior to saving to suppress this warning.\n", + "Lossy conversion from float32 to uint8. Range [0.0, 9.344405174255371]. Convert image to uint8 prior to saving to suppress this warning.\n", + "Lossy conversion from float32 to uint8. Range [0.0, 9.344405174255371]. Convert image to uint8 prior to saving to suppress this warning.\n", + "Lossy conversion from float32 to uint8. Range [0.0, 9.344405174255371]. Convert image to uint8 prior to saving to suppress this warning.\n", + "Lossy conversion from float32 to uint8. Range [0.0, 9.344405174255371]. Convert image to uint8 prior to saving to suppress this warning.\n", + "Lossy conversion from float32 to uint8. Range [0.0, 9.344405174255371]. Convert image to uint8 prior to saving to suppress this warning.\n", + "Lossy conversion from float32 to uint8. Range [0.0, 9.344405174255371]. Convert image to uint8 prior to saving to suppress this warning.\n", + "Lossy conversion from float32 to uint8. Range [0.0, 9.344405174255371]. Convert image to uint8 prior to saving to suppress this warning.\n", + "Lossy conversion from float32 to uint8. Range [0.0, 9.344405174255371]. Convert image to uint8 prior to saving to suppress this warning.\n", + "Lossy conversion from float32 to uint8. Range [0.0, 9.344405174255371]. Convert image to uint8 prior to saving to suppress this warning.\n", + "Lossy conversion from float32 to uint8. Range [0.0, 9.344405174255371]. Convert image to uint8 prior to saving to suppress this warning.\n", + "Lossy conversion from float32 to uint8. Range [0.0, 9.344405174255371]. Convert image to uint8 prior to saving to suppress this warning.\n", + "Lossy conversion from float32 to uint8. Range [0.0, 9.344405174255371]. Convert image to uint8 prior to saving to suppress this warning.\n", + "Lossy conversion from float32 to uint8. Range [0.0, 9.344405174255371]. Convert image to uint8 prior to saving to suppress this warning.\n", + "Lossy conversion from float32 to uint8. Range [0.0, 9.344405174255371]. Convert image to uint8 prior to saving to suppress this warning.\n", + "Lossy conversion from float32 to uint8. Range [0.0, 9.344405174255371]. Convert image to uint8 prior to saving to suppress this warning.\n", + "Lossy conversion from float32 to uint8. Range [0.0, 9.344405174255371]. Convert image to uint8 prior to saving to suppress this warning.\n", + "Lossy conversion from float32 to uint8. Range [0.0, 9.344405174255371]. Convert image to uint8 prior to saving to suppress this warning.\n", + "Lossy conversion from float32 to uint8. Range [0.0, 9.344405174255371]. Convert image to uint8 prior to saving to suppress this warning.\n", + "Lossy conversion from float32 to uint8. Range [0.0, 9.344405174255371]. Convert image to uint8 prior to saving to suppress this warning.\n", + "Lossy conversion from float32 to uint8. Range [0.0, 9.344405174255371]. Convert image to uint8 prior to saving to suppress this warning.\n", + "Lossy conversion from float32 to uint8. Range [0.0, 9.344405174255371]. Convert image to uint8 prior to saving to suppress this warning.\n", + "Lossy conversion from float32 to uint8. Range [0.0, 9.344405174255371]. Convert image to uint8 prior to saving to suppress this warning.\n", + "Lossy conversion from float32 to uint8. Range [0.0, 9.344405174255371]. Convert image to uint8 prior to saving to suppress this warning.\n", + "Lossy conversion from float32 to uint8. Range [0.0, 9.344405174255371]. Convert image to uint8 prior to saving to suppress this warning.\n", + "Lossy conversion from float32 to uint8. Range [0.0, 9.344405174255371]. Convert image to uint8 prior to saving to suppress this warning.\n", + "Lossy conversion from float32 to uint8. Range [0.0, 9.344405174255371]. Convert image to uint8 prior to saving to suppress this warning.\n", + "Lossy conversion from float32 to uint8. Range [0.0, 9.344405174255371]. Convert image to uint8 prior to saving to suppress this warning.\n", + "Lossy conversion from float32 to uint8. Range [0.0, 9.344405174255371]. Convert image to uint8 prior to saving to suppress this warning.\n", + "Lossy conversion from float32 to uint8. Range [0.0, 9.344405174255371]. Convert image to uint8 prior to saving to suppress this warning.\n", + "Lossy conversion from float32 to uint8. Range [0.0, 9.344405174255371]. Convert image to uint8 prior to saving to suppress this warning.\n", + "Lossy conversion from float32 to uint8. Range [0.0, 9.344405174255371]. Convert image to uint8 prior to saving to suppress this warning.\n", + "Lossy conversion from float32 to uint8. Range [0.0, 9.344405174255371]. Convert image to uint8 prior to saving to suppress this warning.\n", + "Lossy conversion from float32 to uint8. Range [0.0, 9.344405174255371]. Convert image to uint8 prior to saving to suppress this warning.\n" ] } ], @@ -2666,7 +2666,7 @@ }, { "cell_type": "code", - "execution_count": 88, + "execution_count": 32, "id": "c33a87a2-d5e0-4ec9-b174-73ebf41bf03a", "metadata": { "colab": { @@ -2681,16 +2681,16 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 88, + "execution_count": 32, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -2706,7 +2706,7 @@ }, { "cell_type": "code", - "execution_count": 89, + "execution_count": 33, "id": "84593332-133c-4205-b7a6-8e235d5e2b3b", "metadata": { "colab": { @@ -2720,10 +2720,10 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 89, + "execution_count": 33, "metadata": {}, "output_type": "execute_result" } @@ -2745,7 +2745,7 @@ }, { "cell_type": "code", - "execution_count": 90, + "execution_count": 34, "id": "d8e9a497-191b-4842-afbd-26f5e13c43ba", "metadata": { "id": "d8e9a497-191b-4842-afbd-26f5e13c43ba", @@ -2761,7 +2761,7 @@ }, { "cell_type": "code", - "execution_count": 93, + "execution_count": 35, "id": "5d376ebe", "metadata": {}, "outputs": [], @@ -2772,7 +2772,7 @@ }, { "cell_type": "code", - "execution_count": 94, + "execution_count": 36, "id": "3f753ed2-70b9-4236-8c1c-08ca065dc8bf", "metadata": { "colab": { @@ -2788,7 +2788,7 @@ "(75, 46, 140, 1)" ] }, - "execution_count": 94, + "execution_count": 36, "metadata": {}, "output_type": "execute_result" } @@ -2799,13 +2799,22 @@ }, { "cell_type": "code", - "execution_count": 114, + "execution_count": 37, "id": "f9171056-a352-491a-9ed9-92b28ced268e", "metadata": { "id": "f9171056-a352-491a-9ed9-92b28ced268e", "tags": [] }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\layers\\convolutional\\base_conv.py:99: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n", + " super().__init__(\n" + ] + } + ], "source": [ "model = Sequential()\n", "model.add(Conv3D(128, 3, input_shape=(75,46,140,1), padding='same'))\n", @@ -2838,7 +2847,7 @@ }, { "cell_type": "code", - "execution_count": 115, + "execution_count": 38, "id": "78851825-2bcd-42a9-b7f2-28bb5a6bf43a", "metadata": { "colab": { @@ -2856,11 +2865,11 @@ { "data": { "text/html": [ - "
Model: \"sequential_5\"\n",
+              "
Model: \"sequential\"\n",
               "
\n"
             ],
             "text/plain": [
-              "\u001b[1mModel: \"sequential_5\"\u001b[0m\n"
+              "\u001b[1mModel: \"sequential\"\u001b[0m\n"
             ]
           },
           "metadata": {},
@@ -2872,42 +2881,42 @@
               "
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
               "┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃\n",
               "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
-              "│ conv3d_15 (Conv3D)              │ (None, 75, 46, 140,    │         3,584 │\n",
+              "│ conv3d (Conv3D)                 │ (None, 75, 46, 140,    │         3,584 │\n",
               "│                                 │ 128)                   │               │\n",
               "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-              "│ activation_15 (Activation)      │ (None, 75, 46, 140,    │             0 │\n",
+              "│ activation (Activation)         │ (None, 75, 46, 140,    │             0 │\n",
               "│                                 │ 128)                   │               │\n",
               "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-              "│ max_pooling3d_15 (MaxPooling3D) │ (None, 75, 23, 70,     │             0 │\n",
+              "│ max_pooling3d (MaxPooling3D)    │ (None, 75, 23, 70,     │             0 │\n",
               "│                                 │ 128)                   │               │\n",
               "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-              "│ conv3d_16 (Conv3D)              │ (None, 75, 23, 70,     │       884,992 │\n",
+              "│ conv3d_1 (Conv3D)               │ (None, 75, 23, 70,     │       884,992 │\n",
               "│                                 │ 256)                   │               │\n",
               "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-              "│ activation_16 (Activation)      │ (None, 75, 23, 70,     │             0 │\n",
+              "│ activation_1 (Activation)       │ (None, 75, 23, 70,     │             0 │\n",
               "│                                 │ 256)                   │               │\n",
               "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-              "│ max_pooling3d_16 (MaxPooling3D) │ (None, 75, 11, 35,     │             0 │\n",
+              "│ max_pooling3d_1 (MaxPooling3D)  │ (None, 75, 11, 35,     │             0 │\n",
               "│                                 │ 256)                   │               │\n",
               "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-              "│ conv3d_17 (Conv3D)              │ (None, 75, 11, 35, 75) │       518,475 │\n",
+              "│ conv3d_2 (Conv3D)               │ (None, 75, 11, 35, 75) │       518,475 │\n",
               "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-              "│ activation_17 (Activation)      │ (None, 75, 11, 35, 75) │             0 │\n",
+              "│ activation_2 (Activation)       │ (None, 75, 11, 35, 75) │             0 │\n",
               "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-              "│ max_pooling3d_17 (MaxPooling3D) │ (None, 75, 5, 17, 75)  │             0 │\n",
+              "│ max_pooling3d_2 (MaxPooling3D)  │ (None, 75, 5, 17, 75)  │             0 │\n",
               "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-              "│ time_distributed_4              │ (None, 75, 6375)       │             0 │\n",
+              "│ time_distributed                │ (None, 75, 6375)       │             0 │\n",
               "│ (TimeDistributed)               │                        │               │\n",
               "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-              "│ bidirectional_3 (Bidirectional) │ (None, 75, 256)        │     6,660,096 │\n",
+              "│ bidirectional (Bidirectional)   │ (None, 75, 256)        │     6,660,096 │\n",
               "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-              "│ dropout_3 (Dropout)             │ (None, 75, 256)        │             0 │\n",
+              "│ dropout (Dropout)               │ (None, 75, 256)        │             0 │\n",
               "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-              "│ bidirectional_4 (Bidirectional) │ (None, 75, 256)        │       394,240 │\n",
+              "│ bidirectional_1 (Bidirectional) │ (None, 75, 256)        │       394,240 │\n",
               "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-              "│ dropout_4 (Dropout)             │ (None, 75, 256)        │             0 │\n",
+              "│ dropout_1 (Dropout)             │ (None, 75, 256)        │             0 │\n",
               "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-              "│ dense_1 (Dense)                 │ (None, 75, 41)         │        10,537 │\n",
+              "│ dense (Dense)                   │ (None, 75, 41)         │        10,537 │\n",
               "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
               "
\n"
             ],
@@ -2915,42 +2924,42 @@
               "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
               "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
               "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
-              "│ conv3d_15 (\u001b[38;5;33mConv3D\u001b[0m)              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m46\u001b[0m, \u001b[38;5;34m140\u001b[0m,    │         \u001b[38;5;34m3,584\u001b[0m │\n",
+              "│ conv3d (\u001b[38;5;33mConv3D\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m46\u001b[0m, \u001b[38;5;34m140\u001b[0m,    │         \u001b[38;5;34m3,584\u001b[0m │\n",
               "│                                 │ \u001b[38;5;34m128\u001b[0m)                   │               │\n",
               "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-              "│ activation_15 (\u001b[38;5;33mActivation\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m46\u001b[0m, \u001b[38;5;34m140\u001b[0m,    │             \u001b[38;5;34m0\u001b[0m │\n",
+              "│ activation (\u001b[38;5;33mActivation\u001b[0m)         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m46\u001b[0m, \u001b[38;5;34m140\u001b[0m,    │             \u001b[38;5;34m0\u001b[0m │\n",
               "│                                 │ \u001b[38;5;34m128\u001b[0m)                   │               │\n",
               "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-              "│ max_pooling3d_15 (\u001b[38;5;33mMaxPooling3D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m23\u001b[0m, \u001b[38;5;34m70\u001b[0m,     │             \u001b[38;5;34m0\u001b[0m │\n",
+              "│ max_pooling3d (\u001b[38;5;33mMaxPooling3D\u001b[0m)    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m23\u001b[0m, \u001b[38;5;34m70\u001b[0m,     │             \u001b[38;5;34m0\u001b[0m │\n",
               "│                                 │ \u001b[38;5;34m128\u001b[0m)                   │               │\n",
               "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-              "│ conv3d_16 (\u001b[38;5;33mConv3D\u001b[0m)              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m23\u001b[0m, \u001b[38;5;34m70\u001b[0m,     │       \u001b[38;5;34m884,992\u001b[0m │\n",
+              "│ conv3d_1 (\u001b[38;5;33mConv3D\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m23\u001b[0m, \u001b[38;5;34m70\u001b[0m,     │       \u001b[38;5;34m884,992\u001b[0m │\n",
               "│                                 │ \u001b[38;5;34m256\u001b[0m)                   │               │\n",
               "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-              "│ activation_16 (\u001b[38;5;33mActivation\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m23\u001b[0m, \u001b[38;5;34m70\u001b[0m,     │             \u001b[38;5;34m0\u001b[0m │\n",
+              "│ activation_1 (\u001b[38;5;33mActivation\u001b[0m)       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m23\u001b[0m, \u001b[38;5;34m70\u001b[0m,     │             \u001b[38;5;34m0\u001b[0m │\n",
               "│                                 │ \u001b[38;5;34m256\u001b[0m)                   │               │\n",
               "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-              "│ max_pooling3d_16 (\u001b[38;5;33mMaxPooling3D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m11\u001b[0m, \u001b[38;5;34m35\u001b[0m,     │             \u001b[38;5;34m0\u001b[0m │\n",
+              "│ max_pooling3d_1 (\u001b[38;5;33mMaxPooling3D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m11\u001b[0m, \u001b[38;5;34m35\u001b[0m,     │             \u001b[38;5;34m0\u001b[0m │\n",
               "│                                 │ \u001b[38;5;34m256\u001b[0m)                   │               │\n",
               "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-              "│ conv3d_17 (\u001b[38;5;33mConv3D\u001b[0m)              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m11\u001b[0m, \u001b[38;5;34m35\u001b[0m, \u001b[38;5;34m75\u001b[0m) │       \u001b[38;5;34m518,475\u001b[0m │\n",
+              "│ conv3d_2 (\u001b[38;5;33mConv3D\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m11\u001b[0m, \u001b[38;5;34m35\u001b[0m, \u001b[38;5;34m75\u001b[0m) │       \u001b[38;5;34m518,475\u001b[0m │\n",
               "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-              "│ activation_17 (\u001b[38;5;33mActivation\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m11\u001b[0m, \u001b[38;5;34m35\u001b[0m, \u001b[38;5;34m75\u001b[0m) │             \u001b[38;5;34m0\u001b[0m │\n",
+              "│ activation_2 (\u001b[38;5;33mActivation\u001b[0m)       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m11\u001b[0m, \u001b[38;5;34m35\u001b[0m, \u001b[38;5;34m75\u001b[0m) │             \u001b[38;5;34m0\u001b[0m │\n",
               "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-              "│ max_pooling3d_17 (\u001b[38;5;33mMaxPooling3D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m17\u001b[0m, \u001b[38;5;34m75\u001b[0m)  │             \u001b[38;5;34m0\u001b[0m │\n",
+              "│ max_pooling3d_2 (\u001b[38;5;33mMaxPooling3D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m17\u001b[0m, \u001b[38;5;34m75\u001b[0m)  │             \u001b[38;5;34m0\u001b[0m │\n",
               "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-              "│ time_distributed_4              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m6375\u001b[0m)       │             \u001b[38;5;34m0\u001b[0m │\n",
+              "│ time_distributed                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m6375\u001b[0m)       │             \u001b[38;5;34m0\u001b[0m │\n",
               "│ (\u001b[38;5;33mTimeDistributed\u001b[0m)               │                        │               │\n",
               "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-              "│ bidirectional_3 (\u001b[38;5;33mBidirectional\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m256\u001b[0m)        │     \u001b[38;5;34m6,660,096\u001b[0m │\n",
+              "│ bidirectional (\u001b[38;5;33mBidirectional\u001b[0m)   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m256\u001b[0m)        │     \u001b[38;5;34m6,660,096\u001b[0m │\n",
               "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-              "│ dropout_3 (\u001b[38;5;33mDropout\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m256\u001b[0m)        │             \u001b[38;5;34m0\u001b[0m │\n",
+              "│ dropout (\u001b[38;5;33mDropout\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m256\u001b[0m)        │             \u001b[38;5;34m0\u001b[0m │\n",
               "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-              "│ bidirectional_4 (\u001b[38;5;33mBidirectional\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m256\u001b[0m)        │       \u001b[38;5;34m394,240\u001b[0m │\n",
+              "│ bidirectional_1 (\u001b[38;5;33mBidirectional\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m256\u001b[0m)        │       \u001b[38;5;34m394,240\u001b[0m │\n",
               "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-              "│ dropout_4 (\u001b[38;5;33mDropout\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m256\u001b[0m)        │             \u001b[38;5;34m0\u001b[0m │\n",
+              "│ dropout_1 (\u001b[38;5;33mDropout\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m256\u001b[0m)        │             \u001b[38;5;34m0\u001b[0m │\n",
               "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-              "│ dense_1 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m41\u001b[0m)         │        \u001b[38;5;34m10,537\u001b[0m │\n",
+              "│ dense (\u001b[38;5;33mDense\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m41\u001b[0m)         │        \u001b[38;5;34m10,537\u001b[0m │\n",
               "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
             ]
           },
@@ -3003,7 +3012,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 116,
+      "execution_count": 39,
       "id": "f4b4798c-a65a-4c47-9e2a-3b09dc98d320",
       "metadata": {
         "colab": {
@@ -3019,7 +3028,7 @@
               "6375"
             ]
           },
-          "execution_count": 116,
+          "execution_count": 39,
           "metadata": {},
           "output_type": "execute_result"
         }
@@ -3030,7 +3039,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 117,
+      "execution_count": 40,
       "id": "e5c2eae0-c359-41a4-97a0-75c44dccb7d1",
       "metadata": {
         "colab": {
@@ -3045,7 +3054,7 @@
           "name": "stdout",
           "output_type": "stream",
           "text": [
-            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 3s/step\n"
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 4s/step\n"
           ]
         }
       ],
@@ -3055,7 +3064,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 118,
+      "execution_count": 41,
       "id": "ffdc7319-0d69-4f7e-a6d4-ce72deb81c0b",
       "metadata": {
         "colab": {
@@ -3068,10 +3077,10 @@
         {
           "data": {
             "text/plain": [
-              ""
+              ""
             ]
           },
-          "execution_count": 118,
+          "execution_count": 41,
           "metadata": {},
           "output_type": "execute_result"
         }
@@ -3082,7 +3091,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 119,
+      "execution_count": 42,
       "id": "6ed47531-8317-4255-9a12-b757642258e6",
       "metadata": {
         "colab": {
@@ -3096,10 +3105,10 @@
         {
           "data": {
             "text/plain": [
-              ""
+              ""
             ]
           },
-          "execution_count": 119,
+          "execution_count": 42,
           "metadata": {},
           "output_type": "execute_result"
         }
@@ -3110,7 +3119,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 120,
+      "execution_count": 43,
       "id": "7c37b9b9-5298-4038-9c33-5031d1b457f0",
       "metadata": {
         "colab": {
@@ -3127,7 +3136,7 @@
               "(None, 75, 46, 140, 1)"
             ]
           },
-          "execution_count": 120,
+          "execution_count": 43,
           "metadata": {},
           "output_type": "execute_result"
         }
@@ -3138,7 +3147,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 121,
+      "execution_count": 44,
       "id": "98b316a4-5322-4782-8e36-4b3c1a696d85",
       "metadata": {
         "colab": {
@@ -3155,7 +3164,7 @@
               "(None, 75, 41)"
             ]
           },
-          "execution_count": 121,
+          "execution_count": 44,
           "metadata": {},
           "output_type": "execute_result"
         }
@@ -3177,7 +3186,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 122,
+      "execution_count": 45,
       "id": "ab015fd0-7fb4-4d5d-9fa2-30a05dbd515a",
       "metadata": {
         "id": "ab015fd0-7fb4-4d5d-9fa2-30a05dbd515a",
@@ -3194,7 +3203,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 123,
+      "execution_count": 46,
       "id": "c564d5c9-db54-4e88-b311-9aeab7fb3e69",
       "metadata": {
         "id": "c564d5c9-db54-4e88-b311-9aeab7fb3e69",
@@ -3216,7 +3225,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 124,
+      "execution_count": 47,
       "id": "a26dc3fc-a19c-4378-bd8c-e2b597a1d15c",
       "metadata": {
         "id": "a26dc3fc-a19c-4378-bd8c-e2b597a1d15c",
@@ -3240,7 +3249,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 125,
+      "execution_count": 48,
       "id": "04be90d8-2482-46f9-b513-d5f4f8001c7e",
       "metadata": {
         "id": "04be90d8-2482-46f9-b513-d5f4f8001c7e",
@@ -3253,7 +3262,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 126,
+      "execution_count": 49,
       "id": "eab49367-3f1e-4464-ae76-dbd07549d97e",
       "metadata": {
         "id": "eab49367-3f1e-4464-ae76-dbd07549d97e",
@@ -3278,7 +3287,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 127,
+      "execution_count": 50,
       "id": "e085a632-d464-46ef-8777-959cad4adb2c",
       "metadata": {
         "id": "e085a632-d464-46ef-8777-959cad4adb2c",
@@ -3291,7 +3300,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 128,
+      "execution_count": 51,
       "id": "48eca991-90ab-4592-8a79-b50e9ca015b6",
       "metadata": {
         "id": "48eca991-90ab-4592-8a79-b50e9ca015b6",
@@ -3304,7 +3313,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 129,
+      "execution_count": 52,
       "id": "8ffba483-aa61-4bbe-a15f-a73e1ddf097c",
       "metadata": {
         "colab": {
@@ -3322,17 +3331,25 @@
             "Epoch 1/100\n"
           ]
         },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "WARNING:tensorflow:From C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\legacy\\backend.py:666: The name tf.nn.ctc_loss is deprecated. Please use tf.compat.v1.nn.ctc_loss instead.\n",
+            "\n"
+          ]
+        },
         {
           "ename": "InvalidArgumentError",
-          "evalue": "Graph execution error:\n\nDetected at node sequential_5_1/time_distributed_4_1/Reshape_9 defined at (most recent call last):\n  File \"\", line 198, in _run_module_as_main\n\n  File \"\", line 88, in _run_code\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\ipykernel_launcher.py\", line 18, in \n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\traitlets\\config\\application.py\", line 1075, in launch_instance\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\ipykernel\\kernelapp.py\", line 739, in start\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\tornado\\platform\\asyncio.py\", line 205, in start\n\n  File \"C:\\Program Files\\WindowsApps\\PythonSoftwareFoundation.Python.3.12_3.12.752.0_x64__qbz5n2kfra8p0\\Lib\\asyncio\\base_events.py\", line 639, in run_forever\n\n  File \"C:\\Program Files\\WindowsApps\\PythonSoftwareFoundation.Python.3.12_3.12.752.0_x64__qbz5n2kfra8p0\\Lib\\asyncio\\base_events.py\", line 1985, in _run_once\n\n  File \"C:\\Program Files\\WindowsApps\\PythonSoftwareFoundation.Python.3.12_3.12.752.0_x64__qbz5n2kfra8p0\\Lib\\asyncio\\events.py\", line 88, in _run\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\ipykernel\\kernelbase.py\", line 545, in dispatch_queue\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\ipykernel\\kernelbase.py\", line 534, in process_one\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\ipykernel\\kernelbase.py\", line 437, in dispatch_shell\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\ipykernel\\ipkernel.py\", line 362, in execute_request\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\ipykernel\\kernelbase.py\", line 778, in execute_request\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\ipykernel\\ipkernel.py\", line 449, in do_execute\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\ipykernel\\zmqshell.py\", line 549, in run_cell\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\IPython\\core\\interactiveshell.py\", line 3075, in run_cell\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\IPython\\core\\interactiveshell.py\", line 3130, in _run_cell\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\IPython\\core\\async_helpers.py\", line 129, in _pseudo_sync_runner\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\IPython\\core\\interactiveshell.py\", line 3334, in run_cell_async\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\IPython\\core\\interactiveshell.py\", line 3517, in run_ast_nodes\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\IPython\\core\\interactiveshell.py\", line 3577, in run_code\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Temp\\ipykernel_15232\\3536403359.py\", line 1, in \n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 117, in error_handler\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\backend\\tensorflow\\trainer.py\", line 329, in fit\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\backend\\tensorflow\\trainer.py\", line 122, in one_step_on_iterator\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\backend\\tensorflow\\trainer.py\", line 110, in one_step_on_data\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\backend\\tensorflow\\trainer.py\", line 57, in train_step\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 117, in error_handler\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\layers\\layer.py\", line 825, in __call__\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 117, in error_handler\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\ops\\operation.py\", line 48, in __call__\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 156, in error_handler\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\models\\sequential.py\", line 202, in call\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\models\\functional.py\", line 195, in call\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\ops\\function.py\", line 151, in _run_through_graph\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\models\\functional.py\", line 579, in call\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 117, in error_handler\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\layers\\layer.py\", line 825, in __call__\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 117, in error_handler\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\ops\\operation.py\", line 48, in __call__\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 156, in error_handler\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\layers\\rnn\\time_distributed.py\", line 108, in call\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\layers\\rnn\\time_distributed.py\", line 102, in step_function\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\layers\\reshaping\\flatten.py\", line 54, in call\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\ops\\numpy.py\", line 4522, in reshape\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\backend\\tensorflow\\numpy.py\", line 1618, in reshape\n\nOnly one input size may be -1, not both 0 and 1\n\t [[{{node sequential_5_1/time_distributed_4_1/Reshape_9}}]] [Op:__inference_one_step_on_iterator_65940]",
+          "evalue": "Graph execution error:\n\nDetected at node sequential_1/time_distributed_1/Reshape_50 defined at (most recent call last):\n  File \"\", line 198, in _run_module_as_main\n\n  File \"\", line 88, in _run_code\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\ipykernel_launcher.py\", line 18, in \n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\traitlets\\config\\application.py\", line 1075, in launch_instance\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\ipykernel\\kernelapp.py\", line 739, in start\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\tornado\\platform\\asyncio.py\", line 205, in start\n\n  File \"C:\\Program Files\\WindowsApps\\PythonSoftwareFoundation.Python.3.12_3.12.752.0_x64__qbz5n2kfra8p0\\Lib\\asyncio\\base_events.py\", line 639, in run_forever\n\n  File \"C:\\Program Files\\WindowsApps\\PythonSoftwareFoundation.Python.3.12_3.12.752.0_x64__qbz5n2kfra8p0\\Lib\\asyncio\\base_events.py\", line 1985, in _run_once\n\n  File \"C:\\Program Files\\WindowsApps\\PythonSoftwareFoundation.Python.3.12_3.12.752.0_x64__qbz5n2kfra8p0\\Lib\\asyncio\\events.py\", line 88, in _run\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\ipykernel\\kernelbase.py\", line 545, in dispatch_queue\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\ipykernel\\kernelbase.py\", line 534, in process_one\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\ipykernel\\kernelbase.py\", line 437, in dispatch_shell\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\ipykernel\\ipkernel.py\", line 362, in execute_request\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\ipykernel\\kernelbase.py\", line 778, in execute_request\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\ipykernel\\ipkernel.py\", line 449, in do_execute\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\ipykernel\\zmqshell.py\", line 549, in run_cell\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\IPython\\core\\interactiveshell.py\", line 3075, in run_cell\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\IPython\\core\\interactiveshell.py\", line 3130, in _run_cell\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\IPython\\core\\async_helpers.py\", line 129, in _pseudo_sync_runner\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\IPython\\core\\interactiveshell.py\", line 3334, in run_cell_async\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\IPython\\core\\interactiveshell.py\", line 3517, in run_ast_nodes\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\IPython\\core\\interactiveshell.py\", line 3577, in run_code\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Temp\\ipykernel_10024\\3536403359.py\", line 1, in \n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 117, in error_handler\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\backend\\tensorflow\\trainer.py\", line 329, in fit\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\backend\\tensorflow\\trainer.py\", line 122, in one_step_on_iterator\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\backend\\tensorflow\\trainer.py\", line 110, in one_step_on_data\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\backend\\tensorflow\\trainer.py\", line 57, in train_step\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 117, in error_handler\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\layers\\layer.py\", line 825, in __call__\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 117, in error_handler\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\ops\\operation.py\", line 48, in __call__\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 156, in error_handler\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\models\\sequential.py\", line 202, in call\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\models\\functional.py\", line 195, in call\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\ops\\function.py\", line 151, in _run_through_graph\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\models\\functional.py\", line 579, in call\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 117, in error_handler\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\layers\\layer.py\", line 825, in __call__\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 117, in error_handler\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\ops\\operation.py\", line 48, in __call__\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 156, in error_handler\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\layers\\rnn\\time_distributed.py\", line 108, in call\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\layers\\rnn\\time_distributed.py\", line 102, in step_function\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\layers\\reshaping\\flatten.py\", line 54, in call\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\ops\\numpy.py\", line 4522, in reshape\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\backend\\tensorflow\\numpy.py\", line 1618, in reshape\n\nOnly one input size may be -1, not both 0 and 1\n\t [[{{node sequential_1/time_distributed_1/Reshape_50}}]] [Op:__inference_one_step_on_iterator_31137]",
           "output_type": "error",
           "traceback": [
             "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
             "\u001b[1;31mInvalidArgumentError\u001b[0m                      Traceback (most recent call last)",
-            "Cell \u001b[1;32mIn[129], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtrain\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalidation_data\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtest\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mepochs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m100\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcallbacks\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[43mcheckpoint_callback\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mschedule_callback\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mexample_callback\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n",
+            "Cell \u001b[1;32mIn[52], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtrain\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalidation_data\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtest\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mepochs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m100\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcallbacks\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[43mcheckpoint_callback\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mschedule_callback\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mexample_callback\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n",
             "File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\utils\\traceback_utils.py:122\u001b[0m, in \u001b[0;36mfilter_traceback..error_handler\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    119\u001b[0m     filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n\u001b[0;32m    120\u001b[0m     \u001b[38;5;66;03m# To get the full stack trace, call:\u001b[39;00m\n\u001b[0;32m    121\u001b[0m     \u001b[38;5;66;03m# `keras.config.disable_traceback_filtering()`\u001b[39;00m\n\u001b[1;32m--> 122\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m e\u001b[38;5;241m.\u001b[39mwith_traceback(filtered_tb) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m    123\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[0;32m    124\u001b[0m     \u001b[38;5;28;01mdel\u001b[39;00m filtered_tb\n",
             "File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\tensorflow\\python\\eager\\execute.py:53\u001b[0m, in \u001b[0;36mquick_execute\u001b[1;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[0;32m     51\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m     52\u001b[0m   ctx\u001b[38;5;241m.\u001b[39mensure_initialized()\n\u001b[1;32m---> 53\u001b[0m   tensors \u001b[38;5;241m=\u001b[39m pywrap_tfe\u001b[38;5;241m.\u001b[39mTFE_Py_Execute(ctx\u001b[38;5;241m.\u001b[39m_handle, device_name, op_name,\n\u001b[0;32m     54\u001b[0m                                       inputs, attrs, num_outputs)\n\u001b[0;32m     55\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m core\u001b[38;5;241m.\u001b[39m_NotOkStatusException \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m     56\u001b[0m   \u001b[38;5;28;01mif\u001b[39;00m name \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
-            "\u001b[1;31mInvalidArgumentError\u001b[0m: Graph execution error:\n\nDetected at node sequential_5_1/time_distributed_4_1/Reshape_9 defined at (most recent call last):\n  File \"\", line 198, in _run_module_as_main\n\n  File \"\", line 88, in _run_code\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\ipykernel_launcher.py\", line 18, in \n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\traitlets\\config\\application.py\", line 1075, in launch_instance\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\ipykernel\\kernelapp.py\", line 739, in start\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\tornado\\platform\\asyncio.py\", line 205, in start\n\n  File \"C:\\Program Files\\WindowsApps\\PythonSoftwareFoundation.Python.3.12_3.12.752.0_x64__qbz5n2kfra8p0\\Lib\\asyncio\\base_events.py\", line 639, in run_forever\n\n  File \"C:\\Program Files\\WindowsApps\\PythonSoftwareFoundation.Python.3.12_3.12.752.0_x64__qbz5n2kfra8p0\\Lib\\asyncio\\base_events.py\", line 1985, in _run_once\n\n  File \"C:\\Program Files\\WindowsApps\\PythonSoftwareFoundation.Python.3.12_3.12.752.0_x64__qbz5n2kfra8p0\\Lib\\asyncio\\events.py\", line 88, in _run\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\ipykernel\\kernelbase.py\", line 545, in dispatch_queue\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\ipykernel\\kernelbase.py\", line 534, in process_one\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\ipykernel\\kernelbase.py\", line 437, in dispatch_shell\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\ipykernel\\ipkernel.py\", line 362, in execute_request\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\ipykernel\\kernelbase.py\", line 778, in execute_request\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\ipykernel\\ipkernel.py\", line 449, in do_execute\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\ipykernel\\zmqshell.py\", line 549, in run_cell\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\IPython\\core\\interactiveshell.py\", line 3075, in run_cell\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\IPython\\core\\interactiveshell.py\", line 3130, in _run_cell\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\IPython\\core\\async_helpers.py\", line 129, in _pseudo_sync_runner\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\IPython\\core\\interactiveshell.py\", line 3334, in run_cell_async\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\IPython\\core\\interactiveshell.py\", line 3517, in run_ast_nodes\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\IPython\\core\\interactiveshell.py\", line 3577, in run_code\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Temp\\ipykernel_15232\\3536403359.py\", line 1, in \n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 117, in error_handler\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\backend\\tensorflow\\trainer.py\", line 329, in fit\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\backend\\tensorflow\\trainer.py\", line 122, in one_step_on_iterator\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\backend\\tensorflow\\trainer.py\", line 110, in one_step_on_data\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\backend\\tensorflow\\trainer.py\", line 57, in train_step\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 117, in error_handler\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\layers\\layer.py\", line 825, in __call__\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 117, in error_handler\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\ops\\operation.py\", line 48, in __call__\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 156, in error_handler\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\models\\sequential.py\", line 202, in call\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\models\\functional.py\", line 195, in call\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\ops\\function.py\", line 151, in _run_through_graph\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\models\\functional.py\", line 579, in call\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 117, in error_handler\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\layers\\layer.py\", line 825, in __call__\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 117, in error_handler\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\ops\\operation.py\", line 48, in __call__\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 156, in error_handler\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\layers\\rnn\\time_distributed.py\", line 108, in call\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\layers\\rnn\\time_distributed.py\", line 102, in step_function\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\layers\\reshaping\\flatten.py\", line 54, in call\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\ops\\numpy.py\", line 4522, in reshape\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\backend\\tensorflow\\numpy.py\", line 1618, in reshape\n\nOnly one input size may be -1, not both 0 and 1\n\t [[{{node sequential_5_1/time_distributed_4_1/Reshape_9}}]] [Op:__inference_one_step_on_iterator_65940]"
+            "\u001b[1;31mInvalidArgumentError\u001b[0m: Graph execution error:\n\nDetected at node sequential_1/time_distributed_1/Reshape_50 defined at (most recent call last):\n  File \"\", line 198, in _run_module_as_main\n\n  File \"\", line 88, in _run_code\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\ipykernel_launcher.py\", line 18, in \n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\traitlets\\config\\application.py\", line 1075, in launch_instance\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\ipykernel\\kernelapp.py\", line 739, in start\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\tornado\\platform\\asyncio.py\", line 205, in start\n\n  File \"C:\\Program Files\\WindowsApps\\PythonSoftwareFoundation.Python.3.12_3.12.752.0_x64__qbz5n2kfra8p0\\Lib\\asyncio\\base_events.py\", line 639, in run_forever\n\n  File \"C:\\Program Files\\WindowsApps\\PythonSoftwareFoundation.Python.3.12_3.12.752.0_x64__qbz5n2kfra8p0\\Lib\\asyncio\\base_events.py\", line 1985, in _run_once\n\n  File \"C:\\Program Files\\WindowsApps\\PythonSoftwareFoundation.Python.3.12_3.12.752.0_x64__qbz5n2kfra8p0\\Lib\\asyncio\\events.py\", line 88, in _run\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\ipykernel\\kernelbase.py\", line 545, in dispatch_queue\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\ipykernel\\kernelbase.py\", line 534, in process_one\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\ipykernel\\kernelbase.py\", line 437, in dispatch_shell\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\ipykernel\\ipkernel.py\", line 362, in execute_request\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\ipykernel\\kernelbase.py\", line 778, in execute_request\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\ipykernel\\ipkernel.py\", line 449, in do_execute\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\ipykernel\\zmqshell.py\", line 549, in run_cell\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\IPython\\core\\interactiveshell.py\", line 3075, in run_cell\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\IPython\\core\\interactiveshell.py\", line 3130, in _run_cell\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\IPython\\core\\async_helpers.py\", line 129, in _pseudo_sync_runner\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\IPython\\core\\interactiveshell.py\", line 3334, in run_cell_async\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\IPython\\core\\interactiveshell.py\", line 3517, in run_ast_nodes\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\IPython\\core\\interactiveshell.py\", line 3577, in run_code\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Temp\\ipykernel_10024\\3536403359.py\", line 1, in \n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 117, in error_handler\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\backend\\tensorflow\\trainer.py\", line 329, in fit\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\backend\\tensorflow\\trainer.py\", line 122, in one_step_on_iterator\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\backend\\tensorflow\\trainer.py\", line 110, in one_step_on_data\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\backend\\tensorflow\\trainer.py\", line 57, in train_step\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 117, in error_handler\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\layers\\layer.py\", line 825, in __call__\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 117, in error_handler\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\ops\\operation.py\", line 48, in __call__\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 156, in error_handler\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\models\\sequential.py\", line 202, in call\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\models\\functional.py\", line 195, in call\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\ops\\function.py\", line 151, in _run_through_graph\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\models\\functional.py\", line 579, in call\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 117, in error_handler\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\layers\\layer.py\", line 825, in __call__\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 117, in error_handler\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\ops\\operation.py\", line 48, in __call__\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 156, in error_handler\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\layers\\rnn\\time_distributed.py\", line 108, in call\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\layers\\rnn\\time_distributed.py\", line 102, in step_function\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\layers\\reshaping\\flatten.py\", line 54, in call\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\ops\\numpy.py\", line 4522, in reshape\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\backend\\tensorflow\\numpy.py\", line 1618, in reshape\n\nOnly one input size may be -1, not both 0 and 1\n\t [[{{node sequential_1/time_distributed_1/Reshape_50}}]] [Op:__inference_one_step_on_iterator_31137]"
           ]
         }
       ],
@@ -3353,7 +3370,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 130,
+      "execution_count": 53,
       "id": "01fa7204-ce0e-49a8-8dbd-14fe5dfead40",
       "metadata": {
         "id": "01fa7204-ce0e-49a8-8dbd-14fe5dfead40",
@@ -3366,9 +3383,9 @@
           "text": [
             "Downloading...\n",
             "From (original): https://drive.google.com/uc?id=1vWscXs4Vt0a_1IH1-ct2TCgXAZT-N3_Y\n",
-            "From (redirected): https://drive.google.com/uc?id=1vWscXs4Vt0a_1IH1-ct2TCgXAZT-N3_Y&confirm=t&uuid=7ea61f38-3bfe-46da-ac12-a2a31000febd\n",
+            "From (redirected): https://drive.google.com/uc?id=1vWscXs4Vt0a_1IH1-ct2TCgXAZT-N3_Y&confirm=t&uuid=62e39af9-6b13-4dc5-b6b6-9e6a803e62e5\n",
             "To: c:\\haidun.li\\NSCC\\LipNet\\checkpoints.zip\n",
-            "100%|██████████| 94.5M/94.5M [00:02<00:00, 41.0MB/s]\n"
+            "100%|██████████| 94.5M/94.5M [00:02<00:00, 41.5MB/s]\n"
           ]
         },
         {
@@ -3382,7 +3399,7 @@
               " 'models\\\\__MACOSX/._checkpoint']"
             ]
           },
-          "execution_count": 130,
+          "execution_count": 53,
           "metadata": {},
           "output_type": "execute_result"
         }
@@ -3396,7 +3413,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 131,
+      "execution_count": 57,
       "id": "247f664d-3c87-4e96-946e-930dad0e1c2c",
       "metadata": {
         "id": "247f664d-3c87-4e96-946e-930dad0e1c2c",
@@ -3405,20 +3422,20 @@
       "outputs": [
         {
           "ename": "ValueError",
-          "evalue": "File format not supported: filepath=models/checkpoint. Keras 3 only supports V3 `.keras` and `.weights.h5` files, or legacy V1/V2 `.h5` files.",
+          "evalue": "File format not supported: filepath=./models/checkpoint. Keras 3 only supports V3 `.keras` and `.weights.h5` files, or legacy V1/V2 `.h5` files.",
           "output_type": "error",
           "traceback": [
             "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
             "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
-            "Cell \u001b[1;32mIn[131], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mload_weights\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mmodels/checkpoint\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n",
+            "Cell \u001b[1;32mIn[57], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mload_weights\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m./models/checkpoint\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n",
             "File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\utils\\traceback_utils.py:122\u001b[0m, in \u001b[0;36mfilter_traceback..error_handler\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    119\u001b[0m     filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n\u001b[0;32m    120\u001b[0m     \u001b[38;5;66;03m# To get the full stack trace, call:\u001b[39;00m\n\u001b[0;32m    121\u001b[0m     \u001b[38;5;66;03m# `keras.config.disable_traceback_filtering()`\u001b[39;00m\n\u001b[1;32m--> 122\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m e\u001b[38;5;241m.\u001b[39mwith_traceback(filtered_tb) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m    123\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[0;32m    124\u001b[0m     \u001b[38;5;28;01mdel\u001b[39;00m filtered_tb\n",
             "File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\saving\\saving_api.py:260\u001b[0m, in \u001b[0;36mload_weights\u001b[1;34m(model, filepath, skip_mismatch, **kwargs)\u001b[0m\n\u001b[0;32m    258\u001b[0m             legacy_h5_format\u001b[38;5;241m.\u001b[39mload_weights_from_hdf5_group(f, model)\n\u001b[0;32m    259\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 260\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m    261\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFile format not supported: filepath=\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfilepath\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m. \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    262\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mKeras 3 only supports V3 `.keras` and `.weights.h5` \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    263\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfiles, or legacy V1/V2 `.h5` files.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    264\u001b[0m     )\n",
-            "\u001b[1;31mValueError\u001b[0m: File format not supported: filepath=models/checkpoint. Keras 3 only supports V3 `.keras` and `.weights.h5` files, or legacy V1/V2 `.h5` files."
+            "\u001b[1;31mValueError\u001b[0m: File format not supported: filepath=./models/checkpoint. Keras 3 only supports V3 `.keras` and `.weights.h5` files, or legacy V1/V2 `.h5` files."
           ]
         }
       ],
       "source": [
-        "model.load_weights('models/checkpoint')"
+        "model.load_weights('./models/checkpoint')"
       ]
     },
     {
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z)FN4vG`y}qNl+{ln9-OC8(M$2G47JHDv#IBKTr4AJL|(2_
z<`(&SvUFGU{eH!U)%^+L+I;f!Q(Z~!*DWd~%R2meFB?5O31}0eYQ5>(pbJ4ccH7=>
zTzzi1=kPA~g7jwP6GY>V5eS#;htb>&kGD=d4F|o_dy-pVT%m7Oy_`9*w=J0UR-upmU4MRIv2Qlua8bi~#w(Anzlx|s+?sv2;$|?1|`N?0TP(2XFjZd;Z4uKQ2zO8{=>oFe%@i?
zx#kFXn)F>8U08uDxv+?WTbi9T#uL5@?rxsi)Avzin?jGF
z)?P12cbsyW@}TlduH=F>Vkk_Ebs=Jml|Zw2OTB#keu}}*+rxG9h-}jr|ClJA-&&i0
zi@PoG`in{qP8aVQ)lg9Q@(LN7zN5+QywF4EQo#i;pXqA_nG0^JV!ln&6mO(EvX0Du
z|Acb!SDG-=hz(STp(xk?)qIZ5^85HGb{ru;X-WzR>=igm%)Z
z*l?A!kYf+4`V#h8ob2y%T7JT Sequential:
     model = Sequential()
@@ -31,6 +34,25 @@ def load_model() -> Sequential:
 
     model.add(Dense(41, kernel_initializer='he_normal', activation='softmax'))
 
-    model.load_weights(os.path.join('..','models','checkpoint'))
+    # weights_path = os.path.join('..', 'models', 'checkpoint.h5')  # Adjust the extension as necessary
+    # # weights_path = 'C:/haidun.li/NSCC/LipNet/models/checkpoint'
+    # print('weights_path:', weights_path)
+    # if os.path.exists(weights_path):
+    #     model.load_weights(weights_path)
+    # else:
+    #     raise FileNotFoundError(f"Weight file {weights_path} does not exist.")
+
+    # return model
+
+     # Create a checkpoint instance that points to the folder where the checkpoints are saved
+    checkpoint_dir = os.path.join('..', 'models')
+    checkpoint = tf.train.Checkpoint(model=model)
+
+    # Restore the latest checkpoint
+    latest = tf.train.latest_checkpoint(checkpoint_dir)
+    if latest:
+        checkpoint.restore(latest)
+    else:
+        raise FileNotFoundError("No checkpoint found in {}".format(checkpoint_dir))
 
     return model
\ No newline at end of file

From dc64e4e31f72bceae287318ca9e97ed67046395e Mon Sep 17 00:00:00 2001
From: "haidun.li" 
Date: Wed, 10 Apr 2024 12:19:03 -0300
Subject: [PATCH 05/17] Refactor streamlitapp.py: Add new video file and
 comment out old video file

---
 LipNet.ipynb        | 2972 ++++++++++++++++---------------------------
 app/modelutil.py    |   44 +-
 app/streamlitapp.py |    1 +
 3 files changed, 1124 insertions(+), 1893 deletions(-)

diff --git a/LipNet.ipynb b/LipNet.ipynb
index 58d4a872..ef5467f0 100644
--- a/LipNet.ipynb
+++ b/LipNet.ipynb
@@ -39,112 +39,396 @@
           "name": "stdout",
           "output_type": "stream",
           "text": [
-            "Package                   Version\n",
-            "------------------------- -----------\n",
-            "absl-py                   2.1.0\n",
-            "altair                    5.3.0\n",
-            "asttokens                 2.4.1\n",
-            "astunparse                1.6.3\n",
-            "attrs                     23.2.0\n",
-            "beautifulsoup4            4.12.3\n",
-            "blinker                   1.7.0\n",
-            "cachetools                5.3.3\n",
-            "certifi                   2024.2.2\n",
-            "charset-normalizer        3.3.2\n",
-            "click                     8.1.7\n",
-            "colorama                  0.4.6\n",
-            "comm                      0.2.2\n",
-            "contourpy                 1.2.1\n",
-            "cycler                    0.12.1\n",
-            "debugpy                   1.8.1\n",
-            "decorator                 5.1.1\n",
-            "executing                 2.0.1\n",
-            "filelock                  3.13.4\n",
-            "Flask                     3.0.3\n",
-            "flatbuffers               24.3.25\n",
-            "fonttools                 4.51.0\n",
-            "gast                      0.5.4\n",
-            "gdown                     5.1.0\n",
-            "gitdb                     4.0.11\n",
-            "GitPython                 3.1.43\n",
-            "google-pasta              0.2.0\n",
-            "grpcio                    1.62.1\n",
-            "h5py                      3.10.0\n",
-            "idna                      3.6\n",
-            "imageio                   2.23.0\n",
-            "ipykernel                 6.29.4\n",
-            "ipython                   8.23.0\n",
-            "itsdangerous              2.1.2\n",
-            "jedi                      0.19.1\n",
-            "Jinja2                    3.1.3\n",
-            "jsonschema                4.21.1\n",
-            "jsonschema-specifications 2023.12.1\n",
-            "jupyter_client            8.6.1\n",
-            "jupyter_core              5.7.2\n",
-            "keras                     3.2.0\n",
-            "kiwisolver                1.4.5\n",
-            "libclang                  18.1.1\n",
-            "Markdown                  3.6\n",
-            "markdown-it-py            3.0.0\n",
-            "MarkupSafe                2.1.5\n",
-            "matplotlib                3.8.4\n",
-            "matplotlib-inline         0.1.6\n",
-            "mdurl                     0.1.2\n",
-            "ml-dtypes                 0.3.2\n",
-            "namex                     0.0.7\n",
-            "nest-asyncio              1.6.0\n",
-            "numpy                     1.26.4\n",
-            "opencv-python             4.9.0.80\n",
-            "opt-einsum                3.3.0\n",
-            "optree                    0.11.0\n",
-            "packaging                 24.0\n",
-            "pandas                    2.2.1\n",
-            "parso                     0.8.4\n",
-            "pillow                    10.3.0\n",
-            "pip                       24.0\n",
-            "platformdirs              4.2.0\n",
-            "prompt-toolkit            3.0.43\n",
-            "protobuf                  4.25.3\n",
-            "psutil                    5.9.8\n",
-            "pure-eval                 0.2.2\n",
-            "pyarrow                   15.0.2\n",
-            "pydeck                    0.8.1b0\n",
-            "Pygments                  2.17.2\n",
-            "pyparsing                 3.1.2\n",
-            "PySocks                   1.7.1\n",
-            "python-dateutil           2.9.0.post0\n",
-            "pytz                      2024.1\n",
-            "pywin32                   306\n",
-            "pyzmq                     25.1.2\n",
-            "referencing               0.34.0\n",
-            "requests                  2.31.0\n",
-            "rich                      13.7.1\n",
-            "rpds-py                   0.18.0\n",
-            "setuptools                69.2.0\n",
-            "six                       1.16.0\n",
-            "smmap                     5.0.1\n",
-            "soupsieve                 2.5\n",
-            "stack-data                0.6.3\n",
-            "streamlit                 1.33.0\n",
-            "tenacity                  8.2.3\n",
-            "tensorboard               2.16.2\n",
-            "tensorboard-data-server   0.7.2\n",
-            "tensorflow                2.16.1\n",
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-            "wrapt                     1.16.0\n"
+            "Package                           Version\n",
+            "--------------------------------- ------------\n",
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+            "nest-asyncio                      1.6.0\n",
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+            "paramiko                          2.8.1\n",
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+            "partd                             1.4.1\n",
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+            "Protego                           0.1.16\n",
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+            "psutil                            5.9.0\n",
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+            "pycparser                         2.21\n",
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+            "pyerfa                            2.0.0\n",
+            "pyflakes                          3.0.1\n",
+            "Pygments                          2.15.1\n",
+            "PyJWT                             2.4.0\n",
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+            "PyNaCl                            1.5.0\n",
+            "pyodbc                            5.0.1\n",
+            "pyOpenSSL                         24.0.0\n",
+            "pyparsing                         3.0.9\n",
+            "PyQt5                             5.15.10\n",
+            "PyQt5-sip                         12.13.0\n",
+            "PyQtWebEngine                     5.15.6\n",
+            "PySocks                           1.7.1\n",
+            "pytest                            7.4.0\n",
+            "python-dateutil                   2.8.2\n",
+            "python-dotenv                     0.21.0\n",
+            "python-json-logger                2.0.7\n",
+            "python-lsp-black                  1.2.1\n",
+            "python-lsp-jsonrpc                1.0.0\n",
+            "python-lsp-server                 1.7.2\n",
+            "python-slugify                    5.0.2\n",
+            "python-snappy                     0.6.1\n",
+            "pytoolconfig                      1.2.6\n",
+            "pytz                              2023.3.post1\n",
+            "pyviz_comms                       3.0.0\n",
+            "pywavelets                        1.5.0\n",
+            "pywin32                           305.1\n",
+            "pywin32-ctypes                    0.2.0\n",
+            "pywinpty                          2.0.10\n",
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+            "pyzmq                             25.1.2\n",
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+            "qstylizer                         0.2.2\n",
+            "QtAwesome                         1.2.2\n",
+            "qtconsole                         5.4.2\n",
+            "QtPy                              2.4.1\n",
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+            "referencing                       0.30.2\n",
+            "regex                             2023.10.3\n",
+            "requests                          2.31.0\n",
+            "requests-file                     1.5.1\n",
+            "requests-toolbelt                 1.0.0\n",
+            "rfc3339-validator                 0.1.4\n",
+            "rfc3986-validator                 0.1.1\n",
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+            "ruamel-yaml-conda                 0.17.21\n",
+            "s3fs                              2023.10.0\n",
+            "scikit-image                      0.22.0\n",
+            "scikit-learn                      1.2.2\n",
+            "scipy                             1.11.4\n",
+            "Scrapy                            2.8.0\n",
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+            "service-identity                  18.1.0\n",
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+            "six                               1.16.0\n",
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+            "sphinxcontrib-devhelp             1.0.2\n",
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+            "streamlit                         1.30.0\n",
+            "sympy                             1.12\n",
+            "tables                            3.9.2\n",
+            "tabulate                          0.9.0\n",
+            "tblib                             1.7.0\n",
+            "tenacity                          8.2.2\n",
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+            "tensorflow-io-gcs-filesystem      0.31.0\n",
+            "termcolor                         2.4.0\n",
+            "terminado                         0.17.1\n",
+            "text-unidecode                    1.3\n",
+            "textdistance                      4.2.1\n",
+            "threadpoolctl                     2.2.0\n",
+            "three-merge                       0.1.1\n",
+            "tifffile                          2023.4.12\n",
+            "tinycss2                          1.2.1\n",
+            "tldextract                        3.2.0\n",
+            "toml                              0.10.2\n",
+            "tomlkit                           0.11.1\n",
+            "toolz                             0.12.0\n",
+            "tornado                           6.3.3\n",
+            "tqdm                              4.65.0\n",
+            "traitlets                         5.7.1\n",
+            "truststore                        0.8.0\n",
+            "Twisted                           23.10.0\n",
+            "twisted-iocpsupport               1.0.2\n",
+            "typing_extensions                 4.9.0\n",
+            "tzdata                            2023.3\n",
+            "tzlocal                           2.1\n",
+            "uc-micro-py                       1.0.1\n",
+            "ujson                             5.4.0\n",
+            "Unidecode                         1.2.0\n",
+            "urllib3                           2.0.7\n",
+            "validators                        0.18.2\n",
+            "w3lib                             2.1.2\n",
+            "watchdog                          2.1.6\n",
+            "wcwidth                           0.2.5\n",
+            "webencodings                      0.5.1\n",
+            "websocket-client                  0.58.0\n",
+            "Werkzeug                          2.2.3\n",
+            "whatthepatch                      1.0.2\n",
+            "wheel                             0.41.2\n",
+            "widgetsnbextension                3.5.2\n",
+            "win-inet-pton                     1.1.0\n",
+            "wrapt                             1.14.1\n",
+            "xarray                            2023.6.0\n",
+            "xlwings                           0.29.1\n",
+            "xyzservices                       2022.9.0\n",
+            "yapf                              0.31.0\n",
+            "yarl                              1.9.3\n",
+            "zict                              3.0.0\n",
+            "zipp                              3.17.0\n",
+            "zope.interface                    5.4.0\n",
+            "zstandard                         0.19.0\n"
           ]
         }
       ],
@@ -170,61 +454,62 @@
           "output_type": "stream",
           "text": [
             "Defaulting to user installation because normal site-packages is not writeable\n",
-            "Requirement already satisfied: opencv-python in c:\\users\\lhdto\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.12_qbz5n2kfra8p0\\localcache\\local-packages\\python312\\site-packages (4.9.0.80)\n",
-            "Requirement already satisfied: matplotlib in c:\\users\\lhdto\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.12_qbz5n2kfra8p0\\localcache\\local-packages\\python312\\site-packages (3.8.4)\n",
-            "Requirement already satisfied: imageio in c:\\users\\lhdto\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.12_qbz5n2kfra8p0\\localcache\\local-packages\\python312\\site-packages (2.23.0)\n",
-            "Requirement already satisfied: gdown in c:\\users\\lhdto\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.12_qbz5n2kfra8p0\\localcache\\local-packages\\python312\\site-packages (5.1.0)\n",
-            "Requirement already satisfied: tensorflow in c:\\users\\lhdto\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.12_qbz5n2kfra8p0\\localcache\\local-packages\\python312\\site-packages (2.16.1)\n",
-            "Requirement already satisfied: numpy>=1.21.2 in c:\\users\\lhdto\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.12_qbz5n2kfra8p0\\localcache\\local-packages\\python312\\site-packages (from opencv-python) (1.26.4)\n",
-            "Requirement already satisfied: contourpy>=1.0.1 in c:\\users\\lhdto\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.12_qbz5n2kfra8p0\\localcache\\local-packages\\python312\\site-packages (from matplotlib) (1.2.1)\n",
-            "Requirement already satisfied: cycler>=0.10 in c:\\users\\lhdto\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.12_qbz5n2kfra8p0\\localcache\\local-packages\\python312\\site-packages (from matplotlib) (0.12.1)\n",
-            "Requirement already satisfied: fonttools>=4.22.0 in c:\\users\\lhdto\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.12_qbz5n2kfra8p0\\localcache\\local-packages\\python312\\site-packages (from matplotlib) (4.51.0)\n",
-            "Requirement already satisfied: kiwisolver>=1.3.1 in c:\\users\\lhdto\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.12_qbz5n2kfra8p0\\localcache\\local-packages\\python312\\site-packages (from matplotlib) (1.4.5)\n",
-            "Requirement already satisfied: packaging>=20.0 in c:\\users\\lhdto\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.12_qbz5n2kfra8p0\\localcache\\local-packages\\python312\\site-packages (from matplotlib) (24.0)\n",
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-            "Requirement already satisfied: gast!=0.5.0,!=0.5.1,!=0.5.2,>=0.2.1 in c:\\users\\lhdto\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.12_qbz5n2kfra8p0\\localcache\\local-packages\\python312\\site-packages (from tensorflow-intel==2.16.1->tensorflow) (0.5.4)\n",
-            "Requirement already satisfied: google-pasta>=0.1.1 in c:\\users\\lhdto\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.12_qbz5n2kfra8p0\\localcache\\local-packages\\python312\\site-packages (from tensorflow-intel==2.16.1->tensorflow) (0.2.0)\n",
-            "Requirement already satisfied: h5py>=3.10.0 in c:\\users\\lhdto\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.12_qbz5n2kfra8p0\\localcache\\local-packages\\python312\\site-packages (from tensorflow-intel==2.16.1->tensorflow) (3.10.0)\n",
-            "Requirement already satisfied: libclang>=13.0.0 in c:\\users\\lhdto\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.12_qbz5n2kfra8p0\\localcache\\local-packages\\python312\\site-packages (from tensorflow-intel==2.16.1->tensorflow) (18.1.1)\n",
-            "Requirement already satisfied: ml-dtypes~=0.3.1 in c:\\users\\lhdto\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.12_qbz5n2kfra8p0\\localcache\\local-packages\\python312\\site-packages (from tensorflow-intel==2.16.1->tensorflow) (0.3.2)\n",
-            "Requirement already satisfied: opt-einsum>=2.3.2 in c:\\users\\lhdto\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.12_qbz5n2kfra8p0\\localcache\\local-packages\\python312\\site-packages (from tensorflow-intel==2.16.1->tensorflow) (3.3.0)\n",
-            "Requirement already satisfied: protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.20.3 in c:\\users\\lhdto\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.12_qbz5n2kfra8p0\\localcache\\local-packages\\python312\\site-packages (from tensorflow-intel==2.16.1->tensorflow) (4.25.3)\n",
-            "Requirement already satisfied: setuptools in c:\\users\\lhdto\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.12_qbz5n2kfra8p0\\localcache\\local-packages\\python312\\site-packages (from tensorflow-intel==2.16.1->tensorflow) (69.2.0)\n",
-            "Requirement already satisfied: six>=1.12.0 in c:\\users\\lhdto\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.12_qbz5n2kfra8p0\\localcache\\local-packages\\python312\\site-packages (from tensorflow-intel==2.16.1->tensorflow) (1.16.0)\n",
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-            "Requirement already satisfied: typing-extensions>=3.6.6 in c:\\users\\lhdto\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.12_qbz5n2kfra8p0\\localcache\\local-packages\\python312\\site-packages (from tensorflow-intel==2.16.1->tensorflow) (4.11.0)\n",
-            "Requirement already satisfied: wrapt>=1.11.0 in c:\\users\\lhdto\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.12_qbz5n2kfra8p0\\localcache\\local-packages\\python312\\site-packages (from tensorflow-intel==2.16.1->tensorflow) (1.16.0)\n",
-            "Requirement already satisfied: grpcio<2.0,>=1.24.3 in c:\\users\\lhdto\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.12_qbz5n2kfra8p0\\localcache\\local-packages\\python312\\site-packages (from tensorflow-intel==2.16.1->tensorflow) (1.62.1)\n",
-            "Requirement already satisfied: tensorboard<2.17,>=2.16 in c:\\users\\lhdto\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.12_qbz5n2kfra8p0\\localcache\\local-packages\\python312\\site-packages (from tensorflow-intel==2.16.1->tensorflow) (2.16.2)\n",
-            "Requirement already satisfied: keras>=3.0.0 in c:\\users\\lhdto\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.12_qbz5n2kfra8p0\\localcache\\local-packages\\python312\\site-packages (from tensorflow-intel==2.16.1->tensorflow) (3.2.0)\n",
-            "Requirement already satisfied: soupsieve>1.2 in c:\\users\\lhdto\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.12_qbz5n2kfra8p0\\localcache\\local-packages\\python312\\site-packages (from beautifulsoup4->gdown) (2.5)\n",
-            "Requirement already satisfied: charset-normalizer<4,>=2 in c:\\users\\lhdto\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.12_qbz5n2kfra8p0\\localcache\\local-packages\\python312\\site-packages (from requests[socks]->gdown) (3.3.2)\n",
-            "Requirement already satisfied: idna<4,>=2.5 in c:\\users\\lhdto\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.12_qbz5n2kfra8p0\\localcache\\local-packages\\python312\\site-packages (from requests[socks]->gdown) (3.6)\n",
-            "Requirement already satisfied: urllib3<3,>=1.21.1 in c:\\users\\lhdto\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.12_qbz5n2kfra8p0\\localcache\\local-packages\\python312\\site-packages (from requests[socks]->gdown) (2.2.1)\n",
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-            "Requirement already satisfied: PySocks!=1.5.7,>=1.5.6 in c:\\users\\lhdto\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.12_qbz5n2kfra8p0\\localcache\\local-packages\\python312\\site-packages (from requests[socks]->gdown) (1.7.1)\n",
-            "Requirement already satisfied: colorama in c:\\users\\lhdto\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.12_qbz5n2kfra8p0\\localcache\\local-packages\\python312\\site-packages (from tqdm->gdown) (0.4.6)\n",
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-            "Requirement already satisfied: rich in c:\\users\\lhdto\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.12_qbz5n2kfra8p0\\localcache\\local-packages\\python312\\site-packages (from keras>=3.0.0->tensorflow-intel==2.16.1->tensorflow) (13.7.1)\n",
-            "Requirement already satisfied: namex in c:\\users\\lhdto\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.12_qbz5n2kfra8p0\\localcache\\local-packages\\python312\\site-packages (from keras>=3.0.0->tensorflow-intel==2.16.1->tensorflow) (0.0.7)\n",
-            "Requirement already satisfied: optree in c:\\users\\lhdto\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.12_qbz5n2kfra8p0\\localcache\\local-packages\\python312\\site-packages (from keras>=3.0.0->tensorflow-intel==2.16.1->tensorflow) (0.11.0)\n",
-            "Requirement already satisfied: markdown>=2.6.8 in c:\\users\\lhdto\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.12_qbz5n2kfra8p0\\localcache\\local-packages\\python312\\site-packages (from tensorboard<2.17,>=2.16->tensorflow-intel==2.16.1->tensorflow) (3.6)\n",
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-            "Requirement already satisfied: werkzeug>=1.0.1 in c:\\users\\lhdto\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.12_qbz5n2kfra8p0\\localcache\\local-packages\\python312\\site-packages (from tensorboard<2.17,>=2.16->tensorflow-intel==2.16.1->tensorflow) (3.0.2)\n",
-            "Requirement already satisfied: MarkupSafe>=2.1.1 in c:\\users\\lhdto\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.12_qbz5n2kfra8p0\\localcache\\local-packages\\python312\\site-packages (from werkzeug>=1.0.1->tensorboard<2.17,>=2.16->tensorflow-intel==2.16.1->tensorflow) (2.1.5)\n",
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+            "Requirement already satisfied: opencv-python in c:\\users\\lhdto\\appdata\\roaming\\python\\python311\\site-packages (4.9.0.80)\n",
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+            "Requirement already satisfied: ml-dtypes~=0.3.1 in c:\\users\\lhdto\\appdata\\roaming\\python\\python311\\site-packages (from tensorflow-intel==2.16.1->tensorflow) (0.3.2)\n",
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+            "Requirement already satisfied: setuptools in c:\\programdata\\anaconda3\\lib\\site-packages (from tensorflow-intel==2.16.1->tensorflow) (68.2.2)\n",
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+            "Requirement already satisfied: typing-extensions>=3.6.6 in c:\\programdata\\anaconda3\\lib\\site-packages (from tensorflow-intel==2.16.1->tensorflow) (4.9.0)\n",
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+            "Requirement already satisfied: grpcio<2.0,>=1.24.3 in c:\\users\\lhdto\\appdata\\roaming\\python\\python311\\site-packages (from tensorflow-intel==2.16.1->tensorflow) (1.62.1)\n",
+            "Requirement already satisfied: tensorboard<2.17,>=2.16 in c:\\users\\lhdto\\appdata\\roaming\\python\\python311\\site-packages (from tensorflow-intel==2.16.1->tensorflow) (2.16.2)\n",
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+            "Requirement already satisfied: tensorflow-io-gcs-filesystem>=0.23.1 in c:\\users\\lhdto\\appdata\\roaming\\python\\python311\\site-packages (from tensorflow-intel==2.16.1->tensorflow) (0.31.0)\n",
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+            "Requirement already satisfied: certifi>=2017.4.17 in c:\\programdata\\anaconda3\\lib\\site-packages (from requests[socks]->gdown) (2024.2.2)\n",
+            "Requirement already satisfied: PySocks!=1.5.7,>=1.5.6 in c:\\programdata\\anaconda3\\lib\\site-packages (from requests[socks]->gdown) (1.7.1)\n",
+            "Requirement already satisfied: colorama in c:\\programdata\\anaconda3\\lib\\site-packages (from tqdm->gdown) (0.4.6)\n",
+            "Requirement already satisfied: wheel<1.0,>=0.23.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from astunparse>=1.6.0->tensorflow-intel==2.16.1->tensorflow) (0.41.2)\n",
+            "Requirement already satisfied: rich in c:\\programdata\\anaconda3\\lib\\site-packages (from keras>=3.0.0->tensorflow-intel==2.16.1->tensorflow) (13.3.5)\n",
+            "Requirement already satisfied: namex in c:\\users\\lhdto\\appdata\\roaming\\python\\python311\\site-packages (from keras>=3.0.0->tensorflow-intel==2.16.1->tensorflow) (0.0.7)\n",
+            "Requirement already satisfied: optree in c:\\users\\lhdto\\appdata\\roaming\\python\\python311\\site-packages (from keras>=3.0.0->tensorflow-intel==2.16.1->tensorflow) (0.11.0)\n",
+            "Requirement already satisfied: markdown>=2.6.8 in c:\\programdata\\anaconda3\\lib\\site-packages (from tensorboard<2.17,>=2.16->tensorflow-intel==2.16.1->tensorflow) (3.4.1)\n",
+            "Requirement already satisfied: tensorboard-data-server<0.8.0,>=0.7.0 in c:\\users\\lhdto\\appdata\\roaming\\python\\python311\\site-packages (from tensorboard<2.17,>=2.16->tensorflow-intel==2.16.1->tensorflow) (0.7.2)\n",
+            "Requirement already satisfied: werkzeug>=1.0.1 in c:\\programdata\\anaconda3\\lib\\site-packages (from tensorboard<2.17,>=2.16->tensorflow-intel==2.16.1->tensorflow) (2.2.3)\n",
+            "Requirement already satisfied: MarkupSafe>=2.1.1 in c:\\programdata\\anaconda3\\lib\\site-packages (from werkzeug>=1.0.1->tensorboard<2.17,>=2.16->tensorflow-intel==2.16.1->tensorflow) (2.1.3)\n",
+            "Requirement already satisfied: markdown-it-py<3.0.0,>=2.2.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from rich->keras>=3.0.0->tensorflow-intel==2.16.1->tensorflow) (2.2.0)\n",
+            "Requirement already satisfied: pygments<3.0.0,>=2.13.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from rich->keras>=3.0.0->tensorflow-intel==2.16.1->tensorflow) (2.15.1)\n",
+            "Requirement already satisfied: mdurl~=0.1 in c:\\programdata\\anaconda3\\lib\\site-packages (from markdown-it-py<3.0.0,>=2.2.0->rich->keras>=3.0.0->tensorflow-intel==2.16.1->tensorflow) (0.1.0)\n"
           ]
         }
       ],
@@ -235,33 +520,6 @@
     {
       "cell_type": "code",
       "execution_count": 3,
-      "id": "AIsiftVmlrwN",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "AIsiftVmlrwN",
-        "outputId": "6bb43d43-d442-480d-c8f1-480a0403c717"
-      },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Defaulting to user installation because normal site-packages is not writeable\n",
-            "Requirement already satisfied: imageio==2.23.0 in c:\\users\\lhdto\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.12_qbz5n2kfra8p0\\localcache\\local-packages\\python312\\site-packages (2.23.0)\n",
-            "Requirement already satisfied: numpy in c:\\users\\lhdto\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.12_qbz5n2kfra8p0\\localcache\\local-packages\\python312\\site-packages (from imageio==2.23.0) (1.26.4)\n",
-            "Requirement already satisfied: pillow>=8.3.2 in c:\\users\\lhdto\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.12_qbz5n2kfra8p0\\localcache\\local-packages\\python312\\site-packages (from imageio==2.23.0) (10.3.0)\n"
-          ]
-        }
-      ],
-      "source": [
-        "!pip install imageio==2.23.0"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 4,
       "id": "b24af50c-20b8-409d-ad78-30a933fdd669",
       "metadata": {
         "id": "b24af50c-20b8-409d-ad78-30a933fdd669",
@@ -275,12 +533,13 @@
         "import numpy as np\n",
         "from typing import List\n",
         "from matplotlib import pyplot as plt\n",
-        "import imageio"
+        "import imageio\n",
+        "import keras"
       ]
     },
     {
       "cell_type": "code",
-      "execution_count": 5,
+      "execution_count": 4,
       "id": "1e3db0b0-e559-4ad6-91fd-e7414b7d75e6",
       "metadata": {
         "colab": {
@@ -296,7 +555,7 @@
               "[]"
             ]
           },
-          "execution_count": 5,
+          "execution_count": 4,
           "metadata": {},
           "output_type": "execute_result"
         }
@@ -307,7 +566,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 6,
+      "execution_count": 5,
       "id": "378d045a-3003-4f93-b7d2-a25a97774a68",
       "metadata": {
         "id": "378d045a-3003-4f93-b7d2-a25a97774a68",
@@ -335,7 +594,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 7,
+      "execution_count": 6,
       "id": "8fb99c90-e05a-437f-839d-6e772f8c1dd5",
       "metadata": {
         "id": "8fb99c90-e05a-437f-839d-6e772f8c1dd5",
@@ -348,7 +607,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 8,
+      "execution_count": 7,
       "id": "c019e4c6-2af3-4160-99ea-5c8cb009f1a7",
       "metadata": {
         "colab": {
@@ -358,1039 +617,17 @@
         "outputId": "49a7c7ff-ab7d-480e-bcfd-ee652ad89af1",
         "tags": []
       },
-      "outputs": [
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            "Downloading...\n",
-            "From (original): https://drive.google.com/uc?id=1YlvpDLix3S-U8fd-gqRwPcWXAXm8JwjL\n",
-            "From (redirected): https://drive.google.com/uc?id=1YlvpDLix3S-U8fd-gqRwPcWXAXm8JwjL&confirm=t&uuid=2e632692-442f-4cf0-9357-ba800700a97e\n",
-            "To: c:\\haidun.li\\NSCC\\LipNet\\data.zip\n",
-            "100%|██████████| 423M/423M [00:10<00:00, 41.1MB/s] \n"
-          ]
-        },
-        {
-          "data": {
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-              " 'data/alignments/s1/swwp3s.align',\n",
-              " 'data/alignments/s1/swwp4p.align',\n",
-              " 'data/alignments/s1/swwp5a.align',\n",
-              " 'data/alignments/s1/swwv6n.align',\n",
-              " ...]"
-            ]
-          },
-          "execution_count": 8,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
+      "outputs": [],
       "source": [
-        "url = 'https://drive.google.com/uc?id=1YlvpDLix3S-U8fd-gqRwPcWXAXm8JwjL'\n",
-        "output = 'data.zip'\n",
-        "gdown.download(url, output, quiet=False)\n",
-        "gdown.extractall('data.zip')"
+        "# url = 'https://drive.google.com/uc?id=1YlvpDLix3S-U8fd-gqRwPcWXAXm8JwjL'\n",
+        "# output = 'data.zip'\n",
+        "# gdown.download(url, output, quiet=False)\n",
+        "# gdown.extractall('data.zip')"
       ]
     },
     {
       "cell_type": "code",
-      "execution_count": 9,
+      "execution_count": 8,
       "id": "8548cc59-6dfc-4acc-abc3-3e65212db02e",
       "metadata": {
         "id": "8548cc59-6dfc-4acc-abc3-3e65212db02e",
@@ -1415,7 +652,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 10,
+      "execution_count": 9,
       "id": "ec735e0b-ec98-4eb0-8f49-c35527d6670a",
       "metadata": {
         "id": "ec735e0b-ec98-4eb0-8f49-c35527d6670a",
@@ -1428,7 +665,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 11,
+      "execution_count": 10,
       "id": "be04e972-d7a5-4a72-82d8-a6bdde1f3ce6",
       "metadata": {
         "colab": {
@@ -1448,8 +685,12 @@
         }
       ],
       "source": [
-        "char_to_num = tf.keras.layers.StringLookup(vocabulary=vocab, oov_token=\"\")\n",
-        "num_to_char = tf.keras.layers.StringLookup(\n",
+        "# char_to_num = tf.keras.layers.StringLookup(vocabulary=vocab, oov_token=\"\")\n",
+        "# num_to_char = tf.keras.layers.StringLookup(\n",
+        "#     vocabulary=char_to_num.get_vocabulary(), oov_token=\"\", invert=True\n",
+        "# )\n",
+        "char_to_num = keras.layers.StringLookup(vocabulary=vocab, oov_token=\"\")\n",
+        "num_to_char = keras.layers.StringLookup(\n",
         "    vocabulary=char_to_num.get_vocabulary(), oov_token=\"\", invert=True\n",
         ")\n",
         "\n",
@@ -1461,7 +702,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 12,
+      "execution_count": 11,
       "id": "559f7420-6802-45fa-9ca0-b1ff209b461c",
       "metadata": {
         "colab": {
@@ -1517,7 +758,7 @@
               " ' ']"
             ]
           },
-          "execution_count": 12,
+          "execution_count": 11,
           "metadata": {},
           "output_type": "execute_result"
         }
@@ -1528,7 +769,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 13,
+      "execution_count": 12,
       "id": "797ff78b-b48f-4e14-bb62-8cd0ebf9501a",
       "metadata": {
         "colab": {
@@ -1545,7 +786,7 @@
               ""
             ]
           },
-          "execution_count": 13,
+          "execution_count": 12,
           "metadata": {},
           "output_type": "execute_result"
         }
@@ -1556,7 +797,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 14,
+      "execution_count": 13,
       "id": "8cd7f4f4-ae77-4509-a4f4-c723787ebad1",
       "metadata": {
         "colab": {
@@ -1572,7 +813,7 @@
               ""
             ]
           },
-          "execution_count": 14,
+          "execution_count": 13,
           "metadata": {},
           "output_type": "execute_result"
         }
@@ -1583,7 +824,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 15,
+      "execution_count": 14,
       "id": "9491bab5-6a3c-4f79-879a-8f9fbe73ae2e",
       "metadata": {
         "id": "9491bab5-6a3c-4f79-879a-8f9fbe73ae2e",
@@ -1604,7 +845,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 16,
+      "execution_count": 15,
       "id": "dd01ca9f-77fb-4643-a2aa-47dd82c5d66b",
       "metadata": {
         "id": "dd01ca9f-77fb-4643-a2aa-47dd82c5d66b",
@@ -1627,7 +868,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 17,
+      "execution_count": 16,
       "id": "8cb7cc58-31ae-4904-a805-1177a82717d2",
       "metadata": {
         "id": "8cb7cc58-31ae-4904-a805-1177a82717d2",
@@ -1641,7 +882,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 18,
+      "execution_count": 17,
       "id": "76aa964f-0c84-490d-897a-d00e3966e2c9",
       "metadata": {
         "colab": {
@@ -1655,22 +896,22 @@
         {
           "data": {
             "text/plain": [
-              "''"
+              "'bbal6n'"
             ]
           },
-          "execution_count": 18,
+          "execution_count": 17,
           "metadata": {},
           "output_type": "execute_result"
         }
       ],
       "source": [
-        "# tf.convert_to_tensor(test_path).numpy().decode('utf-8').split('\\\\')[-1].split('.')[0]\n",
-        "tf.convert_to_tensor(test_path).numpy().decode('utf-8').split('/')[-1].split('.')[0]"
+        "tf.convert_to_tensor(test_path).numpy().decode('utf-8').split('\\\\')[-1].split('.')[0]\n",
+        "# tf.convert_to_tensor(test_path).numpy().decode('utf-8').split('/')[-1].split('.')[0]"
       ]
     },
     {
       "cell_type": "code",
-      "execution_count": 19,
+      "execution_count": 18,
       "id": "eb602c71-8560-4f9e-b26b-08202febb937",
       "metadata": {
         "id": "eb602c71-8560-4f9e-b26b-08202febb937",
@@ -1684,7 +925,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 20,
+      "execution_count": 19,
       "id": "0e3184a1-6b02-4b4f-84a8-a0a65f951ea2",
       "metadata": {
         "colab": {
@@ -1698,10 +939,10 @@
         {
           "data": {
             "text/plain": [
-              ""
+              ""
             ]
           },
-          "execution_count": 20,
+          "execution_count": 19,
           "metadata": {},
           "output_type": "execute_result"
         },
@@ -1722,7 +963,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 21,
+      "execution_count": 20,
       "id": "d7ec0833-d54b-4073-84cf-92d011c60ec1",
       "metadata": {
         "colab": {
@@ -1740,7 +981,7 @@
               "       39, 14, 15, 23], dtype=int64)>"
             ]
           },
-          "execution_count": 21,
+          "execution_count": 20,
           "metadata": {},
           "output_type": "execute_result"
         }
@@ -1751,7 +992,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 22,
+      "execution_count": 21,
       "id": "fe1ad370-b287-4b46-85a2-7c45b0bd9b10",
       "metadata": {
         "colab": {
@@ -1767,7 +1008,7 @@
               ""
             ]
           },
-          "execution_count": 22,
+          "execution_count": 21,
           "metadata": {},
           "output_type": "execute_result"
         }
@@ -1778,7 +1019,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 23,
+      "execution_count": 22,
       "id": "6871031a-b0ba-4c76-a852-f6329b0f2606",
       "metadata": {
         "id": "6871031a-b0ba-4c76-a852-f6329b0f2606",
@@ -1804,7 +1045,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 24,
+      "execution_count": 23,
       "id": "7686355d-45aa-4c85-ad9c-053e6a9b4d81",
       "metadata": {
         "id": "7686355d-45aa-4c85-ad9c-053e6a9b4d81",
@@ -1817,7 +1058,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 25,
+      "execution_count": 54,
       "id": "f066fea2-91b1-42ed-a67d-00566a1a53ff",
       "metadata": {
         "id": "f066fea2-91b1-42ed-a67d-00566a1a53ff",
@@ -1837,7 +1078,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 26,
+      "execution_count": 25,
       "id": "6b1365bd-7742-41d1-95d4-247021751c3a",
       "metadata": {
         "colab": {
@@ -1853,7 +1094,7 @@
               "50"
             ]
           },
-          "execution_count": 26,
+          "execution_count": 25,
           "metadata": {},
           "output_type": "execute_result"
         }
@@ -1864,7 +1105,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 27,
+      "execution_count": 26,
       "id": "5281bde8-fdc8-4da1-bd55-5a7929a9e80c",
       "metadata": {
         "id": "5281bde8-fdc8-4da1-bd55-5a7929a9e80c"
@@ -1876,7 +1117,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 28,
+      "execution_count": 27,
       "id": "cbebe683-6afd-47fd-bba4-c83b4b13bb32",
       "metadata": {
         "colab": {
@@ -1892,7 +1133,7 @@
               "2"
             ]
           },
-          "execution_count": 28,
+          "execution_count": 27,
           "metadata": {},
           "output_type": "execute_result"
         }
@@ -1903,7 +1144,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 29,
+      "execution_count": 28,
       "id": "5cf2d676-93a9-434c-b3c7-bdcc2577b2e7",
       "metadata": {
         "id": "5cf2d676-93a9-434c-b3c7-bdcc2577b2e7",
@@ -1916,7 +1157,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 30,
+      "execution_count": 29,
       "id": "efa6cd46-7079-46c0-b45b-832f339f6cb0",
       "metadata": {
         "colab": {
@@ -1931,626 +1172,626 @@
         {
           "data": {
             "text/plain": [
-              "array([[[[[ 1.5390785 ],\n",
-              "          [ 1.5024338 ],\n",
-              "          [ 1.5757232 ],\n",
+              "array([[[[[ 1.3226067 ],\n",
+              "          [ 1.3226067 ],\n",
+              "          [ 1.4393072 ],\n",
               "          ...,\n",
-              "          [ 0.36644727],\n",
-              "          [ 0.36644727],\n",
-              "          [ 0.36644727]],\n",
+              "          [ 9.0637455 ],\n",
+              "          [ 9.725049  ],\n",
+              "          [ 9.763949  ]],\n",
               "\n",
-              "         [[ 1.5757232 ],\n",
-              "          [ 1.5757232 ],\n",
-              "          [ 1.612368  ],\n",
+              "         [[ 1.3226067 ],\n",
+              "          [ 1.3226067 ],\n",
+              "          [ 1.4393072 ],\n",
               "          ...,\n",
-              "          [ 0.36644727],\n",
-              "          [ 0.36644727],\n",
-              "          [ 0.36644727]],\n",
+              "          [ 9.180446  ],\n",
+              "          [ 9.608348  ],\n",
+              "          [ 9.686149  ]],\n",
               "\n",
-              "         [[ 1.5024338 ],\n",
-              "          [ 1.5024338 ],\n",
-              "          [ 1.4657891 ],\n",
+              "         [[ 1.2448063 ],\n",
+              "          [ 1.2448063 ],\n",
+              "          [ 1.2448063 ],\n",
               "          ...,\n",
-              "          [ 0.32980254],\n",
-              "          [ 0.32980254],\n",
-              "          [ 0.32980254]],\n",
+              "          [ 9.802849  ],\n",
+              "          [ 9.91955   ],\n",
+              "          [ 9.91955   ]],\n",
               "\n",
               "         ...,\n",
               "\n",
-              "         [[ 1.062697  ],\n",
-              "          [ 1.062697  ],\n",
-              "          [ 1.0260524 ],\n",
+              "         [[ 0.89470446],\n",
+              "          [ 0.89470446],\n",
+              "          [ 0.89470446],\n",
               "          ...,\n",
-              "          [ 0.07328945],\n",
-              "          [ 0.03664473],\n",
-              "          [ 0.03664473]],\n",
+              "          [ 9.802849  ],\n",
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+              "          [ 9.725049  ]],\n",
               "\n",
-              "         [[ 1.062697  ],\n",
-              "          [ 1.062697  ],\n",
-              "          [ 1.0260524 ],\n",
+              "         [[ 0.89470446],\n",
+              "          [ 0.89470446],\n",
+              "          [ 0.89470446],\n",
               "          ...,\n",
-              "          [ 0.07328945],\n",
-              "          [ 0.03664473],\n",
-              "          [ 0.03664473]],\n",
+              "          [ 9.647248  ],\n",
+              "          [ 9.647248  ],\n",
+              "          [ 9.647248  ]],\n",
               "\n",
-              "         [[ 1.062697  ],\n",
-              "          [ 1.062697  ],\n",
-              "          [ 1.0260524 ],\n",
+              "         [[ 0.89470446],\n",
+              "          [ 0.89470446],\n",
+              "          [ 0.89470446],\n",
               "          ...,\n",
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-              "          [ 0.        ]]],\n",
+              "          [ 9.647248  ],\n",
+              "          [ 9.647248  ],\n",
+              "          [ 9.647248  ]]],\n",
               "\n",
               "\n",
-              "        [[[ 1.5390785 ],\n",
-              "          [ 1.4291443 ],\n",
-              "          [ 1.5024338 ],\n",
+              "        [[[ 1.2837064 ],\n",
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-              "          [ 0.32980254],\n",
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+              "          [ 9.219346  ],\n",
+              "          [ 9.841749  ],\n",
+              "          [ 9.841749  ]],\n",
               "\n",
-              "         [[ 1.5024338 ],\n",
-              "          [ 1.5024338 ],\n",
-              "          [ 1.4291443 ],\n",
+              "         [[ 1.2837064 ],\n",
+              "          [ 1.2837064 ],\n",
+              "          [ 1.3615068 ],\n",
               "          ...,\n",
-              "          [ 0.32980254],\n",
-              "          [ 0.32980254],\n",
-              "          [ 0.32980254]],\n",
+              "          [ 9.336047  ],\n",
+              "          [ 9.725049  ],\n",
+              "          [ 9.763949  ]],\n",
               "\n",
-              "         [[ 1.3924996 ],\n",
-              "          [ 1.3924996 ],\n",
-              "          [ 1.4657891 ],\n",
+              "         [[ 1.205906  ],\n",
+              "          [ 1.205906  ],\n",
+              "          [ 1.205906  ],\n",
               "          ...,\n",
-              "          [ 0.32980254],\n",
-              "          [ 0.32980254],\n",
-              "          [ 0.32980254]],\n",
+              "          [ 9.91955   ],\n",
+              "          [ 9.91955   ],\n",
+              "          [ 9.91955   ]],\n",
               "\n",
               "         ...,\n",
               "\n",
-              "         [[ 1.062697  ],\n",
-              "          [ 1.062697  ],\n",
-              "          [ 1.0260524 ],\n",
+              "         [[ 0.93360466],\n",
+              "          [ 0.89470446],\n",
+              "          [ 0.89470446],\n",
               "          ...,\n",
-              "          [ 0.07328945],\n",
-              "          [ 0.03664473],\n",
-              "          [ 0.03664473]],\n",
+              "          [ 9.763949  ],\n",
+              "          [ 9.802849  ],\n",
+              "          [ 9.802849  ]],\n",
               "\n",
-              "         [[ 1.062697  ],\n",
-              "          [ 1.062697  ],\n",
-              "          [ 1.0260524 ],\n",
+              "         [[ 0.93360466],\n",
+              "          [ 0.89470446],\n",
+              "          [ 0.89470446],\n",
               "          ...,\n",
-              "          [ 0.07328945],\n",
-              "          [ 0.03664473],\n",
-              "          [ 0.        ]],\n",
+              "          [ 9.686149  ],\n",
+              "          [ 9.686149  ],\n",
+              "          [ 9.686149  ]],\n",
               "\n",
-              "         [[ 1.0260524 ],\n",
-              "          [ 1.0260524 ],\n",
-              "          [ 0.9894076 ],\n",
+              "         [[ 0.93360466],\n",
+              "          [ 0.89470446],\n",
+              "          [ 0.89470446],\n",
               "          ...,\n",
-              "          [ 0.07328945],\n",
-              "          [ 0.        ],\n",
-              "          [ 0.        ]]],\n",
+              "          [ 9.686149  ],\n",
+              "          [ 9.686149  ],\n",
+              "          [ 9.686149  ]]],\n",
               "\n",
               "\n",
-              "        [[[ 1.5390785 ],\n",
-              "          [ 1.4657891 ],\n",
-              "          [ 1.3924996 ],\n",
+              "        [[[ 1.2837064 ],\n",
+              "          [ 1.2837064 ],\n",
+              "          [ 1.3615068 ],\n",
               "          ...,\n",
-              "          [ 0.32980254],\n",
-              "          [ 0.29315782],\n",
-              "          [ 0.29315782]],\n",
+              "          [ 9.180446  ],\n",
+              "          [ 9.841749  ],\n",
+              "          [ 9.802849  ]],\n",
               "\n",
-              "         [[ 1.5390785 ],\n",
-              "          [ 1.4291443 ],\n",
-              "          [ 1.4657891 ],\n",
+              "         [[ 1.2837064 ],\n",
+              "          [ 1.2837064 ],\n",
+              "          [ 1.3615068 ],\n",
               "          ...,\n",
-              "          [ 0.32980254],\n",
-              "          [ 0.29315782],\n",
-              "          [ 0.29315782]],\n",
+              "          [ 9.336047  ],\n",
+              "          [ 9.647248  ],\n",
+              "          [ 9.763949  ]],\n",
               "\n",
-              "         [[ 1.5024338 ],\n",
-              "          [ 1.5024338 ],\n",
-              "          [ 1.4657891 ],\n",
+              "         [[ 1.3226067 ],\n",
+              "          [ 1.3226067 ],\n",
+              "          [ 1.3226067 ],\n",
               "          ...,\n",
-              "          [ 0.36644727],\n",
-              "          [ 0.32980254],\n",
-              "          [ 0.32980254]],\n",
+              "          [ 9.88065   ],\n",
+              "          [ 9.91955   ],\n",
+              "          [ 9.91955   ]],\n",
               "\n",
               "         ...,\n",
               "\n",
-              "         [[ 1.062697  ],\n",
-              "          [ 1.062697  ],\n",
-              "          [ 1.0260524 ],\n",
+              "         [[ 0.93360466],\n",
+              "          [ 0.89470446],\n",
+              "          [ 0.89470446],\n",
               "          ...,\n",
-              "          [ 0.07328945],\n",
-              "          [ 0.03664473],\n",
-              "          [ 0.03664473]],\n",
+              "          [ 9.763949  ],\n",
+              "          [ 9.802849  ],\n",
+              "          [ 9.802849  ]],\n",
               "\n",
-              "         [[ 1.062697  ],\n",
-              "          [ 1.062697  ],\n",
-              "          [ 1.0260524 ],\n",
+              "         [[ 0.93360466],\n",
+              "          [ 0.89470446],\n",
+              "          [ 0.89470446],\n",
               "          ...,\n",
-              "          [ 0.07328945],\n",
-              "          [ 0.03664473],\n",
-              "          [ 0.        ]],\n",
+              "          [ 9.686149  ],\n",
+              "          [ 9.725049  ],\n",
+              "          [ 9.725049  ]],\n",
               "\n",
-              "         [[ 1.0260524 ],\n",
-              "          [ 1.0260524 ],\n",
-              "          [ 0.9894076 ],\n",
+              "         [[ 0.93360466],\n",
+              "          [ 0.89470446],\n",
+              "          [ 0.89470446],\n",
               "          ...,\n",
-              "          [ 0.07328945],\n",
-              "          [ 0.        ],\n",
-              "          [ 0.        ]]],\n",
+              "          [ 9.686149  ],\n",
+              "          [ 9.725049  ],\n",
+              "          [ 9.725049  ]]],\n",
               "\n",
               "\n",
               "        ...,\n",
               "\n",
               "\n",
-              "        [[[ 1.5024338 ],\n",
-              "          [ 1.4291443 ],\n",
-              "          [ 1.4291443 ],\n",
+              "        [[[ 1.4004071 ],\n",
+              "          [ 1.4004071 ],\n",
+              "          [ 1.4004071 ],\n",
               "          ...,\n",
-              "          [ 0.32980254],\n",
-              "          [ 0.36644727],\n",
-              "          [ 0.36644727]],\n",
+              "          [ 9.297147  ],\n",
+              "          [ 9.802849  ],\n",
+              "          [ 9.802849  ]],\n",
               "\n",
-              "         [[ 1.5390785 ],\n",
-              "          [ 1.2825654 ],\n",
-              "          [ 1.3192102 ],\n",
+              "         [[ 1.3226067 ],\n",
+              "          [ 1.3226067 ],\n",
+              "          [ 1.3226067 ],\n",
               "          ...,\n",
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+              "          [ 9.834705  ],\n",
+              "          [ 9.834705  ],\n",
+              "          [ 9.834705  ]],\n",
               "\n",
-              "         [[ 0.91355103],\n",
-              "          [ 0.91355103],\n",
-              "          [ 0.91355103],\n",
+              "         [[ 0.99140173],\n",
+              "          [ 0.99140173],\n",
+              "          [ 0.99140173],\n",
               "          ...,\n",
-              "          [10.049061  ],\n",
-              "          [10.009341  ],\n",
-              "          [10.009341  ]]],\n",
+              "          [ 9.834705  ],\n",
+              "          [ 9.834705  ],\n",
+              "          [ 9.834705  ]]],\n",
               "\n",
               "\n",
-              "        [[[ 1.3901863 ],\n",
-              "          [ 1.3901863 ],\n",
-              "          [ 1.3901863 ],\n",
+              "        [[[ 1.4276185 ],\n",
+              "          [ 1.4276185 ],\n",
+              "          [ 1.4276185 ],\n",
               "          ...,\n",
-              "          [ 0.11915883],\n",
-              "          [ 0.15887843],\n",
-              "          [ 0.15887843]],\n",
+              "          [10.112297  ],\n",
+              "          [ 0.        ],\n",
+              "          [ 0.        ]],\n",
               "\n",
-              "         [[ 1.3901863 ],\n",
-              "          [ 1.3901863 ],\n",
-              "          [ 1.3901863 ],\n",
+              "         [[ 1.3879623 ],\n",
+              "          [ 1.3879623 ],\n",
+              "          [ 1.3879623 ],\n",
               "          ...,\n",
-              "          [ 0.11915883],\n",
-              "          [ 0.15887843],\n",
-              "          [ 0.15887843]],\n",
+              "          [10.072641  ],\n",
+              "          [10.112297  ],\n",
+              "          [10.112297  ]],\n",
               "\n",
-              "         [[ 1.3901863 ],\n",
-              "          [ 1.3901863 ],\n",
-              "          [ 1.3901863 ],\n",
+              "         [[ 1.3086503 ],\n",
+              "          [ 1.3086503 ],\n",
+              "          [ 1.2689942 ],\n",
               "          ...,\n",
-              "          [ 0.19859804],\n",
-              "          [ 0.11915883],\n",
-              "          [ 0.19859804]],\n",
+              "          [ 0.        ],\n",
+              "          [ 0.        ],\n",
+              "          [ 0.        ]],\n",
               "\n",
               "         ...,\n",
               "\n",
-              "         [[ 0.91355103],\n",
-              "          [ 0.91355103],\n",
-              "          [ 0.91355103],\n",
+              "         [[ 1.0310577 ],\n",
+              "          [ 0.99140173],\n",
+              "          [ 0.9517456 ],\n",
               "          ...,\n",
-              "          [10.049061  ],\n",
-              "          [10.049061  ],\n",
-              "          [10.009341  ]],\n",
+              "          [ 9.795049  ],\n",
+              "          [ 9.795049  ],\n",
+              "          [ 9.795049  ]],\n",
               "\n",
-              "         [[ 0.91355103],\n",
-              "          [ 0.91355103],\n",
-              "          [ 0.91355103],\n",
+              "         [[ 0.99140173],\n",
+              "          [ 0.99140173],\n",
+              "          [ 0.99140173],\n",
               "          ...,\n",
-              "          [10.049061  ],\n",
-              "          [10.009341  ],\n",
-              "          [10.009341  ]],\n",
+              "          [ 9.795049  ],\n",
+              "          [ 9.795049  ],\n",
+              "          [ 9.795049  ]],\n",
               "\n",
-              "         [[ 0.91355103],\n",
-              "          [ 0.91355103],\n",
-              "          [ 0.91355103],\n",
+              "         [[ 0.99140173],\n",
+              "          [ 0.99140173],\n",
+              "          [ 0.99140173],\n",
               "          ...,\n",
-              "          [10.009341  ],\n",
-              "          [10.009341  ],\n",
-              "          [10.009341  ]]],\n",
+              "          [ 9.795049  ],\n",
+              "          [ 9.795049  ],\n",
+              "          [ 9.795049  ]]],\n",
               "\n",
               "\n",
-              "        [[[ 1.3504667 ],\n",
-              "          [ 1.3504667 ],\n",
-              "          [ 1.3901863 ],\n",
+              "        [[[ 1.3483063 ],\n",
+              "          [ 1.3483063 ],\n",
+              "          [ 1.3879623 ],\n",
               "          ...,\n",
-              "          [ 0.07943922],\n",
-              "          [ 0.11915883],\n",
-              "          [ 0.11915883]],\n",
+              "          [ 0.        ],\n",
+              "          [ 0.        ],\n",
+              "          [ 0.        ]],\n",
               "\n",
-              "         [[ 1.3504667 ],\n",
-              "          [ 1.3504667 ],\n",
-              "          [ 1.3901863 ],\n",
+              "         [[ 1.3086503 ],\n",
+              "          [ 1.3086503 ],\n",
+              "          [ 1.3483063 ],\n",
               "          ...,\n",
-              "          [ 0.07943922],\n",
-              "          [ 0.11915883],\n",
-              "          [ 0.11915883]],\n",
+              "          [10.112297  ],\n",
+              "          [10.112297  ],\n",
+              "          [10.112297  ]],\n",
               "\n",
-              "         [[ 1.3107471 ],\n",
-              "          [ 1.3107471 ],\n",
-              "          [ 1.3504667 ],\n",
+              "         [[ 1.3483063 ],\n",
+              "          [ 1.3483063 ],\n",
+              "          [ 1.3086503 ],\n",
               "          ...,\n",
-              "          [ 0.19859804],\n",
-              "          [ 0.19859804],\n",
-              "          [ 0.19859804]],\n",
+              "          [10.112297  ],\n",
+              "          [10.112297  ],\n",
+              "          [10.112297  ]],\n",
               "\n",
               "         ...,\n",
               "\n",
-              "         [[ 0.8738314 ],\n",
-              "          [ 0.8738314 ],\n",
-              "          [ 0.8738314 ],\n",
+              "         [[ 1.0310577 ],\n",
+              "          [ 0.99140173],\n",
+              "          [ 0.9517456 ],\n",
               "          ...,\n",
-              "          [10.049061  ],\n",
-              "          [10.049061  ],\n",
-              "          [10.009341  ]],\n",
+              "          [ 9.795049  ],\n",
+              "          [ 9.795049  ],\n",
+              "          [ 9.795049  ]],\n",
               "\n",
-              "         [[ 0.8738314 ],\n",
-              "          [ 0.8738314 ],\n",
-              "          [ 0.8738314 ],\n",
+              "         [[ 0.99140173],\n",
+              "          [ 0.99140173],\n",
+              "          [ 0.99140173],\n",
               "          ...,\n",
-              "          [10.049061  ],\n",
-              "          [10.009341  ],\n",
-              "          [10.009341  ]],\n",
+              "          [ 9.795049  ],\n",
+              "          [ 9.795049  ],\n",
+              "          [ 9.795049  ]],\n",
               "\n",
-              "         [[ 0.8738314 ],\n",
-              "          [ 0.8738314 ],\n",
-              "          [ 0.8738314 ],\n",
+              "         [[ 0.9517456 ],\n",
+              "          [ 0.9517456 ],\n",
+              "          [ 0.9517456 ],\n",
               "          ...,\n",
-              "          [10.009341  ],\n",
-              "          [10.009341  ],\n",
-              "          [10.009341  ]]]]], dtype=float32)"
+              "          [ 9.795049  ],\n",
+              "          [ 9.795049  ],\n",
+              "          [ 9.795049  ]]]]], dtype=float32)"
             ]
           },
-          "execution_count": 30,
+          "execution_count": 29,
           "metadata": {},
           "output_type": "execute_result"
         }
@@ -2561,7 +1802,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 31,
+      "execution_count": 30,
       "id": "acf5eb4f-a0da-4a9a-bf24-af13e9cc2fbe",
       "metadata": {
         "colab": {
@@ -2576,87 +1817,81 @@
           "name": "stderr",
           "output_type": "stream",
           "text": [
-            "Lossy conversion from float32 to uint8. Range [0.0, 9.344405174255371]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 9.344405174255371]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 9.344405174255371]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 9.344405174255371]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 9.344405174255371]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 9.344405174255371]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 9.344405174255371]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 9.344405174255371]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 9.344405174255371]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 9.344405174255371]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 9.344405174255371]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 9.344405174255371]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 9.344405174255371]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 9.344405174255371]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 9.344405174255371]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 9.344405174255371]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 9.344405174255371]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 9.344405174255371]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 9.344405174255371]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 9.344405174255371]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 9.344405174255371]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 9.344405174255371]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 9.344405174255371]. Convert image to uint8 prior to saving to suppress this warning.\n"
-          ]
-        },
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            "Lossy conversion from float32 to uint8. Range [0.0, 9.344405174255371]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 9.344405174255371]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 9.344405174255371]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 9.344405174255371]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 9.344405174255371]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 9.344405174255371]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 9.344405174255371]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 9.344405174255371]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 9.344405174255371]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 9.344405174255371]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 9.344405174255371]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 9.344405174255371]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 9.344405174255371]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 9.344405174255371]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 9.344405174255371]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 9.344405174255371]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 9.344405174255371]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 9.344405174255371]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 9.344405174255371]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 9.344405174255371]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 9.344405174255371]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 9.344405174255371]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 9.344405174255371]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 9.344405174255371]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 9.344405174255371]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 9.344405174255371]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 9.344405174255371]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 9.344405174255371]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 9.344405174255371]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 9.344405174255371]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 9.344405174255371]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 9.344405174255371]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 9.344405174255371]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 9.344405174255371]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 9.344405174255371]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 9.344405174255371]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 9.344405174255371]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 9.344405174255371]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 9.344405174255371]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 9.344405174255371]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 9.344405174255371]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 9.344405174255371]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 9.344405174255371]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 9.344405174255371]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 9.344405174255371]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 9.344405174255371]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 9.344405174255371]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 9.344405174255371]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 9.344405174255371]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 9.344405174255371]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 9.344405174255371]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 9.344405174255371]. Convert image to uint8 prior to saving to suppress this warning.\n"
+            "Lossy conversion from float32 to uint8. Range [0.0, 9.919549942016602]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 9.919549942016602]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 9.919549942016602]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 9.919549942016602]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 9.919549942016602]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 9.919549942016602]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 9.919549942016602]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 9.919549942016602]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 9.919549942016602]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 9.919549942016602]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 9.919549942016602]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 9.919549942016602]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 9.919549942016602]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 9.919549942016602]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 9.919549942016602]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 9.919549942016602]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 9.919549942016602]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 9.919549942016602]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 9.919549942016602]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 9.919549942016602]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 9.919549942016602]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 9.919549942016602]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 9.919549942016602]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 9.919549942016602]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 9.919549942016602]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 9.919549942016602]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 9.919549942016602]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 9.919549942016602]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 9.919549942016602]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 9.919549942016602]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 9.919549942016602]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 9.919549942016602]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 9.919549942016602]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 9.919549942016602]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 9.919549942016602]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 9.919549942016602]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 9.919549942016602]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 9.919549942016602]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 9.919549942016602]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 9.919549942016602]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 9.919549942016602]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 9.919549942016602]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 9.919549942016602]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 9.919549942016602]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 9.919549942016602]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 9.919549942016602]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 9.919549942016602]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 9.919549942016602]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 9.919549942016602]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 9.919549942016602]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 9.919549942016602]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 9.919549942016602]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 9.919549942016602]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 9.919549942016602]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 9.919549942016602]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 9.919549942016602]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 9.919549942016602]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 9.919549942016602]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 9.919549942016602]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 9.919549942016602]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 9.919549942016602]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 9.919549942016602]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 9.919549942016602]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 9.919549942016602]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 9.919549942016602]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 9.919549942016602]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 9.919549942016602]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 9.919549942016602]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 9.919549942016602]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 9.919549942016602]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 9.919549942016602]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 9.919549942016602]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 9.919549942016602]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 9.919549942016602]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 9.919549942016602]. Convert image to uint8 prior to saving to suppress this warning.\n"
           ]
         }
       ],
@@ -2666,7 +1901,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 32,
+      "execution_count": 31,
       "id": "c33a87a2-d5e0-4ec9-b174-73ebf41bf03a",
       "metadata": {
         "colab": {
@@ -2681,16 +1916,16 @@
         {
           "data": {
             "text/plain": [
-              ""
+              ""
             ]
           },
-          "execution_count": 32,
+          "execution_count": 31,
           "metadata": {},
           "output_type": "execute_result"
         },
         {
           "data": {
-            "image/png": 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",
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",
             "text/plain": [
               "
"
             ]
@@ -2706,7 +1941,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 33,
+      "execution_count": 32,
       "id": "84593332-133c-4205-b7a6-8e235d5e2b3b",
       "metadata": {
         "colab": {
@@ -2720,10 +1955,10 @@
         {
           "data": {
             "text/plain": [
-              ""
+              ""
             ]
           },
-          "execution_count": 33,
+          "execution_count": 32,
           "metadata": {},
           "output_type": "execute_result"
         }
@@ -2745,7 +1980,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 34,
+      "execution_count": 33,
       "id": "d8e9a497-191b-4842-afbd-26f5e13c43ba",
       "metadata": {
         "id": "d8e9a497-191b-4842-afbd-26f5e13c43ba",
@@ -2753,15 +1988,21 @@
       },
       "outputs": [],
       "source": [
-        "from tensorflow.keras.models import Sequential\n",
-        "from tensorflow.keras.layers import Conv3D, LSTM, Dense, Dropout, Bidirectional, MaxPool3D, Activation, Reshape, SpatialDropout3D, BatchNormalization, TimeDistributed, Flatten\n",
-        "from tensorflow.keras.optimizers import Adam\n",
-        "from tensorflow.keras.callbacks import ModelCheckpoint, LearningRateScheduler"
+        "from tensorflow import keras\n",
+        "# from tensorflow.keras.models import Sequential\n",
+        "# from tensorflow.keras.layers import Conv3D, LSTM, Dense, Dropout, Bidirectional, MaxPool3D, Activation, Reshape, SpatialDropout3D, BatchNormalization, TimeDistributed, Flatten\n",
+        "# from tensorflow.keras.optimizers import Adam\n",
+        "# from tensorflow.keras.callbacks import ModelCheckpoint, LearningRateScheduler\n",
+        "\n",
+        "from keras.models import Sequential\n",
+        "from keras.layers import Conv3D, LSTM, Dense, Dropout, Bidirectional, MaxPool3D, Activation, Reshape, SpatialDropout3D, BatchNormalization, TimeDistributed, Flatten\n",
+        "from keras.optimizers import Adam\n",
+        "from keras.callbacks import ModelCheckpoint, LearningRateScheduler"
       ]
     },
     {
       "cell_type": "code",
-      "execution_count": 35,
+      "execution_count": 34,
       "id": "5d376ebe",
       "metadata": {},
       "outputs": [],
@@ -2772,7 +2013,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 36,
+      "execution_count": 35,
       "id": "3f753ed2-70b9-4236-8c1c-08ca065dc8bf",
       "metadata": {
         "colab": {
@@ -2788,7 +2029,7 @@
               "(75, 46, 140, 1)"
             ]
           },
-          "execution_count": 36,
+          "execution_count": 35,
           "metadata": {},
           "output_type": "execute_result"
         }
@@ -2799,7 +2040,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 37,
+      "execution_count": 36,
       "id": "f9171056-a352-491a-9ed9-92b28ced268e",
       "metadata": {
         "id": "f9171056-a352-491a-9ed9-92b28ced268e",
@@ -2810,7 +2051,7 @@
           "name": "stderr",
           "output_type": "stream",
           "text": [
-            "C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\layers\\convolutional\\base_conv.py:99: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
+            "c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\layers\\convolutional\\base_conv.py:99: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
             "  super().__init__(\n"
           ]
         }
@@ -2832,22 +2073,22 @@
         "\n",
         "model.add(TimeDistributed(Flatten()))\n",
         "\n",
-        "# model.add(Bidirectional(LSTM(128, kernel_initializer='Orthogonal', return_sequences=True)))\n",
-        "# Now use the Orthogonal initializer directly when adding your layer\n",
-        "model.add(Bidirectional(LSTM(128, kernel_initializer=Orthogonal(), return_sequences=True)))\n",
-        "model.add(Dropout(.5))\n",
+        "model.add(Bidirectional(LSTM(128, return_sequences=True)))\n",
         "\n",
-        "# model.add(Bidirectional(LSTM(128, kernel_initializer='Orthogonal', return_sequences=True)))\n",
-        "# Now use the Orthogonal initializer directly when adding your layer\n",
-        "model.add(Bidirectional(LSTM(128, kernel_initializer=Orthogonal(), return_sequences=True)))\n",
-        "model.add(Dropout(.5))\n",
+        "# # model.add(Bidirectional(LSTM(128, kernel_initializer='Orthogonal', return_sequences=True)))\n",
+        "# model.add(Bidirectional(LSTM(128, kernel_initializer=Orthogonal(), return_sequences=True)))\n",
+        "# model.add(Dropout(.5))\n",
+        "\n",
+        "# # model.add(Bidirectional(LSTM(128, kernel_initializer='Orthogonal', return_sequences=True)))\n",
+        "# model.add(Bidirectional(LSTM(128, kernel_initializer=Orthogonal(), return_sequences=True)))\n",
+        "# model.add(Dropout(.5))\n",
         "\n",
         "model.add(Dense(char_to_num.vocabulary_size()+1, kernel_initializer='he_normal', activation='softmax'))"
       ]
     },
     {
       "cell_type": "code",
-      "execution_count": 38,
+      "execution_count": 37,
       "id": "78851825-2bcd-42a9-b7f2-28bb5a6bf43a",
       "metadata": {
         "colab": {
@@ -2910,12 +2151,6 @@
               "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
               "│ bidirectional (Bidirectional)   │ (None, 75, 256)        │     6,660,096 │\n",
               "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-              "│ dropout (Dropout)               │ (None, 75, 256)        │             0 │\n",
-              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-              "│ bidirectional_1 (Bidirectional) │ (None, 75, 256)        │       394,240 │\n",
-              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-              "│ dropout_1 (Dropout)             │ (None, 75, 256)        │             0 │\n",
-              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
               "│ dense (Dense)                   │ (None, 75, 41)         │        10,537 │\n",
               "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
               "
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\n" @@ -2953,12 +2188,6 @@ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", "│ bidirectional (\u001b[38;5;33mBidirectional\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m6,660,096\u001b[0m │\n", "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", - "│ dropout (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n", - "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", - "│ bidirectional_1 (\u001b[38;5;33mBidirectional\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m394,240\u001b[0m │\n", - "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", - "│ dropout_1 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n", - "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", "│ dense (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m41\u001b[0m) │ \u001b[38;5;34m10,537\u001b[0m │\n", "└─────────────────────────────────┴────────────────────────┴───────────────┘\n" ] @@ -2969,11 +2198,11 @@ { "data": { "text/html": [ - "
 Total params: 8,471,924 (32.32 MB)\n",
+              "
 Total params: 8,077,684 (30.81 MB)\n",
               "
\n"
             ],
             "text/plain": [
-              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m8,471,924\u001b[0m (32.32 MB)\n"
+              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m8,077,684\u001b[0m (30.81 MB)\n"
             ]
           },
           "metadata": {},
@@ -2982,11 +2211,11 @@
         {
           "data": {
             "text/html": [
-              "
 Trainable params: 8,471,924 (32.32 MB)\n",
+              "
 Trainable params: 8,077,684 (30.81 MB)\n",
               "
\n"
             ],
             "text/plain": [
-              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m8,471,924\u001b[0m (32.32 MB)\n"
+              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m8,077,684\u001b[0m (30.81 MB)\n"
             ]
           },
           "metadata": {},
@@ -3012,7 +2241,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 39,
+      "execution_count": 38,
       "id": "f4b4798c-a65a-4c47-9e2a-3b09dc98d320",
       "metadata": {
         "colab": {
@@ -3028,7 +2257,7 @@
               "6375"
             ]
           },
-          "execution_count": 39,
+          "execution_count": 38,
           "metadata": {},
           "output_type": "execute_result"
         }
@@ -3039,7 +2268,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 40,
+      "execution_count": 39,
       "id": "e5c2eae0-c359-41a4-97a0-75c44dccb7d1",
       "metadata": {
         "colab": {
@@ -3054,7 +2283,7 @@
           "name": "stdout",
           "output_type": "stream",
           "text": [
-            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 4s/step\n"
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2s/step\n"
           ]
         }
       ],
@@ -3064,7 +2293,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 41,
+      "execution_count": 40,
       "id": "ffdc7319-0d69-4f7e-a6d4-ce72deb81c0b",
       "metadata": {
         "colab": {
@@ -3077,10 +2306,10 @@
         {
           "data": {
             "text/plain": [
-              ""
+              ""
             ]
           },
-          "execution_count": 41,
+          "execution_count": 40,
           "metadata": {},
           "output_type": "execute_result"
         }
@@ -3091,7 +2320,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 42,
+      "execution_count": 41,
       "id": "6ed47531-8317-4255-9a12-b757642258e6",
       "metadata": {
         "colab": {
@@ -3105,10 +2334,10 @@
         {
           "data": {
             "text/plain": [
-              ""
+              ""
             ]
           },
-          "execution_count": 42,
+          "execution_count": 41,
           "metadata": {},
           "output_type": "execute_result"
         }
@@ -3119,7 +2348,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 43,
+      "execution_count": 42,
       "id": "7c37b9b9-5298-4038-9c33-5031d1b457f0",
       "metadata": {
         "colab": {
@@ -3136,7 +2365,7 @@
               "(None, 75, 46, 140, 1)"
             ]
           },
-          "execution_count": 43,
+          "execution_count": 42,
           "metadata": {},
           "output_type": "execute_result"
         }
@@ -3147,7 +2376,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 44,
+      "execution_count": 43,
       "id": "98b316a4-5322-4782-8e36-4b3c1a696d85",
       "metadata": {
         "colab": {
@@ -3164,7 +2393,7 @@
               "(None, 75, 41)"
             ]
           },
-          "execution_count": 44,
+          "execution_count": 43,
           "metadata": {},
           "output_type": "execute_result"
         }
@@ -3186,7 +2415,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 45,
+      "execution_count": 44,
       "id": "ab015fd0-7fb4-4d5d-9fa2-30a05dbd515a",
       "metadata": {
         "id": "ab015fd0-7fb4-4d5d-9fa2-30a05dbd515a",
@@ -3203,7 +2432,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 46,
+      "execution_count": 45,
       "id": "c564d5c9-db54-4e88-b311-9aeab7fb3e69",
       "metadata": {
         "id": "c564d5c9-db54-4e88-b311-9aeab7fb3e69",
@@ -3219,13 +2448,14 @@
         "    input_length = input_length * tf.ones(shape=(batch_len, 1), dtype=\"int64\")\n",
         "    label_length = label_length * tf.ones(shape=(batch_len, 1), dtype=\"int64\")\n",
         "\n",
-        "    loss = tf.keras.backend.ctc_batch_cost(y_true, y_pred, input_length, label_length)\n",
+        "    # loss = tf.keras.backend.ctc_batch_cost(y_true, y_pred, input_length, label_length)\n",
+        "    loss = keras.backend.ctc_batch_cost(y_true, y_pred, input_length, label_length)\n",
         "    return loss"
       ]
     },
     {
       "cell_type": "code",
-      "execution_count": 47,
+      "execution_count": 46,
       "id": "a26dc3fc-a19c-4378-bd8c-e2b597a1d15c",
       "metadata": {
         "id": "a26dc3fc-a19c-4378-bd8c-e2b597a1d15c",
@@ -3233,23 +2463,36 @@
       },
       "outputs": [],
       "source": [
-        "class ProduceExample(tf.keras.callbacks.Callback):\n",
+        "class ProduceExample(keras.callbacks.Callback):\n",
         "    def __init__(self, dataset) -> None:\n",
         "        self.dataset = dataset.as_numpy_iterator()\n",
         "\n",
         "    def on_epoch_end(self, epoch, logs=None) -> None:\n",
         "        data = self.dataset.next()\n",
         "        yhat = self.model.predict(data[0])\n",
-        "        decoded = tf.keras.backend.ctc_decode(yhat, [75,75], greedy=False)[0][0].numpy()\n",
+        "        decoded = keras.backend.ctc_decode(yhat, [75,75], greedy=False)[0][0].numpy()\n",
         "        for x in range(len(yhat)):\n",
         "            print('Original:', tf.strings.reduce_join(num_to_char(data[1][x])).numpy().decode('utf-8'))\n",
         "            print('Prediction:', tf.strings.reduce_join(num_to_char(decoded[x])).numpy().decode('utf-8'))\n",
-        "            print('~'*100)"
+        "            print('~'*100)\n",
+        "\n",
+        "# class ProduceExample(tf.keras.callbacks.Callback):\n",
+        "#     def __init__(self, dataset) -> None:\n",
+        "#         self.dataset = dataset.as_numpy_iterator()\n",
+        "\n",
+        "#     def on_epoch_end(self, epoch, logs=None) -> None:\n",
+        "#         data = self.dataset.next()\n",
+        "#         yhat = self.model.predict(data[0])\n",
+        "#         decoded = tf.keras.backend.ctc_decode(yhat, [75,75], greedy=False)[0][0].numpy()\n",
+        "#         for x in range(len(yhat)):\n",
+        "#             print('Original:', tf.strings.reduce_join(num_to_char(data[1][x])).numpy().decode('utf-8'))\n",
+        "#             print('Prediction:', tf.strings.reduce_join(num_to_char(decoded[x])).numpy().decode('utf-8'))\n",
+        "#             print('~'*100)"
       ]
     },
     {
       "cell_type": "code",
-      "execution_count": 48,
+      "execution_count": 47,
       "id": "04be90d8-2482-46f9-b513-d5f4f8001c7e",
       "metadata": {
         "id": "04be90d8-2482-46f9-b513-d5f4f8001c7e",
@@ -3262,7 +2505,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 49,
+      "execution_count": 48,
       "id": "eab49367-3f1e-4464-ae76-dbd07549d97e",
       "metadata": {
         "id": "eab49367-3f1e-4464-ae76-dbd07549d97e",
@@ -3271,8 +2514,7 @@
       "outputs": [],
       "source": [
         "# checkpoint_callback = ModelCheckpoint(os.path.join('models','checkpoint'), monitor='loss', save_weights_only=True)\n",
-        "from keras.callbacks import ModelCheckpoint\n",
-        "import os\n",
+        "\n",
         "\n",
         "# Adjust the file path to end with '.weights.h5'\n",
         "checkpoint_path = os.path.join('models', 'checkpoint.weights.h5')\n",
@@ -3287,7 +2529,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 50,
+      "execution_count": 49,
       "id": "e085a632-d464-46ef-8777-959cad4adb2c",
       "metadata": {
         "id": "e085a632-d464-46ef-8777-959cad4adb2c",
@@ -3300,7 +2542,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 51,
+      "execution_count": 50,
       "id": "48eca991-90ab-4592-8a79-b50e9ca015b6",
       "metadata": {
         "id": "48eca991-90ab-4592-8a79-b50e9ca015b6",
@@ -3331,25 +2573,17 @@
             "Epoch 1/100\n"
           ]
         },
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "WARNING:tensorflow:From C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\legacy\\backend.py:666: The name tf.nn.ctc_loss is deprecated. Please use tf.compat.v1.nn.ctc_loss instead.\n",
-            "\n"
-          ]
-        },
         {
           "ename": "InvalidArgumentError",
-          "evalue": "Graph execution error:\n\nDetected at node sequential_1/time_distributed_1/Reshape_50 defined at (most recent call last):\n  File \"\", line 198, in _run_module_as_main\n\n  File \"\", line 88, in _run_code\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\ipykernel_launcher.py\", line 18, in \n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\traitlets\\config\\application.py\", line 1075, in launch_instance\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\ipykernel\\kernelapp.py\", line 739, in start\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\tornado\\platform\\asyncio.py\", line 205, in start\n\n  File \"C:\\Program Files\\WindowsApps\\PythonSoftwareFoundation.Python.3.12_3.12.752.0_x64__qbz5n2kfra8p0\\Lib\\asyncio\\base_events.py\", line 639, in run_forever\n\n  File \"C:\\Program Files\\WindowsApps\\PythonSoftwareFoundation.Python.3.12_3.12.752.0_x64__qbz5n2kfra8p0\\Lib\\asyncio\\base_events.py\", line 1985, in _run_once\n\n  File \"C:\\Program Files\\WindowsApps\\PythonSoftwareFoundation.Python.3.12_3.12.752.0_x64__qbz5n2kfra8p0\\Lib\\asyncio\\events.py\", line 88, in _run\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\ipykernel\\kernelbase.py\", line 545, in dispatch_queue\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\ipykernel\\kernelbase.py\", line 534, in process_one\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\ipykernel\\kernelbase.py\", line 437, in dispatch_shell\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\ipykernel\\ipkernel.py\", line 362, in execute_request\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\ipykernel\\kernelbase.py\", line 778, in execute_request\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\ipykernel\\ipkernel.py\", line 449, in do_execute\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\ipykernel\\zmqshell.py\", line 549, in run_cell\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\IPython\\core\\interactiveshell.py\", line 3075, in run_cell\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\IPython\\core\\interactiveshell.py\", line 3130, in _run_cell\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\IPython\\core\\async_helpers.py\", line 129, in _pseudo_sync_runner\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\IPython\\core\\interactiveshell.py\", line 3334, in run_cell_async\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\IPython\\core\\interactiveshell.py\", line 3517, in run_ast_nodes\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\IPython\\core\\interactiveshell.py\", line 3577, in run_code\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Temp\\ipykernel_10024\\3536403359.py\", line 1, in \n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 117, in error_handler\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\backend\\tensorflow\\trainer.py\", line 329, in fit\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\backend\\tensorflow\\trainer.py\", line 122, in one_step_on_iterator\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\backend\\tensorflow\\trainer.py\", line 110, in one_step_on_data\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\backend\\tensorflow\\trainer.py\", line 57, in train_step\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 117, in error_handler\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\layers\\layer.py\", line 825, in __call__\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 117, in error_handler\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\ops\\operation.py\", line 48, in __call__\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 156, in error_handler\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\models\\sequential.py\", line 202, in call\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\models\\functional.py\", line 195, in call\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\ops\\function.py\", line 151, in _run_through_graph\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\models\\functional.py\", line 579, in call\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 117, in error_handler\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\layers\\layer.py\", line 825, in __call__\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 117, in error_handler\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\ops\\operation.py\", line 48, in __call__\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 156, in error_handler\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\layers\\rnn\\time_distributed.py\", line 108, in call\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\layers\\rnn\\time_distributed.py\", line 102, in step_function\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\layers\\reshaping\\flatten.py\", line 54, in call\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\ops\\numpy.py\", line 4522, in reshape\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\backend\\tensorflow\\numpy.py\", line 1618, in reshape\n\nOnly one input size may be -1, not both 0 and 1\n\t [[{{node sequential_1/time_distributed_1/Reshape_50}}]] [Op:__inference_one_step_on_iterator_31137]",
+          "evalue": "Graph execution error:\n\nDetected at node sequential_1/time_distributed_1/Reshape_43 defined at (most recent call last):\n  File \"\", line 198, in _run_module_as_main\n\n  File \"\", line 88, in _run_code\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel_launcher.py\", line 18, in \n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\traitlets\\config\\application.py\", line 1075, in launch_instance\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel\\kernelapp.py\", line 739, in start\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\tornado\\platform\\asyncio.py\", line 205, in start\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\asyncio\\base_events.py\", line 641, in run_forever\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\asyncio\\base_events.py\", line 1987, in _run_once\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\asyncio\\events.py\", line 88, in _run\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 545, in dispatch_queue\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 534, in process_one\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 437, in dispatch_shell\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel\\ipkernel.py\", line 362, in execute_request\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 778, in execute_request\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel\\ipkernel.py\", line 449, in do_execute\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel\\zmqshell.py\", line 549, in run_cell\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3075, in run_cell\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3130, in _run_cell\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\async_helpers.py\", line 129, in _pseudo_sync_runner\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3334, in run_cell_async\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3517, in run_ast_nodes\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3577, in run_code\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Temp\\ipykernel_17328\\3536403359.py\", line 1, in \n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 117, in error_handler\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\backend\\tensorflow\\trainer.py\", line 329, in fit\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\backend\\tensorflow\\trainer.py\", line 122, in one_step_on_iterator\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\backend\\tensorflow\\trainer.py\", line 110, in one_step_on_data\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\backend\\tensorflow\\trainer.py\", line 57, in train_step\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 117, in error_handler\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\layers\\layer.py\", line 825, in __call__\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 117, in error_handler\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\ops\\operation.py\", line 48, in __call__\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 156, in error_handler\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\models\\sequential.py\", line 202, in call\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\models\\functional.py\", line 195, in call\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\ops\\function.py\", line 151, in _run_through_graph\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\models\\functional.py\", line 579, in call\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 117, in error_handler\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\layers\\layer.py\", line 825, in __call__\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 117, in error_handler\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\ops\\operation.py\", line 48, in __call__\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 156, in error_handler\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\layers\\rnn\\time_distributed.py\", line 108, in call\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\layers\\rnn\\time_distributed.py\", line 102, in step_function\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\layers\\reshaping\\flatten.py\", line 54, in call\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\ops\\numpy.py\", line 4522, in reshape\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\backend\\tensorflow\\numpy.py\", line 1618, in reshape\n\nOnly one input size may be -1, not both 0 and 1\n\t [[{{node sequential_1/time_distributed_1/Reshape_43}}]] [Op:__inference_one_step_on_iterator_27512]",
           "output_type": "error",
           "traceback": [
             "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
             "\u001b[1;31mInvalidArgumentError\u001b[0m                      Traceback (most recent call last)",
             "Cell \u001b[1;32mIn[52], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtrain\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalidation_data\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtest\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mepochs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m100\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcallbacks\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[43mcheckpoint_callback\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mschedule_callback\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mexample_callback\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n",
-            "File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\utils\\traceback_utils.py:122\u001b[0m, in \u001b[0;36mfilter_traceback..error_handler\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    119\u001b[0m     filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n\u001b[0;32m    120\u001b[0m     \u001b[38;5;66;03m# To get the full stack trace, call:\u001b[39;00m\n\u001b[0;32m    121\u001b[0m     \u001b[38;5;66;03m# `keras.config.disable_traceback_filtering()`\u001b[39;00m\n\u001b[1;32m--> 122\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m e\u001b[38;5;241m.\u001b[39mwith_traceback(filtered_tb) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m    123\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[0;32m    124\u001b[0m     \u001b[38;5;28;01mdel\u001b[39;00m filtered_tb\n",
-            "File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\tensorflow\\python\\eager\\execute.py:53\u001b[0m, in \u001b[0;36mquick_execute\u001b[1;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[0;32m     51\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m     52\u001b[0m   ctx\u001b[38;5;241m.\u001b[39mensure_initialized()\n\u001b[1;32m---> 53\u001b[0m   tensors \u001b[38;5;241m=\u001b[39m pywrap_tfe\u001b[38;5;241m.\u001b[39mTFE_Py_Execute(ctx\u001b[38;5;241m.\u001b[39m_handle, device_name, op_name,\n\u001b[0;32m     54\u001b[0m                                       inputs, attrs, num_outputs)\n\u001b[0;32m     55\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m core\u001b[38;5;241m.\u001b[39m_NotOkStatusException \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m     56\u001b[0m   \u001b[38;5;28;01mif\u001b[39;00m name \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
-            "\u001b[1;31mInvalidArgumentError\u001b[0m: Graph execution error:\n\nDetected at node sequential_1/time_distributed_1/Reshape_50 defined at (most recent call last):\n  File \"\", line 198, in _run_module_as_main\n\n  File \"\", line 88, in _run_code\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\ipykernel_launcher.py\", line 18, in \n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\traitlets\\config\\application.py\", line 1075, in launch_instance\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\ipykernel\\kernelapp.py\", line 739, in start\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\tornado\\platform\\asyncio.py\", line 205, in start\n\n  File \"C:\\Program Files\\WindowsApps\\PythonSoftwareFoundation.Python.3.12_3.12.752.0_x64__qbz5n2kfra8p0\\Lib\\asyncio\\base_events.py\", line 639, in run_forever\n\n  File \"C:\\Program Files\\WindowsApps\\PythonSoftwareFoundation.Python.3.12_3.12.752.0_x64__qbz5n2kfra8p0\\Lib\\asyncio\\base_events.py\", line 1985, in _run_once\n\n  File \"C:\\Program Files\\WindowsApps\\PythonSoftwareFoundation.Python.3.12_3.12.752.0_x64__qbz5n2kfra8p0\\Lib\\asyncio\\events.py\", line 88, in _run\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\ipykernel\\kernelbase.py\", line 545, in dispatch_queue\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\ipykernel\\kernelbase.py\", line 534, in process_one\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\ipykernel\\kernelbase.py\", line 437, in dispatch_shell\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\ipykernel\\ipkernel.py\", line 362, in execute_request\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\ipykernel\\kernelbase.py\", line 778, in execute_request\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\ipykernel\\ipkernel.py\", line 449, in do_execute\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\ipykernel\\zmqshell.py\", line 549, in run_cell\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\IPython\\core\\interactiveshell.py\", line 3075, in run_cell\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\IPython\\core\\interactiveshell.py\", line 3130, in _run_cell\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\IPython\\core\\async_helpers.py\", line 129, in _pseudo_sync_runner\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\IPython\\core\\interactiveshell.py\", line 3334, in run_cell_async\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\IPython\\core\\interactiveshell.py\", line 3517, in run_ast_nodes\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\IPython\\core\\interactiveshell.py\", line 3577, in run_code\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Temp\\ipykernel_10024\\3536403359.py\", line 1, in \n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 117, in error_handler\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\backend\\tensorflow\\trainer.py\", line 329, in fit\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\backend\\tensorflow\\trainer.py\", line 122, in one_step_on_iterator\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\backend\\tensorflow\\trainer.py\", line 110, in one_step_on_data\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\backend\\tensorflow\\trainer.py\", line 57, in train_step\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 117, in error_handler\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\layers\\layer.py\", line 825, in __call__\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 117, in error_handler\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\ops\\operation.py\", line 48, in __call__\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 156, in error_handler\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\models\\sequential.py\", line 202, in call\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\models\\functional.py\", line 195, in call\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\ops\\function.py\", line 151, in _run_through_graph\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\models\\functional.py\", line 579, in call\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 117, in error_handler\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\layers\\layer.py\", line 825, in __call__\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 117, in error_handler\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\ops\\operation.py\", line 48, in __call__\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 156, in error_handler\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\layers\\rnn\\time_distributed.py\", line 108, in call\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\layers\\rnn\\time_distributed.py\", line 102, in step_function\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\layers\\reshaping\\flatten.py\", line 54, in call\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\ops\\numpy.py\", line 4522, in reshape\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\backend\\tensorflow\\numpy.py\", line 1618, in reshape\n\nOnly one input size may be -1, not both 0 and 1\n\t [[{{node sequential_1/time_distributed_1/Reshape_50}}]] [Op:__inference_one_step_on_iterator_31137]"
+            "File \u001b[1;32mc:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\utils\\traceback_utils.py:122\u001b[0m, in \u001b[0;36mfilter_traceback..error_handler\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    119\u001b[0m     filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n\u001b[0;32m    120\u001b[0m     \u001b[38;5;66;03m# To get the full stack trace, call:\u001b[39;00m\n\u001b[0;32m    121\u001b[0m     \u001b[38;5;66;03m# `keras.config.disable_traceback_filtering()`\u001b[39;00m\n\u001b[1;32m--> 122\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m e\u001b[38;5;241m.\u001b[39mwith_traceback(filtered_tb) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m    123\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[0;32m    124\u001b[0m     \u001b[38;5;28;01mdel\u001b[39;00m filtered_tb\n",
+            "File \u001b[1;32mc:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\tensorflow\\python\\eager\\execute.py:53\u001b[0m, in \u001b[0;36mquick_execute\u001b[1;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[0;32m     51\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m     52\u001b[0m   ctx\u001b[38;5;241m.\u001b[39mensure_initialized()\n\u001b[1;32m---> 53\u001b[0m   tensors \u001b[38;5;241m=\u001b[39m pywrap_tfe\u001b[38;5;241m.\u001b[39mTFE_Py_Execute(ctx\u001b[38;5;241m.\u001b[39m_handle, device_name, op_name,\n\u001b[0;32m     54\u001b[0m                                       inputs, attrs, num_outputs)\n\u001b[0;32m     55\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m core\u001b[38;5;241m.\u001b[39m_NotOkStatusException \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m     56\u001b[0m   \u001b[38;5;28;01mif\u001b[39;00m name \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
+            "\u001b[1;31mInvalidArgumentError\u001b[0m: Graph execution error:\n\nDetected at node sequential_1/time_distributed_1/Reshape_43 defined at (most recent call last):\n  File \"\", line 198, in _run_module_as_main\n\n  File \"\", line 88, in _run_code\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel_launcher.py\", line 18, in \n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\traitlets\\config\\application.py\", line 1075, in launch_instance\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel\\kernelapp.py\", line 739, in start\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\tornado\\platform\\asyncio.py\", line 205, in start\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\asyncio\\base_events.py\", line 641, in run_forever\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\asyncio\\base_events.py\", line 1987, in _run_once\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\asyncio\\events.py\", line 88, in _run\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 545, in dispatch_queue\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 534, in process_one\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 437, in dispatch_shell\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel\\ipkernel.py\", line 362, in execute_request\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 778, in execute_request\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel\\ipkernel.py\", line 449, in do_execute\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel\\zmqshell.py\", line 549, in run_cell\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3075, in run_cell\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3130, in _run_cell\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\async_helpers.py\", line 129, in _pseudo_sync_runner\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3334, in run_cell_async\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3517, in run_ast_nodes\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3577, in run_code\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Temp\\ipykernel_17328\\3536403359.py\", line 1, in \n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 117, in error_handler\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\backend\\tensorflow\\trainer.py\", line 329, in fit\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\backend\\tensorflow\\trainer.py\", line 122, in one_step_on_iterator\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\backend\\tensorflow\\trainer.py\", line 110, in one_step_on_data\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\backend\\tensorflow\\trainer.py\", line 57, in train_step\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 117, in error_handler\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\layers\\layer.py\", line 825, in __call__\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 117, in error_handler\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\ops\\operation.py\", line 48, in __call__\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 156, in error_handler\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\models\\sequential.py\", line 202, in call\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\models\\functional.py\", line 195, in call\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\ops\\function.py\", line 151, in _run_through_graph\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\models\\functional.py\", line 579, in call\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 117, in error_handler\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\layers\\layer.py\", line 825, in __call__\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 117, in error_handler\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\ops\\operation.py\", line 48, in __call__\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 156, in error_handler\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\layers\\rnn\\time_distributed.py\", line 108, in call\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\layers\\rnn\\time_distributed.py\", line 102, in step_function\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\layers\\reshaping\\flatten.py\", line 54, in call\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\ops\\numpy.py\", line 4522, in reshape\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\backend\\tensorflow\\numpy.py\", line 1618, in reshape\n\nOnly one input size may be -1, not both 0 and 1\n\t [[{{node sequential_1/time_distributed_1/Reshape_43}}]] [Op:__inference_one_step_on_iterator_27512]"
           ]
         }
       ],
@@ -3370,7 +2604,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 53,
+      "execution_count": null,
       "id": "01fa7204-ce0e-49a8-8dbd-14fe5dfead40",
       "metadata": {
         "id": "01fa7204-ce0e-49a8-8dbd-14fe5dfead40",
@@ -3383,9 +2617,9 @@
           "text": [
             "Downloading...\n",
             "From (original): https://drive.google.com/uc?id=1vWscXs4Vt0a_1IH1-ct2TCgXAZT-N3_Y\n",
-            "From (redirected): https://drive.google.com/uc?id=1vWscXs4Vt0a_1IH1-ct2TCgXAZT-N3_Y&confirm=t&uuid=62e39af9-6b13-4dc5-b6b6-9e6a803e62e5\n",
+            "From (redirected): https://drive.google.com/uc?id=1vWscXs4Vt0a_1IH1-ct2TCgXAZT-N3_Y&confirm=t&uuid=28aca149-41af-49fd-b783-ca9b4b4cc840\n",
             "To: c:\\haidun.li\\NSCC\\LipNet\\checkpoints.zip\n",
-            "100%|██████████| 94.5M/94.5M [00:02<00:00, 41.5MB/s]\n"
+            "100%|██████████| 94.5M/94.5M [00:05<00:00, 17.1MB/s]\n"
           ]
         },
         {
@@ -3399,21 +2633,21 @@
               " 'models\\\\__MACOSX/._checkpoint']"
             ]
           },
-          "execution_count": 53,
+          "execution_count": 54,
           "metadata": {},
           "output_type": "execute_result"
         }
       ],
       "source": [
-        "url = 'https://drive.google.com/uc?id=1vWscXs4Vt0a_1IH1-ct2TCgXAZT-N3_Y'\n",
-        "output = 'checkpoints.zip'\n",
-        "gdown.download(url, output, quiet=False)\n",
-        "gdown.extractall('checkpoints.zip', 'models')"
+        "# url = 'https://drive.google.com/uc?id=1vWscXs4Vt0a_1IH1-ct2TCgXAZT-N3_Y'\n",
+        "# output = 'checkpoints.zip'\n",
+        "# gdown.download(url, output, quiet=False)\n",
+        "# gdown.extractall('checkpoints.zip', 'models')"
       ]
     },
     {
       "cell_type": "code",
-      "execution_count": 57,
+      "execution_count": null,
       "id": "247f664d-3c87-4e96-946e-930dad0e1c2c",
       "metadata": {
         "id": "247f664d-3c87-4e96-946e-930dad0e1c2c",
@@ -3428,8 +2662,8 @@
             "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
             "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
             "Cell \u001b[1;32mIn[57], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mload_weights\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m./models/checkpoint\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n",
-            "File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\utils\\traceback_utils.py:122\u001b[0m, in \u001b[0;36mfilter_traceback..error_handler\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    119\u001b[0m     filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n\u001b[0;32m    120\u001b[0m     \u001b[38;5;66;03m# To get the full stack trace, call:\u001b[39;00m\n\u001b[0;32m    121\u001b[0m     \u001b[38;5;66;03m# `keras.config.disable_traceback_filtering()`\u001b[39;00m\n\u001b[1;32m--> 122\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m e\u001b[38;5;241m.\u001b[39mwith_traceback(filtered_tb) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m    123\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[0;32m    124\u001b[0m     \u001b[38;5;28;01mdel\u001b[39;00m filtered_tb\n",
-            "File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\saving\\saving_api.py:260\u001b[0m, in \u001b[0;36mload_weights\u001b[1;34m(model, filepath, skip_mismatch, **kwargs)\u001b[0m\n\u001b[0;32m    258\u001b[0m             legacy_h5_format\u001b[38;5;241m.\u001b[39mload_weights_from_hdf5_group(f, model)\n\u001b[0;32m    259\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 260\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m    261\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFile format not supported: filepath=\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfilepath\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m. \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    262\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mKeras 3 only supports V3 `.keras` and `.weights.h5` \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    263\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfiles, or legacy V1/V2 `.h5` files.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    264\u001b[0m     )\n",
+            "File \u001b[1;32mc:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\utils\\traceback_utils.py:122\u001b[0m, in \u001b[0;36mfilter_traceback..error_handler\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    119\u001b[0m     filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n\u001b[0;32m    120\u001b[0m     \u001b[38;5;66;03m# To get the full stack trace, call:\u001b[39;00m\n\u001b[0;32m    121\u001b[0m     \u001b[38;5;66;03m# `keras.config.disable_traceback_filtering()`\u001b[39;00m\n\u001b[1;32m--> 122\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m e\u001b[38;5;241m.\u001b[39mwith_traceback(filtered_tb) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m    123\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[0;32m    124\u001b[0m     \u001b[38;5;28;01mdel\u001b[39;00m filtered_tb\n",
+            "File \u001b[1;32mc:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\saving\\saving_api.py:260\u001b[0m, in \u001b[0;36mload_weights\u001b[1;34m(model, filepath, skip_mismatch, **kwargs)\u001b[0m\n\u001b[0;32m    258\u001b[0m             legacy_h5_format\u001b[38;5;241m.\u001b[39mload_weights_from_hdf5_group(f, model)\n\u001b[0;32m    259\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 260\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m    261\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFile format not supported: filepath=\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfilepath\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m. \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    262\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mKeras 3 only supports V3 `.keras` and `.weights.h5` \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    263\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfiles, or legacy V1/V2 `.h5` files.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    264\u001b[0m     )\n",
             "\u001b[1;31mValueError\u001b[0m: File format not supported: filepath=./models/checkpoint. Keras 3 only supports V3 `.keras` and `.weights.h5` files, or legacy V1/V2 `.h5` files."
           ]
         }
@@ -3501,7 +2735,8 @@
       },
       "outputs": [],
       "source": [
-        "decoded = tf.keras.backend.ctc_decode(yhat, input_length=[75,75], greedy=True)[0][0].numpy()"
+        "# decoded = tf.keras.backend.ctc_decode(yhat, input_length=[75,75], greedy=True)[0][0].numpy()\n",
+        "decoded = keras.backend.ctc_decode(yhat, input_length=[75,75], greedy=True)[0][0].numpy()"
       ]
     },
     {
@@ -3574,7 +2809,8 @@
       },
       "outputs": [],
       "source": [
-        "decoded = tf.keras.backend.ctc_decode(yhat, input_length=[75], greedy=True)[0][0].numpy()"
+        "# decoded = tf.keras.backend.ctc_decode(yhat, input_length=[75], greedy=True)[0][0].numpy()\n",
+        "decoded = keras.backend.ctc_decode(yhat, input_length=[75], greedy=True)[0][0].numpy()"
       ]
     },
     {
@@ -3632,7 +2868,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.12.2"
+      "version": "3.12.3"
     }
   },
   "nbformat": 4,
diff --git a/app/modelutil.py b/app/modelutil.py
index 2d406f43..fcd140a6 100644
--- a/app/modelutil.py
+++ b/app/modelutil.py
@@ -1,10 +1,10 @@
 import os
 from tensorflow.keras.models import Sequential
 from tensorflow.keras.layers import Conv3D, LSTM, Dense, Dropout, Bidirectional, MaxPool3D, Activation, Reshape, SpatialDropout3D, BatchNormalization, TimeDistributed, Flatten
-from keras.layers import Conv3D, LSTM, Dense, Dropout, Bidirectional, MaxPool3D, Activation, Reshape, SpatialDropout3D, BatchNormalization, TimeDistributed, Flatten
-from keras.models import Sequential
-from keras.layers import LSTM, Bidirectional
-from keras.initializers import Orthogonal
+# from keras.layers import Conv3D, LSTM, Dense, Dropout, Bidirectional, MaxPool3D, Activation, Reshape, SpatialDropout3D, BatchNormalization, TimeDistributed, Flatten
+# from keras.models import Sequential
+# from keras.layers import LSTM, Bidirectional
+# from keras.initializers import Orthogonal
 import tensorflow as tf
 
 def load_model() -> Sequential:
@@ -24,35 +24,29 @@ def load_model() -> Sequential:
 
     model.add(TimeDistributed(Flatten()))
 
-    # model.add(Bidirectional(LSTM(128, kernel_initializer='Orthogonal', return_sequences=True)))
-    model.add(Bidirectional(LSTM(128, kernel_initializer=Orthogonal(), return_sequences=True)))
+    model.add(Bidirectional(LSTM(128, kernel_initializer='Orthogonal', return_sequences=True)))
+    # model.add(Bidirectional(LSTM(128, kernel_initializer=Orthogonal(), return_sequences=True)))
     model.add(Dropout(.5))
 
-    # model.add(Bidirectional(LSTM(128, kernel_initializer='Orthogonal', return_sequences=True)))
-    model.add(Bidirectional(LSTM(128, kernel_initializer=Orthogonal(), return_sequences=True)))
+    model.add(Bidirectional(LSTM(128, kernel_initializer='Orthogonal', return_sequences=True)))
+    # model.add(Bidirectional(LSTM(128, kernel_initializer=Orthogonal(), return_sequences=True)))
     model.add(Dropout(.5))
 
     model.add(Dense(41, kernel_initializer='he_normal', activation='softmax'))
 
-    # weights_path = os.path.join('..', 'models', 'checkpoint.h5')  # Adjust the extension as necessary
-    # # weights_path = 'C:/haidun.li/NSCC/LipNet/models/checkpoint'
-    # print('weights_path:', weights_path)
-    # if os.path.exists(weights_path):
-    #     model.load_weights(weights_path)
-    # else:
-    #     raise FileNotFoundError(f"Weight file {weights_path} does not exist.")
+    model.load_weights(os.path.join('..','models','checkpoint'))
 
-    # return model
+    # # Ensure the checkpoint directory is correctly set relative to the script's execution directory
+    # checkpoint_dir = os.path.join('..', 'models')
 
-     # Create a checkpoint instance that points to the folder where the checkpoints are saved
-    checkpoint_dir = os.path.join('..', 'models')
-    checkpoint = tf.train.Checkpoint(model=model)
+    # # Create a checkpoint instance that points to the folder where the checkpoints are saved
+    # checkpoint = tf.train.Checkpoint(model=model)
 
-    # Restore the latest checkpoint
-    latest = tf.train.latest_checkpoint(checkpoint_dir)
-    if latest:
-        checkpoint.restore(latest)
-    else:
-        raise FileNotFoundError("No checkpoint found in {}".format(checkpoint_dir))
+    # # Restore the latest checkpoint
+    # latest = tf.train.latest_checkpoint(checkpoint_dir)
+    # if latest:
+    #     checkpoint.restore(latest)
+    # else:
+    #     raise FileNotFoundError(f"No checkpoint found in {checkpoint_dir}")
 
     return model
\ No newline at end of file
diff --git a/app/streamlitapp.py b/app/streamlitapp.py
index 0381680b..811cb4e7 100644
--- a/app/streamlitapp.py
+++ b/app/streamlitapp.py
@@ -34,6 +34,7 @@
 
         # Rendering inside of the app
         video = open('test_video.mp4', 'rb')
+        # video = open('input.mp4', 'rb')
         video_bytes = video.read()
         st.video(video_bytes)
 

From bb19d97ecf9c5ba79483c370dadcf4426e9c84a5 Mon Sep 17 00:00:00 2001
From: "haidun.li" 
Date: Fri, 12 Apr 2024 03:31:23 -0300
Subject: [PATCH 06/17] Refactor modelutil.py: Import Keras layers and models,
 update load_model function

---
 LipNet.ipynb        | 5295 ++++++++++++++++++++-----------------------
 LipNet_my.ipynb     | 2590 +++++++++++++++++++++
 animation.gif       |  Bin 448311 -> 443531 bytes
 app/animation.gif   |  Bin 430386 -> 444093 bytes
 app/modelutil.py    |   42 +-
 app/streamlitapp.py |    9 +-
 6 files changed, 5052 insertions(+), 2884 deletions(-)
 create mode 100644 LipNet_my.ipynb

diff --git a/LipNet.ipynb b/LipNet.ipynb
index ef5467f0..bab88dcf 100644
--- a/LipNet.ipynb
+++ b/LipNet.ipynb
@@ -1,2876 +1,2453 @@
 {
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "colab_type": "text",
-        "id": "view-in-github"
-      },
-      "source": [
-        "\"Open"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "a3573a47-3689-4668-b62f-5c8451b2b4e9",
-      "metadata": {
-        "id": "a3573a47-3689-4668-b62f-5c8451b2b4e9",
-        "tags": []
-      },
-      "source": [
-        "# 0. Install and Import Dependencies"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 1,
-      "id": "ddfbccbe-41ae-4c23-98b1-a13868e2b499",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "ddfbccbe-41ae-4c23-98b1-a13868e2b499",
-        "outputId": "964dae10-91f7-4769-b15c-4a00ca9db312",
-        "scrolled": true,
-        "tags": []
-      },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Package                           Version\n",
-            "--------------------------------- ------------\n",
-            "absl-py                           2.1.0\n",
-            "aiobotocore                       2.7.0\n",
-            "aiohttp                           3.9.3\n",
-            "aioitertools                      0.7.1\n",
-            "aiosignal                         1.2.0\n",
-            "alabaster                         0.7.12\n",
-            "altair                            5.0.1\n",
-            "anaconda-anon-usage               0.4.3\n",
-            "anaconda-catalogs                 0.2.0\n",
-            "anaconda-client                   1.12.3\n",
-            "anaconda-cloud-auth               0.1.4\n",
-            "anaconda-navigator                2.5.4\n",
-            "anaconda-project                  0.11.1\n",
-            "anyio                             4.2.0\n",
-            "appdirs                           1.4.4\n",
-            "archspec                          0.2.3\n",
-            "argon2-cffi                       21.3.0\n",
-            "argon2-cffi-bindings              21.2.0\n",
-            "arrow                             1.2.3\n",
-            "astroid                           2.14.2\n",
-            "astropy                           5.3.4\n",
-            "asttokens                         2.0.5\n",
-            "astunparse                        1.6.3\n",
-            "async-lru                         2.0.4\n",
-            "atomicwrites                      1.4.0\n",
-            "attrs                             23.1.0\n",
-            "Automat                           20.2.0\n",
-            "autopep8                          1.6.0\n",
-            "Babel                             2.11.0\n",
-            "backports.functools-lru-cache     1.6.4\n",
-            "backports.tempfile                1.0\n",
-            "backports.weakref                 1.0.post1\n",
-            "bcrypt                            3.2.0\n",
-            "beautifulsoup4                    4.12.2\n",
-            "binaryornot                       0.4.4\n",
-            "black                             23.11.0\n",
-            "bleach                            4.1.0\n",
-            "blinker                           1.6.2\n",
-            "bokeh                             3.3.4\n",
-            "boltons                           23.0.0\n",
-            "botocore                          1.31.64\n",
-            "Bottleneck                        1.3.7\n",
-            "Brotli                            1.0.9\n",
-            "cachetools                        4.2.2\n",
-            "certifi                           2024.2.2\n",
-            "cffi                              1.16.0\n",
-            "chardet                           4.0.0\n",
-            "charset-normalizer                2.0.4\n",
-            "click                             8.1.7\n",
-            "cloudpickle                       2.2.1\n",
-            "clyent                            1.2.2\n",
-            "colorama                          0.4.6\n",
-            "colorcet                          3.0.1\n",
-            "comm                              0.1.2\n",
-            "conda                             24.3.0\n",
-            "conda-build                       24.1.2\n",
-            "conda-content-trust               0.2.0\n",
-            "conda_index                       0.4.0\n",
-            "conda-libmamba-solver             24.1.0\n",
-            "conda-pack                        0.6.0\n",
-            "conda-package-handling            2.2.0\n",
-            "conda_package_streaming           0.9.0\n",
-            "conda-repo-cli                    1.0.75\n",
-            "conda-token                       0.4.0\n",
-            "conda-verify                      3.4.2\n",
-            "constantly                        23.10.4\n",
-            "contourpy                         1.2.0\n",
-            "cookiecutter                      2.5.0\n",
-            "cryptography                      42.0.2\n",
-            "cssselect                         1.2.0\n",
-            "cycler                            0.11.0\n",
-            "cytoolz                           0.12.2\n",
-            "dask                              2023.11.0\n",
-            "datashader                        0.16.0\n",
-            "debugpy                           1.6.7\n",
-            "decorator                         5.1.1\n",
-            "defusedxml                        0.7.1\n",
-            "diff-match-patch                  20200713\n",
-            "dill                              0.3.7\n",
-            "distributed                       2023.11.0\n",
-            "distro                            1.8.0\n",
-            "docstring-to-markdown             0.11\n",
-            "docutils                          0.18.1\n",
-            "entrypoints                       0.4\n",
-            "et-xmlfile                        1.1.0\n",
-            "executing                         0.8.3\n",
-            "fastjsonschema                    2.16.2\n",
-            "filelock                          3.13.1\n",
-            "flake8                            6.0.0\n",
-            "Flask                             2.2.5\n",
-            "flatbuffers                       24.3.25\n",
-            "fonttools                         4.25.0\n",
-            "frozenlist                        1.4.0\n",
-            "fsspec                            2023.10.0\n",
-            "future                            0.18.3\n",
-            "gast                              0.5.4\n",
-            "gdown                             5.1.0\n",
-            "gensim                            4.3.0\n",
-            "gitdb                             4.0.7\n",
-            "GitPython                         3.1.37\n",
-            "gmpy2                             2.1.2\n",
-            "google-pasta                      0.2.0\n",
-            "greenlet                          3.0.1\n",
-            "grpcio                            1.62.1\n",
-            "h5py                              3.11.0\n",
-            "HeapDict                          1.0.1\n",
-            "holoviews                         1.18.3\n",
-            "hvplot                            0.9.2\n",
-            "hyperlink                         21.0.0\n",
-            "idna                              3.4\n",
-            "imagecodecs                       2023.1.23\n",
-            "imageio                           2.33.1\n",
-            "imagesize                         1.4.1\n",
-            "imbalanced-learn                  0.11.0\n",
-            "importlib-metadata                7.0.1\n",
-            "incremental                       22.10.0\n",
-            "inflection                        0.5.1\n",
-            "iniconfig                         1.1.1\n",
-            "intake                            0.6.8\n",
-            "intervaltree                      3.1.0\n",
-            "ipykernel                         6.28.0\n",
-            "ipython                           8.20.0\n",
-            "ipython-genutils                  0.2.0\n",
-            "ipywidgets                        7.6.5\n",
-            "isort                             5.9.3\n",
-            "itemadapter                       0.3.0\n",
-            "itemloaders                       1.1.0\n",
-            "itsdangerous                      2.0.1\n",
-            "jaraco.classes                    3.2.1\n",
-            "jedi                              0.18.1\n",
-            "jellyfish                         1.0.1\n",
-            "Jinja2                            3.1.3\n",
-            "jmespath                          1.0.1\n",
-            "joblib                            1.2.0\n",
-            "json5                             0.9.6\n",
-            "jsonpatch                         1.32\n",
-            "jsonpointer                       2.1\n",
-            "jsonschema                        4.19.2\n",
-            "jsonschema-specifications         2023.7.1\n",
-            "jupyter                           1.0.0\n",
-            "jupyter_client                    8.6.0\n",
-            "jupyter-console                   6.6.3\n",
-            "jupyter_core                      5.5.0\n",
-            "jupyter-events                    0.8.0\n",
-            "jupyter-lsp                       2.2.0\n",
-            "jupyter_server                    2.10.0\n",
-            "jupyter_server_terminals          0.4.4\n",
-            "jupyterlab                        4.0.11\n",
-            "jupyterlab-pygments               0.1.2\n",
-            "jupyterlab_server                 2.25.1\n",
-            "jupyterlab-widgets                3.0.9\n",
-            "keras                             3.2.0\n",
-            "keyring                           23.13.1\n",
-            "kiwisolver                        1.4.4\n",
-            "lazy_loader                       0.3\n",
-            "lazy-object-proxy                 1.6.0\n",
-            "lckr_jupyterlab_variableinspector 3.1.0\n",
-            "libarchive-c                      2.9\n",
-            "libclang                          18.1.1\n",
-            "libmambapy                        1.5.6\n",
-            "linkify-it-py                     2.0.0\n",
-            "llvmlite                          0.42.0\n",
-            "lmdb                              1.4.1\n",
-            "locket                            1.0.0\n",
-            "lxml                              4.9.3\n",
-            "lz4                               4.3.2\n",
-            "Markdown                          3.4.1\n",
-            "markdown-it-py                    2.2.0\n",
-            "MarkupSafe                        2.1.3\n",
-            "matplotlib                        3.8.0\n",
-            "matplotlib-inline                 0.1.6\n",
-            "mccabe                            0.7.0\n",
-            "mdit-py-plugins                   0.3.0\n",
-            "mdurl                             0.1.0\n",
-            "menuinst                          2.0.2\n",
-            "mistune                           2.0.4\n",
-            "mkl-fft                           1.3.8\n",
-            "mkl-random                        1.2.4\n",
-            "mkl-service                       2.4.0\n",
-            "ml-dtypes                         0.3.2\n",
-            "more-itertools                    10.1.0\n",
-            "mpmath                            1.3.0\n",
-            "msgpack                           1.0.3\n",
-            "multidict                         6.0.4\n",
-            "multipledispatch                  0.6.0\n",
-            "munkres                           1.1.4\n",
-            "mypy                              1.8.0\n",
-            "mypy-extensions                   1.0.0\n",
-            "namex                             0.0.7\n",
-            "navigator-updater                 0.4.0\n",
-            "nbclient                          0.8.0\n",
-            "nbconvert                         7.10.0\n",
-            "nbformat                          5.9.2\n",
-            "nest-asyncio                      1.6.0\n",
-            "networkx                          3.1\n",
-            "nltk                              3.8.1\n",
-            "notebook                          7.0.8\n",
-            "notebook_shim                     0.2.3\n",
-            "numba                             0.59.0\n",
-            "numexpr                           2.8.7\n",
-            "numpy                             1.26.4\n",
-            "numpydoc                          1.5.0\n",
-            "opencv-python                     4.9.0.80\n",
-            "openpyxl                          3.0.10\n",
-            "opt-einsum                        3.3.0\n",
-            "optree                            0.11.0\n",
-            "overrides                         7.4.0\n",
-            "packaging                         23.1\n",
-            "pandas                            2.1.4\n",
-            "pandocfilters                     1.5.0\n",
-            "panel                             1.3.8\n",
-            "param                             2.0.2\n",
-            "paramiko                          2.8.1\n",
-            "parsel                            1.8.1\n",
-            "parso                             0.8.3\n",
-            "partd                             1.4.1\n",
-            "pathlib                           1.0.1\n",
-            "pathspec                          0.10.3\n",
-            "patsy                             0.5.3\n",
-            "pexpect                           4.8.0\n",
-            "pickleshare                       0.7.5\n",
-            "pillow                            10.2.0\n",
-            "pip                               23.3.1\n",
-            "pkce                              1.0.3\n",
-            "pkginfo                           1.9.6\n",
-            "platformdirs                      3.10.0\n",
-            "plotly                            5.9.0\n",
-            "pluggy                            1.0.0\n",
-            "ply                               3.11\n",
-            "prometheus-client                 0.14.1\n",
-            "prompt-toolkit                    3.0.43\n",
-            "Protego                           0.1.16\n",
-            "protobuf                          3.20.3\n",
-            "psutil                            5.9.0\n",
-            "ptyprocess                        0.7.0\n",
-            "pure-eval                         0.2.2\n",
-            "py-cpuinfo                        9.0.0\n",
-            "pyarrow                           14.0.2\n",
-            "pyasn1                            0.4.8\n",
-            "pyasn1-modules                    0.2.8\n",
-            "pycodestyle                       2.10.0\n",
-            "pycosat                           0.6.6\n",
-            "pycparser                         2.21\n",
-            "pyct                              0.5.0\n",
-            "pycurl                            7.45.2\n",
-            "pydantic                          1.10.12\n",
-            "pydeck                            0.8.0\n",
-            "PyDispatcher                      2.0.5\n",
-            "pydocstyle                        6.3.0\n",
-            "pyerfa                            2.0.0\n",
-            "pyflakes                          3.0.1\n",
-            "Pygments                          2.15.1\n",
-            "PyJWT                             2.4.0\n",
-            "pylint                            2.16.2\n",
-            "pylint-venv                       2.3.0\n",
-            "pyls-spyder                       0.4.0\n",
-            "PyNaCl                            1.5.0\n",
-            "pyodbc                            5.0.1\n",
-            "pyOpenSSL                         24.0.0\n",
-            "pyparsing                         3.0.9\n",
-            "PyQt5                             5.15.10\n",
-            "PyQt5-sip                         12.13.0\n",
-            "PyQtWebEngine                     5.15.6\n",
-            "PySocks                           1.7.1\n",
-            "pytest                            7.4.0\n",
-            "python-dateutil                   2.8.2\n",
-            "python-dotenv                     0.21.0\n",
-            "python-json-logger                2.0.7\n",
-            "python-lsp-black                  1.2.1\n",
-            "python-lsp-jsonrpc                1.0.0\n",
-            "python-lsp-server                 1.7.2\n",
-            "python-slugify                    5.0.2\n",
-            "python-snappy                     0.6.1\n",
-            "pytoolconfig                      1.2.6\n",
-            "pytz                              2023.3.post1\n",
-            "pyviz_comms                       3.0.0\n",
-            "pywavelets                        1.5.0\n",
-            "pywin32                           305.1\n",
-            "pywin32-ctypes                    0.2.0\n",
-            "pywinpty                          2.0.10\n",
-            "PyYAML                            6.0.1\n",
-            "pyzmq                             25.1.2\n",
-            "QDarkStyle                        3.0.2\n",
-            "qstylizer                         0.2.2\n",
-            "QtAwesome                         1.2.2\n",
-            "qtconsole                         5.4.2\n",
-            "QtPy                              2.4.1\n",
-            "queuelib                          1.6.2\n",
-            "referencing                       0.30.2\n",
-            "regex                             2023.10.3\n",
-            "requests                          2.31.0\n",
-            "requests-file                     1.5.1\n",
-            "requests-toolbelt                 1.0.0\n",
-            "rfc3339-validator                 0.1.4\n",
-            "rfc3986-validator                 0.1.1\n",
-            "rich                              13.3.5\n",
-            "rope                              1.7.0\n",
-            "rpds-py                           0.10.6\n",
-            "Rtree                             1.0.1\n",
-            "ruamel.yaml                       0.17.21\n",
-            "ruamel-yaml-conda                 0.17.21\n",
-            "s3fs                              2023.10.0\n",
-            "scikit-image                      0.22.0\n",
-            "scikit-learn                      1.2.2\n",
-            "scipy                             1.11.4\n",
-            "Scrapy                            2.8.0\n",
-            "seaborn                           0.12.2\n",
-            "semver                            2.13.0\n",
-            "Send2Trash                        1.8.2\n",
-            "service-identity                  18.1.0\n",
-            "setuptools                        68.2.2\n",
-            "sip                               6.7.12\n",
-            "six                               1.16.0\n",
-            "smart-open                        5.2.1\n",
-            "smmap                             4.0.0\n",
-            "sniffio                           1.3.0\n",
-            "snowballstemmer                   2.2.0\n",
-            "sortedcontainers                  2.4.0\n",
-            "soupsieve                         2.5\n",
-            "Sphinx                            5.0.2\n",
-            "sphinxcontrib-applehelp           1.0.2\n",
-            "sphinxcontrib-devhelp             1.0.2\n",
-            "sphinxcontrib-htmlhelp            2.0.0\n",
-            "sphinxcontrib-jsmath              1.0.1\n",
-            "sphinxcontrib-qthelp              1.0.3\n",
-            "sphinxcontrib-serializinghtml     1.1.5\n",
-            "spyder                            5.4.3\n",
-            "spyder-kernels                    2.4.4\n",
-            "SQLAlchemy                        2.0.25\n",
-            "stack-data                        0.2.0\n",
-            "statsmodels                       0.14.0\n",
-            "streamlit                         1.30.0\n",
-            "sympy                             1.12\n",
-            "tables                            3.9.2\n",
-            "tabulate                          0.9.0\n",
-            "tblib                             1.7.0\n",
-            "tenacity                          8.2.2\n",
-            "tensorboard                       2.16.2\n",
-            "tensorboard-data-server           0.7.2\n",
-            "tensorflow                        2.16.1\n",
-            "tensorflow-intel                  2.16.1\n",
-            "tensorflow-io-gcs-filesystem      0.31.0\n",
-            "termcolor                         2.4.0\n",
-            "terminado                         0.17.1\n",
-            "text-unidecode                    1.3\n",
-            "textdistance                      4.2.1\n",
-            "threadpoolctl                     2.2.0\n",
-            "three-merge                       0.1.1\n",
-            "tifffile                          2023.4.12\n",
-            "tinycss2                          1.2.1\n",
-            "tldextract                        3.2.0\n",
-            "toml                              0.10.2\n",
-            "tomlkit                           0.11.1\n",
-            "toolz                             0.12.0\n",
-            "tornado                           6.3.3\n",
-            "tqdm                              4.65.0\n",
-            "traitlets                         5.7.1\n",
-            "truststore                        0.8.0\n",
-            "Twisted                           23.10.0\n",
-            "twisted-iocpsupport               1.0.2\n",
-            "typing_extensions                 4.9.0\n",
-            "tzdata                            2023.3\n",
-            "tzlocal                           2.1\n",
-            "uc-micro-py                       1.0.1\n",
-            "ujson                             5.4.0\n",
-            "Unidecode                         1.2.0\n",
-            "urllib3                           2.0.7\n",
-            "validators                        0.18.2\n",
-            "w3lib                             2.1.2\n",
-            "watchdog                          2.1.6\n",
-            "wcwidth                           0.2.5\n",
-            "webencodings                      0.5.1\n",
-            "websocket-client                  0.58.0\n",
-            "Werkzeug                          2.2.3\n",
-            "whatthepatch                      1.0.2\n",
-            "wheel                             0.41.2\n",
-            "widgetsnbextension                3.5.2\n",
-            "win-inet-pton                     1.1.0\n",
-            "wrapt                             1.14.1\n",
-            "xarray                            2023.6.0\n",
-            "xlwings                           0.29.1\n",
-            "xyzservices                       2022.9.0\n",
-            "yapf                              0.31.0\n",
-            "yarl                              1.9.3\n",
-            "zict                              3.0.0\n",
-            "zipp                              3.17.0\n",
-            "zope.interface                    5.4.0\n",
-            "zstandard                         0.19.0\n"
-          ]
-        }
-      ],
-      "source": [
-        "!pip list"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 2,
-      "id": "02f907ea-f669-46c7-adcf-7f257e663448",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "02f907ea-f669-46c7-adcf-7f257e663448",
-        "outputId": "e444b2f9-189a-40bd-a2f9-925f13917c35",
-        "tags": []
-      },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Defaulting to user installation because normal site-packages is not writeable\n",
-            "Requirement already satisfied: opencv-python in c:\\users\\lhdto\\appdata\\roaming\\python\\python311\\site-packages (4.9.0.80)\n",
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-            "Requirement already satisfied: rich in c:\\programdata\\anaconda3\\lib\\site-packages (from keras>=3.0.0->tensorflow-intel==2.16.1->tensorflow) (13.3.5)\n",
-            "Requirement already satisfied: namex in c:\\users\\lhdto\\appdata\\roaming\\python\\python311\\site-packages (from keras>=3.0.0->tensorflow-intel==2.16.1->tensorflow) (0.0.7)\n",
-            "Requirement already satisfied: optree in c:\\users\\lhdto\\appdata\\roaming\\python\\python311\\site-packages (from keras>=3.0.0->tensorflow-intel==2.16.1->tensorflow) (0.11.0)\n",
-            "Requirement already satisfied: markdown>=2.6.8 in c:\\programdata\\anaconda3\\lib\\site-packages (from tensorboard<2.17,>=2.16->tensorflow-intel==2.16.1->tensorflow) (3.4.1)\n",
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-            "Requirement already satisfied: werkzeug>=1.0.1 in c:\\programdata\\anaconda3\\lib\\site-packages (from tensorboard<2.17,>=2.16->tensorflow-intel==2.16.1->tensorflow) (2.2.3)\n",
-            "Requirement already satisfied: MarkupSafe>=2.1.1 in c:\\programdata\\anaconda3\\lib\\site-packages (from werkzeug>=1.0.1->tensorboard<2.17,>=2.16->tensorflow-intel==2.16.1->tensorflow) (2.1.3)\n",
-            "Requirement already satisfied: markdown-it-py<3.0.0,>=2.2.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from rich->keras>=3.0.0->tensorflow-intel==2.16.1->tensorflow) (2.2.0)\n",
-            "Requirement already satisfied: pygments<3.0.0,>=2.13.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from rich->keras>=3.0.0->tensorflow-intel==2.16.1->tensorflow) (2.15.1)\n",
-            "Requirement already satisfied: mdurl~=0.1 in c:\\programdata\\anaconda3\\lib\\site-packages (from markdown-it-py<3.0.0,>=2.2.0->rich->keras>=3.0.0->tensorflow-intel==2.16.1->tensorflow) (0.1.0)\n"
-          ]
-        }
-      ],
-      "source": [
-        "!pip install opencv-python matplotlib imageio gdown tensorflow"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 3,
-      "id": "b24af50c-20b8-409d-ad78-30a933fdd669",
-      "metadata": {
-        "id": "b24af50c-20b8-409d-ad78-30a933fdd669",
-        "tags": []
-      },
-      "outputs": [],
-      "source": [
-        "import os\n",
-        "import cv2\n",
-        "import tensorflow as tf\n",
-        "import numpy as np\n",
-        "from typing import List\n",
-        "from matplotlib import pyplot as plt\n",
-        "import imageio\n",
-        "import keras"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 4,
-      "id": "1e3db0b0-e559-4ad6-91fd-e7414b7d75e6",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "1e3db0b0-e559-4ad6-91fd-e7414b7d75e6",
-        "outputId": "c1e41844-c8fd-4efc-eb22-11f3817b0c9f"
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              "[]"
-            ]
-          },
-          "execution_count": 4,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "tf.config.list_physical_devices('GPU')"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 5,
-      "id": "378d045a-3003-4f93-b7d2-a25a97774a68",
-      "metadata": {
-        "id": "378d045a-3003-4f93-b7d2-a25a97774a68",
-        "tags": []
-      },
-      "outputs": [],
-      "source": [
-        "physical_devices = tf.config.list_physical_devices('GPU')\n",
-        "try:\n",
-        "    tf.config.experimental.set_memory_growth(physical_devices[0], True)\n",
-        "except:\n",
-        "    pass"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "7a19e88e-c7b9-45c1-ae1e-f2109329c71b",
-      "metadata": {
-        "id": "7a19e88e-c7b9-45c1-ae1e-f2109329c71b",
-        "tags": []
-      },
-      "source": [
-        "# 1. Build Data Loading Functions"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 6,
-      "id": "8fb99c90-e05a-437f-839d-6e772f8c1dd5",
-      "metadata": {
-        "id": "8fb99c90-e05a-437f-839d-6e772f8c1dd5",
-        "tags": []
-      },
-      "outputs": [],
-      "source": [
-        "import gdown"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 7,
-      "id": "c019e4c6-2af3-4160-99ea-5c8cb009f1a7",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "c019e4c6-2af3-4160-99ea-5c8cb009f1a7",
-        "outputId": "49a7c7ff-ab7d-480e-bcfd-ee652ad89af1",
-        "tags": []
-      },
-      "outputs": [],
-      "source": [
-        "# url = 'https://drive.google.com/uc?id=1YlvpDLix3S-U8fd-gqRwPcWXAXm8JwjL'\n",
-        "# output = 'data.zip'\n",
-        "# gdown.download(url, output, quiet=False)\n",
-        "# gdown.extractall('data.zip')"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 8,
-      "id": "8548cc59-6dfc-4acc-abc3-3e65212db02e",
-      "metadata": {
-        "id": "8548cc59-6dfc-4acc-abc3-3e65212db02e",
-        "tags": []
-      },
-      "outputs": [],
-      "source": [
-        "def load_video(path:str) -> List[float]:\n",
-        "\n",
-        "    cap = cv2.VideoCapture(path)\n",
-        "    frames = []\n",
-        "    for _ in range(int(cap.get(cv2.CAP_PROP_FRAME_COUNT))):\n",
-        "        ret, frame = cap.read()\n",
-        "        frame = tf.image.rgb_to_grayscale(frame)\n",
-        "        frames.append(frame[190:236,80:220,:])\n",
-        "    cap.release()\n",
-        "\n",
-        "    mean = tf.math.reduce_mean(frames)\n",
-        "    std = tf.math.reduce_std(tf.cast(frames, tf.float32))\n",
-        "    return tf.cast((frames - mean), tf.float32) / std"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 9,
-      "id": "ec735e0b-ec98-4eb0-8f49-c35527d6670a",
-      "metadata": {
-        "id": "ec735e0b-ec98-4eb0-8f49-c35527d6670a",
-        "tags": []
-      },
-      "outputs": [],
-      "source": [
-        "vocab = [x for x in \"abcdefghijklmnopqrstuvwxyz'?!123456789 \"]"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 10,
-      "id": "be04e972-d7a5-4a72-82d8-a6bdde1f3ce6",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "be04e972-d7a5-4a72-82d8-a6bdde1f3ce6",
-        "outputId": "01f490ee-dabf-4e94-c6ed-181e8c1ae2c4",
-        "tags": []
-      },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "The vocabulary is: ['', 'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z', \"'\", '?', '!', '1', '2', '3', '4', '5', '6', '7', '8', '9', ' '] (size =40)\n"
-          ]
-        }
-      ],
-      "source": [
-        "# char_to_num = tf.keras.layers.StringLookup(vocabulary=vocab, oov_token=\"\")\n",
-        "# num_to_char = tf.keras.layers.StringLookup(\n",
-        "#     vocabulary=char_to_num.get_vocabulary(), oov_token=\"\", invert=True\n",
-        "# )\n",
-        "char_to_num = keras.layers.StringLookup(vocabulary=vocab, oov_token=\"\")\n",
-        "num_to_char = keras.layers.StringLookup(\n",
-        "    vocabulary=char_to_num.get_vocabulary(), oov_token=\"\", invert=True\n",
-        ")\n",
-        "\n",
-        "print(\n",
-        "    f\"The vocabulary is: {char_to_num.get_vocabulary()} \"\n",
-        "    f\"(size ={char_to_num.vocabulary_size()})\"\n",
-        ")"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 11,
-      "id": "559f7420-6802-45fa-9ca0-b1ff209b461c",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "559f7420-6802-45fa-9ca0-b1ff209b461c",
-        "outputId": "90f0547b-3308-478e-be4a-c89cef1d58e6",
-        "tags": []
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              "['',\n",
-              " 'a',\n",
-              " 'b',\n",
-              " 'c',\n",
-              " 'd',\n",
-              " 'e',\n",
-              " 'f',\n",
-              " 'g',\n",
-              " 'h',\n",
-              " 'i',\n",
-              " 'j',\n",
-              " 'k',\n",
-              " 'l',\n",
-              " 'm',\n",
-              " 'n',\n",
-              " 'o',\n",
-              " 'p',\n",
-              " 'q',\n",
-              " 'r',\n",
-              " 's',\n",
-              " 't',\n",
-              " 'u',\n",
-              " 'v',\n",
-              " 'w',\n",
-              " 'x',\n",
-              " 'y',\n",
-              " 'z',\n",
-              " \"'\",\n",
-              " '?',\n",
-              " '!',\n",
-              " '1',\n",
-              " '2',\n",
-              " '3',\n",
-              " '4',\n",
-              " '5',\n",
-              " '6',\n",
-              " '7',\n",
-              " '8',\n",
-              " '9',\n",
-              " ' ']"
-            ]
-          },
-          "execution_count": 11,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "char_to_num.get_vocabulary()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 12,
-      "id": "797ff78b-b48f-4e14-bb62-8cd0ebf9501a",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "797ff78b-b48f-4e14-bb62-8cd0ebf9501a",
-        "outputId": "cedf213c-95e4-439a-a871-641aec307517",
-        "tags": []
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              ""
-            ]
-          },
-          "execution_count": 12,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "char_to_num(['n','i','c','k'])"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 13,
-      "id": "8cd7f4f4-ae77-4509-a4f4-c723787ebad1",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "8cd7f4f4-ae77-4509-a4f4-c723787ebad1",
-        "outputId": "cb5428be-73ea-47b2-ea8e-4d2c4e3dcb00"
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              ""
-            ]
-          },
-          "execution_count": 13,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "num_to_char([14,  9,  3, 11])"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 14,
-      "id": "9491bab5-6a3c-4f79-879a-8f9fbe73ae2e",
-      "metadata": {
-        "id": "9491bab5-6a3c-4f79-879a-8f9fbe73ae2e",
-        "tags": []
-      },
-      "outputs": [],
-      "source": [
-        "def load_alignments(path:str) -> List[str]:\n",
-        "    with open(path, 'r') as f:\n",
-        "        lines = f.readlines()\n",
-        "    tokens = []\n",
-        "    for line in lines:\n",
-        "        line = line.split()\n",
-        "        if line[2] != 'sil':\n",
-        "            tokens = [*tokens,' ',line[2]]\n",
-        "    return char_to_num(tf.reshape(tf.strings.unicode_split(tokens, input_encoding='UTF-8'), (-1)))[1:]"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 15,
-      "id": "dd01ca9f-77fb-4643-a2aa-47dd82c5d66b",
-      "metadata": {
-        "id": "dd01ca9f-77fb-4643-a2aa-47dd82c5d66b",
-        "tags": []
-      },
-      "outputs": [],
-      "source": [
-        "def load_data(path: str):\n",
-        "    path = bytes.decode(path.numpy())\n",
-        "    # file_name = path.split('/')[-1].split('.')[0]\n",
-        "    # File name splitting for windows\n",
-        "    file_name = path.split('\\\\')[-1].split('.')[0]\n",
-        "    video_path = os.path.join('data','s1',f'{file_name}.mpg')\n",
-        "    alignment_path = os.path.join('data','alignments','s1',f'{file_name}.align')\n",
-        "    frames = load_video(video_path)\n",
-        "    alignments = load_alignments(alignment_path)\n",
-        "\n",
-        "    return frames, alignments"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 16,
-      "id": "8cb7cc58-31ae-4904-a805-1177a82717d2",
-      "metadata": {
-        "id": "8cb7cc58-31ae-4904-a805-1177a82717d2",
-        "tags": []
-      },
-      "outputs": [],
-      "source": [
-        "test_path = '.\\\\data\\\\s1\\\\bbal6n.mpg'\n",
-        "# test_path = './data/s1/bbal6n.mpg'"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 17,
-      "id": "76aa964f-0c84-490d-897a-d00e3966e2c9",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 35
-        },
-        "id": "76aa964f-0c84-490d-897a-d00e3966e2c9",
-        "outputId": "d04d49ad-4eeb-4be1-ffa7-f2655aa86a7a"
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              "'bbal6n'"
-            ]
-          },
-          "execution_count": 17,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "tf.convert_to_tensor(test_path).numpy().decode('utf-8').split('\\\\')[-1].split('.')[0]\n",
-        "# tf.convert_to_tensor(test_path).numpy().decode('utf-8').split('/')[-1].split('.')[0]"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 18,
-      "id": "eb602c71-8560-4f9e-b26b-08202febb937",
-      "metadata": {
-        "id": "eb602c71-8560-4f9e-b26b-08202febb937",
-        "scrolled": true,
-        "tags": []
-      },
-      "outputs": [],
-      "source": [
-        "frames, alignments = load_data(tf.convert_to_tensor(test_path))"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 19,
-      "id": "0e3184a1-6b02-4b4f-84a8-a0a65f951ea2",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 244
-        },
-        "id": "0e3184a1-6b02-4b4f-84a8-a0a65f951ea2",
-        "outputId": "ca65c601-b300-4a41-d8fe-7915a8e5a50b"
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              ""
-            ]
-          },
-          "execution_count": 19,
-          "metadata": {},
-          "output_type": "execute_result"
-        },
-        {
-          "data": {
-            "image/png": 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",
-            "text/plain": [
-              "
"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        }
-      ],
-      "source": [
-        "plt.imshow(frames[40])"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 20,
-      "id": "d7ec0833-d54b-4073-84cf-92d011c60ec1",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "d7ec0833-d54b-4073-84cf-92d011c60ec1",
-        "outputId": "02abd7a1-113b-42b1-a6ba-9a458373b112"
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              ""
-            ]
-          },
-          "execution_count": 20,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "alignments"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 21,
-      "id": "fe1ad370-b287-4b46-85a2-7c45b0bd9b10",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "fe1ad370-b287-4b46-85a2-7c45b0bd9b10",
-        "outputId": "8910d788-545b-4e76-b154-4a90d8e63927"
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              ""
-            ]
-          },
-          "execution_count": 21,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "tf.strings.reduce_join([bytes.decode(x) for x in num_to_char(alignments.numpy()).numpy()])"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 22,
-      "id": "6871031a-b0ba-4c76-a852-f6329b0f2606",
-      "metadata": {
-        "id": "6871031a-b0ba-4c76-a852-f6329b0f2606",
-        "tags": []
-      },
-      "outputs": [],
-      "source": [
-        "def mappable_function(path:str) ->List[str]:\n",
-        "    result = tf.py_function(load_data, [path], (tf.float32, tf.int64))\n",
-        "    return result"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "c40a7eb4-0c3e-4eab-9291-5611cb68ce08",
-      "metadata": {
-        "id": "c40a7eb4-0c3e-4eab-9291-5611cb68ce08",
-        "tags": []
-      },
-      "source": [
-        "# 2. Create Data Pipeline"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 23,
-      "id": "7686355d-45aa-4c85-ad9c-053e6a9b4d81",
-      "metadata": {
-        "id": "7686355d-45aa-4c85-ad9c-053e6a9b4d81",
-        "tags": []
-      },
-      "outputs": [],
-      "source": [
-        "from matplotlib import pyplot as plt"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 54,
-      "id": "f066fea2-91b1-42ed-a67d-00566a1a53ff",
-      "metadata": {
-        "id": "f066fea2-91b1-42ed-a67d-00566a1a53ff",
-        "tags": []
-      },
-      "outputs": [],
-      "source": [
-        "data = tf.data.Dataset.list_files('./data/s1/*.mpg')\n",
-        "data = data.shuffle(500, reshuffle_each_iteration=False)\n",
-        "data = data.map(mappable_function)\n",
-        "data = data.padded_batch(2, padded_shapes=([75,None,None,None],[40]))\n",
-        "data = data.prefetch(tf.data.AUTOTUNE)\n",
-        "# Added for split\n",
-        "train = data.take(450)\n",
-        "test = data.skip(450)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 25,
-      "id": "6b1365bd-7742-41d1-95d4-247021751c3a",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "6b1365bd-7742-41d1-95d4-247021751c3a",
-        "outputId": "facfc0fa-bf61-4aff-b8ed-f1a42278a4b3"
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              "50"
-            ]
-          },
-          "execution_count": 25,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "len(test)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 26,
-      "id": "5281bde8-fdc8-4da1-bd55-5a7929a9e80c",
-      "metadata": {
-        "id": "5281bde8-fdc8-4da1-bd55-5a7929a9e80c"
-      },
-      "outputs": [],
-      "source": [
-        "frames, alignments = data.as_numpy_iterator().next()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 27,
-      "id": "cbebe683-6afd-47fd-bba4-c83b4b13bb32",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "cbebe683-6afd-47fd-bba4-c83b4b13bb32",
-        "outputId": "8924019e-28cd-4bfa-9e1e-bb836d71e6f8"
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              "2"
-            ]
-          },
-          "execution_count": 27,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "len(frames)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 28,
-      "id": "5cf2d676-93a9-434c-b3c7-bdcc2577b2e7",
-      "metadata": {
-        "id": "5cf2d676-93a9-434c-b3c7-bdcc2577b2e7",
-        "tags": []
-      },
-      "outputs": [],
-      "source": [
-        "sample = data.as_numpy_iterator()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 29,
-      "id": "efa6cd46-7079-46c0-b45b-832f339f6cb0",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "efa6cd46-7079-46c0-b45b-832f339f6cb0",
-        "outputId": "c24ecd06-8d12-4531-df65-27a9f77acada",
-        "scrolled": true,
-        "tags": []
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              "array([[[[[ 1.3226067 ],\n",
-              "          [ 1.3226067 ],\n",
-              "          [ 1.4393072 ],\n",
-              "          ...,\n",
-              "          [ 9.0637455 ],\n",
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-              "\n",
-              "         [[ 0.9517456 ],\n",
-              "          [ 0.9517456 ],\n",
-              "          [ 0.9517456 ],\n",
-              "          ...,\n",
-              "          [ 9.795049  ],\n",
-              "          [ 9.795049  ],\n",
-              "          [ 9.795049  ]]]]], dtype=float32)"
-            ]
-          },
-          "execution_count": 29,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "val = sample.next(); val[0]"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 30,
-      "id": "acf5eb4f-a0da-4a9a-bf24-af13e9cc2fbe",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "acf5eb4f-a0da-4a9a-bf24-af13e9cc2fbe",
-        "outputId": "2b7ce709-6400-402c-ab9d-0e4c5739cafc",
-        "tags": []
-      },
-      "outputs": [
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            "Lossy conversion from float32 to uint8. Range [0.0, 9.919549942016602]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 9.919549942016602]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 9.919549942016602]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 9.919549942016602]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 9.919549942016602]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 9.919549942016602]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 9.919549942016602]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 9.919549942016602]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 9.919549942016602]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 9.919549942016602]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 9.919549942016602]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 9.919549942016602]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 9.919549942016602]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 9.919549942016602]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 9.919549942016602]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 9.919549942016602]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 9.919549942016602]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 9.919549942016602]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 9.919549942016602]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 9.919549942016602]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 9.919549942016602]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 9.919549942016602]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 9.919549942016602]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 9.919549942016602]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 9.919549942016602]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 9.919549942016602]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 9.919549942016602]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 9.919549942016602]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 9.919549942016602]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 9.919549942016602]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 9.919549942016602]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 9.919549942016602]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 9.919549942016602]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 9.919549942016602]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 9.919549942016602]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 9.919549942016602]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 9.919549942016602]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 9.919549942016602]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 9.919549942016602]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 9.919549942016602]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 9.919549942016602]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 9.919549942016602]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 9.919549942016602]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 9.919549942016602]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 9.919549942016602]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 9.919549942016602]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 9.919549942016602]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 9.919549942016602]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 9.919549942016602]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 9.919549942016602]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 9.919549942016602]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 9.919549942016602]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 9.919549942016602]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 9.919549942016602]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 9.919549942016602]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 9.919549942016602]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 9.919549942016602]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 9.919549942016602]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 9.919549942016602]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 9.919549942016602]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 9.919549942016602]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 9.919549942016602]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 9.919549942016602]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 9.919549942016602]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 9.919549942016602]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 9.919549942016602]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 9.919549942016602]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 9.919549942016602]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 9.919549942016602]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 9.919549942016602]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 9.919549942016602]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 9.919549942016602]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 9.919549942016602]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 9.919549942016602]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 9.919549942016602]. Convert image to uint8 prior to saving to suppress this warning.\n"
-          ]
-        }
-      ],
-      "source": [
-        "imageio.mimsave('./animation.gif', val[0][0], fps=10)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 31,
-      "id": "c33a87a2-d5e0-4ec9-b174-73ebf41bf03a",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 244
-        },
-        "id": "c33a87a2-d5e0-4ec9-b174-73ebf41bf03a",
-        "outputId": "1b7cc028-1990-4714-8d7e-fda85218c0d5",
-        "tags": []
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              ""
-            ]
-          },
-          "execution_count": 31,
-          "metadata": {},
-          "output_type": "execute_result"
-        },
-        {
-          "data": {
-            "image/png": 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",
-            "text/plain": [
-              "
"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        }
-      ],
-      "source": [
-        "# 0:videos, 0: 1st video out of the batch,  0: return the first frame in the video\n",
-        "plt.imshow(val[0][0][35])"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 32,
-      "id": "84593332-133c-4205-b7a6-8e235d5e2b3b",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "84593332-133c-4205-b7a6-8e235d5e2b3b",
-        "outputId": "69f24afc-2cb9-4d07-9d93-3b4e1fde5046",
-        "tags": []
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              ""
-            ]
-          },
-          "execution_count": 32,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "tf.strings.reduce_join([num_to_char(word) for word in val[1][0]])"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "0f47733c-83bc-465c-b118-b198b492ad37",
-      "metadata": {
-        "id": "0f47733c-83bc-465c-b118-b198b492ad37",
-        "tags": []
-      },
-      "source": [
-        "# 3. Design the Deep Neural Network"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 33,
-      "id": "d8e9a497-191b-4842-afbd-26f5e13c43ba",
-      "metadata": {
-        "id": "d8e9a497-191b-4842-afbd-26f5e13c43ba",
-        "tags": []
-      },
-      "outputs": [],
-      "source": [
-        "from tensorflow import keras\n",
-        "# from tensorflow.keras.models import Sequential\n",
-        "# from tensorflow.keras.layers import Conv3D, LSTM, Dense, Dropout, Bidirectional, MaxPool3D, Activation, Reshape, SpatialDropout3D, BatchNormalization, TimeDistributed, Flatten\n",
-        "# from tensorflow.keras.optimizers import Adam\n",
-        "# from tensorflow.keras.callbacks import ModelCheckpoint, LearningRateScheduler\n",
-        "\n",
-        "from keras.models import Sequential\n",
-        "from keras.layers import Conv3D, LSTM, Dense, Dropout, Bidirectional, MaxPool3D, Activation, Reshape, SpatialDropout3D, BatchNormalization, TimeDistributed, Flatten\n",
-        "from keras.optimizers import Adam\n",
-        "from keras.callbacks import ModelCheckpoint, LearningRateScheduler"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 34,
-      "id": "5d376ebe",
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from keras.layers import LSTM, Bidirectional\n",
-        "from keras.initializers import Orthogonal"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 35,
-      "id": "3f753ed2-70b9-4236-8c1c-08ca065dc8bf",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "3f753ed2-70b9-4236-8c1c-08ca065dc8bf",
-        "outputId": "b06442c3-cc68-4c88-d5e8-c9a915fc8b96"
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              "(75, 46, 140, 1)"
-            ]
-          },
-          "execution_count": 35,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "data.as_numpy_iterator().next()[0][0].shape"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 36,
-      "id": "f9171056-a352-491a-9ed9-92b28ced268e",
-      "metadata": {
-        "id": "f9171056-a352-491a-9ed9-92b28ced268e",
-        "tags": []
-      },
-      "outputs": [
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            "c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\layers\\convolutional\\base_conv.py:99: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
-            "  super().__init__(\n"
-          ]
-        }
-      ],
-      "source": [
-        "model = Sequential()\n",
-        "model.add(Conv3D(128, 3, input_shape=(75,46,140,1), padding='same'))\n",
-        "model.add(Activation('relu'))\n",
-        "model.add(MaxPool3D((1,2,2)))\n",
-        "\n",
-        "model.add(Conv3D(256, 3, padding='same'))\n",
-        "model.add(Activation('relu'))\n",
-        "model.add(MaxPool3D((1,2,2)))\n",
-        "\n",
-        "model.add(Conv3D(75, 3, padding='same'))\n",
-        "model.add(Activation('relu'))\n",
-        "model.add(MaxPool3D((1,2,2)))\n",
-        "\n",
-        "\n",
-        "model.add(TimeDistributed(Flatten()))\n",
-        "\n",
-        "model.add(Bidirectional(LSTM(128, return_sequences=True)))\n",
-        "\n",
-        "# # model.add(Bidirectional(LSTM(128, kernel_initializer='Orthogonal', return_sequences=True)))\n",
-        "# model.add(Bidirectional(LSTM(128, kernel_initializer=Orthogonal(), return_sequences=True)))\n",
-        "# model.add(Dropout(.5))\n",
-        "\n",
-        "# # model.add(Bidirectional(LSTM(128, kernel_initializer='Orthogonal', return_sequences=True)))\n",
-        "# model.add(Bidirectional(LSTM(128, kernel_initializer=Orthogonal(), return_sequences=True)))\n",
-        "# model.add(Dropout(.5))\n",
-        "\n",
-        "model.add(Dense(char_to_num.vocabulary_size()+1, kernel_initializer='he_normal', activation='softmax'))"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 37,
-      "id": "78851825-2bcd-42a9-b7f2-28bb5a6bf43a",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "collapsed": true,
-        "id": "78851825-2bcd-42a9-b7f2-28bb5a6bf43a",
-        "jupyter": {
-          "outputs_hidden": true
-        },
-        "outputId": "90ebbd81-10a8-4674-8551-7b3cd530381a",
-        "tags": []
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/html": [
-              "
Model: \"sequential\"\n",
-              "
\n"
-            ],
-            "text/plain": [
-              "\u001b[1mModel: \"sequential\"\u001b[0m\n"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        },
-        {
-          "data": {
-            "text/html": [
-              "
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
-              "┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃\n",
-              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
-              "│ conv3d (Conv3D)                 │ (None, 75, 46, 140,    │         3,584 │\n",
-              "│                                 │ 128)                   │               │\n",
-              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-              "│ activation (Activation)         │ (None, 75, 46, 140,    │             0 │\n",
-              "│                                 │ 128)                   │               │\n",
-              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-              "│ max_pooling3d (MaxPooling3D)    │ (None, 75, 23, 70,     │             0 │\n",
-              "│                                 │ 128)                   │               │\n",
-              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-              "│ conv3d_1 (Conv3D)               │ (None, 75, 23, 70,     │       884,992 │\n",
-              "│                                 │ 256)                   │               │\n",
-              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-              "│ activation_1 (Activation)       │ (None, 75, 23, 70,     │             0 │\n",
-              "│                                 │ 256)                   │               │\n",
-              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-              "│ max_pooling3d_1 (MaxPooling3D)  │ (None, 75, 11, 35,     │             0 │\n",
-              "│                                 │ 256)                   │               │\n",
-              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-              "│ conv3d_2 (Conv3D)               │ (None, 75, 11, 35, 75) │       518,475 │\n",
-              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-              "│ activation_2 (Activation)       │ (None, 75, 11, 35, 75) │             0 │\n",
-              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-              "│ max_pooling3d_2 (MaxPooling3D)  │ (None, 75, 5, 17, 75)  │             0 │\n",
-              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-              "│ time_distributed                │ (None, 75, 6375)       │             0 │\n",
-              "│ (TimeDistributed)               │                        │               │\n",
-              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-              "│ bidirectional (Bidirectional)   │ (None, 75, 256)        │     6,660,096 │\n",
-              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-              "│ dense (Dense)                   │ (None, 75, 41)         │        10,537 │\n",
-              "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
-              "
\n"
-            ],
-            "text/plain": [
-              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
-              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
-              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
-              "│ conv3d (\u001b[38;5;33mConv3D\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m46\u001b[0m, \u001b[38;5;34m140\u001b[0m,    │         \u001b[38;5;34m3,584\u001b[0m │\n",
-              "│                                 │ \u001b[38;5;34m128\u001b[0m)                   │               │\n",
-              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-              "│ activation (\u001b[38;5;33mActivation\u001b[0m)         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m46\u001b[0m, \u001b[38;5;34m140\u001b[0m,    │             \u001b[38;5;34m0\u001b[0m │\n",
-              "│                                 │ \u001b[38;5;34m128\u001b[0m)                   │               │\n",
-              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-              "│ max_pooling3d (\u001b[38;5;33mMaxPooling3D\u001b[0m)    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m23\u001b[0m, \u001b[38;5;34m70\u001b[0m,     │             \u001b[38;5;34m0\u001b[0m │\n",
-              "│                                 │ \u001b[38;5;34m128\u001b[0m)                   │               │\n",
-              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-              "│ conv3d_1 (\u001b[38;5;33mConv3D\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m23\u001b[0m, \u001b[38;5;34m70\u001b[0m,     │       \u001b[38;5;34m884,992\u001b[0m │\n",
-              "│                                 │ \u001b[38;5;34m256\u001b[0m)                   │               │\n",
-              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-              "│ activation_1 (\u001b[38;5;33mActivation\u001b[0m)       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m23\u001b[0m, \u001b[38;5;34m70\u001b[0m,     │             \u001b[38;5;34m0\u001b[0m │\n",
-              "│                                 │ \u001b[38;5;34m256\u001b[0m)                   │               │\n",
-              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-              "│ max_pooling3d_1 (\u001b[38;5;33mMaxPooling3D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m11\u001b[0m, \u001b[38;5;34m35\u001b[0m,     │             \u001b[38;5;34m0\u001b[0m │\n",
-              "│                                 │ \u001b[38;5;34m256\u001b[0m)                   │               │\n",
-              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-              "│ conv3d_2 (\u001b[38;5;33mConv3D\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m11\u001b[0m, \u001b[38;5;34m35\u001b[0m, \u001b[38;5;34m75\u001b[0m) │       \u001b[38;5;34m518,475\u001b[0m │\n",
-              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-              "│ activation_2 (\u001b[38;5;33mActivation\u001b[0m)       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m11\u001b[0m, \u001b[38;5;34m35\u001b[0m, \u001b[38;5;34m75\u001b[0m) │             \u001b[38;5;34m0\u001b[0m │\n",
-              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-              "│ max_pooling3d_2 (\u001b[38;5;33mMaxPooling3D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m17\u001b[0m, \u001b[38;5;34m75\u001b[0m)  │             \u001b[38;5;34m0\u001b[0m │\n",
-              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-              "│ time_distributed                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m6375\u001b[0m)       │             \u001b[38;5;34m0\u001b[0m │\n",
-              "│ (\u001b[38;5;33mTimeDistributed\u001b[0m)               │                        │               │\n",
-              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-              "│ bidirectional (\u001b[38;5;33mBidirectional\u001b[0m)   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m256\u001b[0m)        │     \u001b[38;5;34m6,660,096\u001b[0m │\n",
-              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-              "│ dense (\u001b[38;5;33mDense\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m41\u001b[0m)         │        \u001b[38;5;34m10,537\u001b[0m │\n",
-              "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        },
-        {
-          "data": {
-            "text/html": [
-              "
 Total params: 8,077,684 (30.81 MB)\n",
-              "
\n"
-            ],
-            "text/plain": [
-              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m8,077,684\u001b[0m (30.81 MB)\n"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        },
-        {
-          "data": {
-            "text/html": [
-              "
 Trainable params: 8,077,684 (30.81 MB)\n",
-              "
\n"
-            ],
-            "text/plain": [
-              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m8,077,684\u001b[0m (30.81 MB)\n"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        },
-        {
-          "data": {
-            "text/html": [
-              "
 Non-trainable params: 0 (0.00 B)\n",
-              "
\n"
-            ],
-            "text/plain": [
-              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        }
-      ],
-      "source": [
-        "model.summary()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 38,
-      "id": "f4b4798c-a65a-4c47-9e2a-3b09dc98d320",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "f4b4798c-a65a-4c47-9e2a-3b09dc98d320",
-        "outputId": "2a2066ab-f352-4b02-c64f-d0e6d139d7cf"
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              "6375"
-            ]
-          },
-          "execution_count": 38,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "5*17*75"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 39,
-      "id": "e5c2eae0-c359-41a4-97a0-75c44dccb7d1",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "e5c2eae0-c359-41a4-97a0-75c44dccb7d1",
-        "outputId": "02f5688b-3290-4eef-b0f9-bd61d8b5031b",
-        "tags": []
-      },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2s/step\n"
-          ]
-        }
-      ],
-      "source": [
-        "yhat = model.predict(val[0])"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 40,
-      "id": "ffdc7319-0d69-4f7e-a6d4-ce72deb81c0b",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "ffdc7319-0d69-4f7e-a6d4-ce72deb81c0b",
-        "outputId": "bf6148fc-b7fa-4914-860a-bed361d0be87"
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              ""
-            ]
-          },
-          "execution_count": 40,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "tf.strings.reduce_join([num_to_char(x) for x in tf.argmax(yhat[0],axis=1)])"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 41,
-      "id": "6ed47531-8317-4255-9a12-b757642258e6",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "6ed47531-8317-4255-9a12-b757642258e6",
-        "outputId": "5b755432-4136-4834-fd69-78aace01b57b",
-        "tags": []
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              ""
-            ]
-          },
-          "execution_count": 41,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "tf.strings.reduce_join([num_to_char(tf.argmax(x)) for x in yhat[0]])"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 42,
-      "id": "7c37b9b9-5298-4038-9c33-5031d1b457f0",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "7c37b9b9-5298-4038-9c33-5031d1b457f0",
-        "outputId": "fcdf9c94-35db-4aa0-fcf7-3029f32eac97",
-        "tags": []
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              "(None, 75, 46, 140, 1)"
-            ]
-          },
-          "execution_count": 42,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "a3573a47-3689-4668-b62f-5c8451b2b4e9",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "# 0. Install and Import Dependencies"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "id": "ddfbccbe-41ae-4c23-98b1-a13868e2b499",
+   "metadata": {
+    "scrolled": true,
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Package                   Version\n",
+      "------------------------- --------------\n",
+      "absl-py                   2.1.0\n",
+      "altair                    5.3.0\n",
+      "anyio                     4.3.0\n",
+      "argon2-cffi               23.1.0\n",
+      "argon2-cffi-bindings      21.2.0\n",
+      "arrow                     1.3.0\n",
+      "asttokens                 2.4.1\n",
+      "astunparse                1.6.3\n",
+      "async-lru                 2.0.4\n",
+      "attrs                     23.2.0\n",
+      "av                        12.0.0\n",
+      "Babel                     2.14.0\n",
+      "beautifulsoup4            4.12.3\n",
+      "bleach                    6.1.0\n",
+      "blinker                   1.7.0\n",
+      "cachetools                5.3.3\n",
+      "certifi                   2024.2.2\n",
+      "cffi                      1.16.0\n",
+      "charset-normalizer        3.3.2\n",
+      "click                     8.1.7\n",
+      "colorama                  0.4.6\n",
+      "comm                      0.2.2\n",
+      "contourpy                 1.2.1\n",
+      "cycler                    0.12.1\n",
+      "debugpy                   1.8.1\n",
+      "decorator                 5.1.1\n",
+      "defusedxml                0.7.1\n",
+      "executing                 2.0.1\n",
+      "fastjsonschema            2.19.1\n",
+      "filelock                  3.13.4\n",
+      "flatbuffers               24.3.25\n",
+      "fonttools                 4.51.0\n",
+      "fqdn                      1.5.1\n",
+      "fsspec                    2024.3.1\n",
+      "gast                      0.5.4\n",
+      "gdown                     5.1.0\n",
+      "gitdb                     4.0.11\n",
+      "GitPython                 3.1.43\n",
+      "google-pasta              0.2.0\n",
+      "grpcio                    1.62.1\n",
+      "h11                       0.14.0\n",
+      "h5py                      3.11.0\n",
+      "httpcore                  1.0.5\n",
+      "httpx                     0.27.0\n",
+      "idna                      3.7\n",
+      "imageio                   2.23.0\n",
+      "ipykernel                 6.29.4\n",
+      "ipynb-py-convert          0.4.6\n",
+      "ipython                   8.23.0\n",
+      "ipywidgets                8.1.2\n",
+      "isoduration               20.11.0\n",
+      "jedi                      0.19.1\n",
+      "Jinja2                    3.1.3\n",
+      "json5                     0.9.24\n",
+      "jsonpointer               2.4\n",
+      "jsonschema                4.21.1\n",
+      "jsonschema-specifications 2023.12.1\n",
+      "jupyter                   1.0.0\n",
+      "jupyter_client            8.6.1\n",
+      "jupyter-console           6.6.3\n",
+      "jupyter_core              5.7.2\n",
+      "jupyter-events            0.10.0\n",
+      "jupyter-lsp               2.2.5\n",
+      "jupyter_server            2.13.0\n",
+      "jupyter_server_terminals  0.5.3\n",
+      "jupyterlab                4.1.6\n",
+      "jupyterlab_pygments       0.3.0\n",
+      "jupyterlab_server         2.26.0\n",
+      "jupyterlab_widgets        3.0.10\n",
+      "keras                     3.2.1\n",
+      "kiwisolver                1.4.5\n",
+      "libclang                  18.1.1\n",
+      "Markdown                  3.6\n",
+      "markdown-it-py            3.0.0\n",
+      "MarkupSafe                2.1.5\n",
+      "matplotlib                3.8.4\n",
+      "matplotlib-inline         0.1.6\n",
+      "mdurl                     0.1.2\n",
+      "mistune                   3.0.2\n",
+      "ml-dtypes                 0.3.2\n",
+      "mpmath                    1.3.0\n",
+      "namex                     0.0.7\n",
+      "nbclient                  0.10.0\n",
+      "nbconvert                 7.16.3\n",
+      "nbformat                  5.10.4\n",
+      "nest-asyncio              1.6.0\n",
+      "networkx                  3.3\n",
+      "notebook                  7.1.2\n",
+      "notebook_shim             0.2.4\n",
+      "numpy                     1.26.4\n",
+      "opencv-python             4.6.0.66\n",
+      "opt-einsum                3.3.0\n",
+      "optree                    0.11.0\n",
+      "overrides                 7.7.0\n",
+      "packaging                 24.0\n",
+      "pandas                    2.2.1\n",
+      "pandocfilters             1.5.1\n",
+      "parso                     0.8.4\n",
+      "pillow                    10.3.0\n",
+      "pip                       24.0\n",
+      "platformdirs              4.2.0\n",
+      "prometheus_client         0.20.0\n",
+      "prompt-toolkit            3.0.43\n",
+      "protobuf                  4.25.3\n",
+      "psutil                    5.9.8\n",
+      "pure-eval                 0.2.2\n",
+      "pyarrow                   15.0.2\n",
+      "pycparser                 2.22\n",
+      "pydeck                    0.8.1b0\n",
+      "Pygments                  2.17.2\n",
+      "pyparsing                 3.1.2\n",
+      "PySocks                   1.7.1\n",
+      "python-dateutil           2.9.0.post0\n",
+      "python-json-logger        2.0.7\n",
+      "pytz                      2024.1\n",
+      "pywin32                   306\n",
+      "pywinpty                  2.0.13\n",
+      "PyYAML                    6.0.1\n",
+      "pyzmq                     25.1.2\n",
+      "qtconsole                 5.5.1\n",
+      "QtPy                      2.4.1\n",
+      "referencing               0.34.0\n",
+      "requests                  2.31.0\n",
+      "rfc3339-validator         0.1.4\n",
+      "rfc3986-validator         0.1.1\n",
+      "rich                      13.7.1\n",
+      "rpds-py                   0.18.0\n",
+      "Send2Trash                1.8.3\n",
+      "setuptools                69.2.0\n",
+      "six                       1.16.0\n",
+      "smmap                     5.0.1\n",
+      "sniffio                   1.3.1\n",
+      "soupsieve                 2.5\n",
+      "stack-data                0.6.3\n",
+      "streamlit                 1.33.0\n",
+      "sympy                     1.12\n",
+      "tenacity                  8.2.3\n",
+      "tensorboard               2.16.2\n",
+      "tensorboard-data-server   0.7.2\n",
+      "tensorflow                2.16.1\n",
+      "tensorflow-intel          2.16.1\n",
+      "termcolor                 2.4.0\n",
+      "terminado                 0.18.1\n",
+      "tinycss2                  1.2.1\n",
+      "toml                      0.10.2\n",
+      "toolz                     0.12.1\n",
+      "torch                     2.2.2\n",
+      "torchvision               0.17.2\n",
+      "tornado                   6.4\n",
+      "tqdm                      4.66.2\n",
+      "traitlets                 5.14.2\n",
+      "types-python-dateutil     2.9.0.20240316\n",
+      "typing_extensions         4.11.0\n",
+      "tzdata                    2024.1\n",
+      "uri-template              1.3.0\n",
+      "urllib3                   2.2.1\n",
+      "watchdog                  4.0.0\n",
+      "wcwidth                   0.2.13\n",
+      "webcolors                 1.13\n",
+      "webencodings              0.5.1\n",
+      "websocket-client          1.7.0\n",
+      "Werkzeug                  3.0.2\n",
+      "wheel                     0.43.0\n",
+      "widgetsnbextension        4.0.10\n",
+      "wrapt                     1.16.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip list"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "id": "02f907ea-f669-46c7-adcf-7f257e663448",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Requirement already satisfied: opencv-python in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (4.6.0.66)\n",
+      "Requirement already satisfied: matplotlib in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (3.8.4)\n",
+      "Requirement already satisfied: imageio in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (2.23.0)\n",
+      "Requirement already satisfied: gdown in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (5.1.0)\n",
+      "Requirement already satisfied: tensorflow in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (2.16.1)\n",
+      "Requirement already satisfied: numpy>=1.21.2 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from opencv-python) (1.26.4)\n",
+      "Requirement already satisfied: contourpy>=1.0.1 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from matplotlib) (1.2.1)\n",
+      "Requirement already satisfied: cycler>=0.10 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from matplotlib) (0.12.1)\n",
+      "Requirement already satisfied: fonttools>=4.22.0 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from matplotlib) (4.51.0)\n",
+      "Requirement already satisfied: kiwisolver>=1.3.1 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from matplotlib) (1.4.5)\n",
+      "Requirement already satisfied: packaging>=20.0 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from matplotlib) (24.0)\n",
+      "Requirement already satisfied: pillow>=8 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from matplotlib) (10.3.0)\n",
+      "Requirement already satisfied: pyparsing>=2.3.1 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from matplotlib) (3.1.2)\n",
+      "Requirement already satisfied: python-dateutil>=2.7 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from matplotlib) (2.9.0.post0)\n",
+      "Requirement already satisfied: beautifulsoup4 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from gdown) (4.12.3)\n",
+      "Requirement already satisfied: filelock in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from gdown) (3.13.4)\n",
+      "Requirement already satisfied: requests[socks] in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from gdown) (2.31.0)\n",
+      "Requirement already satisfied: tqdm in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from gdown) (4.66.2)\n",
+      "Requirement already satisfied: tensorflow-intel==2.16.1 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from tensorflow) (2.16.1)\n",
+      "Requirement already satisfied: absl-py>=1.0.0 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from tensorflow-intel==2.16.1->tensorflow) (2.1.0)\n",
+      "Requirement already satisfied: astunparse>=1.6.0 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from tensorflow-intel==2.16.1->tensorflow) (1.6.3)\n",
+      "Requirement already satisfied: flatbuffers>=23.5.26 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from tensorflow-intel==2.16.1->tensorflow) (24.3.25)\n",
+      "Requirement already satisfied: gast!=0.5.0,!=0.5.1,!=0.5.2,>=0.2.1 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from tensorflow-intel==2.16.1->tensorflow) (0.5.4)\n",
+      "Requirement already satisfied: google-pasta>=0.1.1 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from tensorflow-intel==2.16.1->tensorflow) (0.2.0)\n",
+      "Requirement already satisfied: h5py>=3.10.0 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from tensorflow-intel==2.16.1->tensorflow) (3.11.0)\n",
+      "Requirement already satisfied: libclang>=13.0.0 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from tensorflow-intel==2.16.1->tensorflow) (18.1.1)\n",
+      "Requirement already satisfied: ml-dtypes~=0.3.1 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from tensorflow-intel==2.16.1->tensorflow) (0.3.2)\n",
+      "Requirement already satisfied: opt-einsum>=2.3.2 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from tensorflow-intel==2.16.1->tensorflow) (3.3.0)\n",
+      "Requirement already satisfied: protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.20.3 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from tensorflow-intel==2.16.1->tensorflow) (4.25.3)\n",
+      "Requirement already satisfied: setuptools in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from tensorflow-intel==2.16.1->tensorflow) (69.2.0)\n",
+      "Requirement already satisfied: six>=1.12.0 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from tensorflow-intel==2.16.1->tensorflow) (1.16.0)\n",
+      "Requirement already satisfied: termcolor>=1.1.0 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from tensorflow-intel==2.16.1->tensorflow) (2.4.0)\n",
+      "Requirement already satisfied: typing-extensions>=3.6.6 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from tensorflow-intel==2.16.1->tensorflow) (4.11.0)\n",
+      "Requirement already satisfied: wrapt>=1.11.0 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from tensorflow-intel==2.16.1->tensorflow) (1.16.0)\n",
+      "Requirement already satisfied: grpcio<2.0,>=1.24.3 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from tensorflow-intel==2.16.1->tensorflow) (1.62.1)\n",
+      "Requirement already satisfied: tensorboard<2.17,>=2.16 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from tensorflow-intel==2.16.1->tensorflow) (2.16.2)\n",
+      "Requirement already satisfied: keras>=3.0.0 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from tensorflow-intel==2.16.1->tensorflow) (3.2.1)\n",
+      "Requirement already satisfied: soupsieve>1.2 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from beautifulsoup4->gdown) (2.5)\n",
+      "Requirement already satisfied: charset-normalizer<4,>=2 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from requests[socks]->gdown) (3.3.2)\n",
+      "Requirement already satisfied: idna<4,>=2.5 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from requests[socks]->gdown) (3.7)\n",
+      "Requirement already satisfied: urllib3<3,>=1.21.1 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from requests[socks]->gdown) (2.2.1)\n",
+      "Requirement already satisfied: certifi>=2017.4.17 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from requests[socks]->gdown) (2024.2.2)\n",
+      "Requirement already satisfied: PySocks!=1.5.7,>=1.5.6 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from requests[socks]->gdown) (1.7.1)\n",
+      "Requirement already satisfied: colorama in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from tqdm->gdown) (0.4.6)\n",
+      "Requirement already satisfied: wheel<1.0,>=0.23.0 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from astunparse>=1.6.0->tensorflow-intel==2.16.1->tensorflow) (0.43.0)\n",
+      "Requirement already satisfied: rich in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from keras>=3.0.0->tensorflow-intel==2.16.1->tensorflow) (13.7.1)\n",
+      "Requirement already satisfied: namex in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from keras>=3.0.0->tensorflow-intel==2.16.1->tensorflow) (0.0.7)\n",
+      "Requirement already satisfied: optree in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from keras>=3.0.0->tensorflow-intel==2.16.1->tensorflow) (0.11.0)\n",
+      "Requirement already satisfied: markdown>=2.6.8 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from tensorboard<2.17,>=2.16->tensorflow-intel==2.16.1->tensorflow) (3.6)\n",
+      "Requirement already satisfied: tensorboard-data-server<0.8.0,>=0.7.0 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from tensorboard<2.17,>=2.16->tensorflow-intel==2.16.1->tensorflow) (0.7.2)\n",
+      "Requirement already satisfied: werkzeug>=1.0.1 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from tensorboard<2.17,>=2.16->tensorflow-intel==2.16.1->tensorflow) (3.0.2)\n",
+      "Requirement already satisfied: MarkupSafe>=2.1.1 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from werkzeug>=1.0.1->tensorboard<2.17,>=2.16->tensorflow-intel==2.16.1->tensorflow) (2.1.5)\n",
+      "Requirement already satisfied: markdown-it-py>=2.2.0 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from rich->keras>=3.0.0->tensorflow-intel==2.16.1->tensorflow) (3.0.0)\n",
+      "Requirement already satisfied: pygments<3.0.0,>=2.13.0 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from rich->keras>=3.0.0->tensorflow-intel==2.16.1->tensorflow) (2.17.2)\n",
+      "Requirement already satisfied: mdurl~=0.1 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from markdown-it-py>=2.2.0->rich->keras>=3.0.0->tensorflow-intel==2.16.1->tensorflow) (0.1.2)\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install opencv-python matplotlib imageio gdown tensorflow"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "id": "b24af50c-20b8-409d-ad78-30a933fdd669",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import cv2\n",
+    "import tensorflow as tf\n",
+    "import numpy as np\n",
+    "from typing import List\n",
+    "from matplotlib import pyplot as plt\n",
+    "import imageio"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "id": "1e3db0b0-e559-4ad6-91fd-e7414b7d75e6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[]"
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tf.config.list_physical_devices('GPU')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "id": "378d045a-3003-4f93-b7d2-a25a97774a68",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "physical_devices = tf.config.list_physical_devices('GPU')\n",
+    "try:\n",
+    "    tf.config.experimental.set_memory_growth(physical_devices[0], True)\n",
+    "except:\n",
+    "    pass"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7a19e88e-c7b9-45c1-ae1e-f2109329c71b",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "# 1. Build Data Loading Functions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "id": "8fb99c90-e05a-437f-839d-6e772f8c1dd5",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import gdown"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "id": "c019e4c6-2af3-4160-99ea-5c8cb009f1a7",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# url = 'https://drive.google.com/uc?id=1YlvpDLix3S-U8fd-gqRwPcWXAXm8JwjL'\n",
+    "# output = 'data.zip'\n",
+    "# gdown.download(url, output, quiet=False)\n",
+    "# gdown.extractall('data.zip')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "id": "8548cc59-6dfc-4acc-abc3-3e65212db02e",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "def load_video(path:str) -> List[float]:\n",
+    "\n",
+    "    cap = cv2.VideoCapture(path)\n",
+    "    frames = []\n",
+    "    for _ in range(int(cap.get(cv2.CAP_PROP_FRAME_COUNT))):\n",
+    "        ret, frame = cap.read()\n",
+    "        frame = tf.image.rgb_to_grayscale(frame)\n",
+    "        frames.append(frame[190:236,80:220,:])\n",
+    "    cap.release()\n",
+    "\n",
+    "    mean = tf.math.reduce_mean(frames)\n",
+    "    std = tf.math.reduce_std(tf.cast(frames, tf.float32))\n",
+    "    return tf.cast((frames - mean), tf.float32) / std"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "id": "ec735e0b-ec98-4eb0-8f49-c35527d6670a",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "vocab = [x for x in \"abcdefghijklmnopqrstuvwxyz'?!123456789 \"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "id": "be04e972-d7a5-4a72-82d8-a6bdde1f3ce6",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The vocabulary is: ['', 'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z', \"'\", '?', '!', '1', '2', '3', '4', '5', '6', '7', '8', '9', ' '] (size =40)\n"
+     ]
+    }
+   ],
+   "source": [
+    "char_to_num = tf.keras.layers.StringLookup(vocabulary=vocab, oov_token=\"\")\n",
+    "num_to_char = tf.keras.layers.StringLookup(\n",
+    "    vocabulary=char_to_num.get_vocabulary(), oov_token=\"\", invert=True\n",
+    ")\n",
+    "\n",
+    "print(\n",
+    "    f\"The vocabulary is: {char_to_num.get_vocabulary()} \"\n",
+    "    f\"(size ={char_to_num.vocabulary_size()})\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "id": "559f7420-6802-45fa-9ca0-b1ff209b461c",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['',\n",
+       " 'a',\n",
+       " 'b',\n",
+       " 'c',\n",
+       " 'd',\n",
+       " 'e',\n",
+       " 'f',\n",
+       " 'g',\n",
+       " 'h',\n",
+       " 'i',\n",
+       " 'j',\n",
+       " 'k',\n",
+       " 'l',\n",
+       " 'm',\n",
+       " 'n',\n",
+       " 'o',\n",
+       " 'p',\n",
+       " 'q',\n",
+       " 'r',\n",
+       " 's',\n",
+       " 't',\n",
+       " 'u',\n",
+       " 'v',\n",
+       " 'w',\n",
+       " 'x',\n",
+       " 'y',\n",
+       " 'z',\n",
+       " \"'\",\n",
+       " '?',\n",
+       " '!',\n",
+       " '1',\n",
+       " '2',\n",
+       " '3',\n",
+       " '4',\n",
+       " '5',\n",
+       " '6',\n",
+       " '7',\n",
+       " '8',\n",
+       " '9',\n",
+       " ' ']"
+      ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "char_to_num.get_vocabulary()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "id": "797ff78b-b48f-4e14-bb62-8cd0ebf9501a",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       ""
+      ]
+     },
+     "execution_count": 65,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "char_to_num(['n','i','c','k'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "id": "8cd7f4f4-ae77-4509-a4f4-c723787ebad1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       ""
+      ]
+     },
+     "execution_count": 66,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "num_to_char([14,  9,  3, 11])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "id": "9491bab5-6a3c-4f79-879a-8f9fbe73ae2e",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "def load_alignments(path:str) -> List[str]:\n",
+    "    with open(path, 'r') as f:\n",
+    "        lines = f.readlines()\n",
+    "    tokens = []\n",
+    "    for line in lines:\n",
+    "        line = line.split()\n",
+    "        if line[2] != 'sil':\n",
+    "            tokens = [*tokens,' ',line[2]]\n",
+    "    return char_to_num(tf.reshape(tf.strings.unicode_split(tokens, input_encoding='UTF-8'), (-1)))[1:]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "id": "dd01ca9f-77fb-4643-a2aa-47dd82c5d66b",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "def load_data(path: str):\n",
+    "    path = bytes.decode(path.numpy())\n",
+    "    #file_name = path.split('/')[-1].split('.')[0]\n",
+    "    # File name splitting for windows\n",
+    "    file_name = path.split('\\\\')[-1].split('.')[0]\n",
+    "    video_path = os.path.join('data','s1',f'{file_name}.mpg')\n",
+    "    alignment_path = os.path.join('data','alignments','s1',f'{file_name}.align')\n",
+    "    frames = load_video(video_path)\n",
+    "    alignments = load_alignments(alignment_path)\n",
+    "\n",
+    "    return frames, alignments"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "id": "8cb7cc58-31ae-4904-a805-1177a82717d2",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "test_path = '.\\\\data\\\\s1\\\\bbal6n.mpg'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "id": "76aa964f-0c84-490d-897a-d00e3966e2c9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'bbal6n'"
+      ]
+     },
+     "execution_count": 70,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tf.convert_to_tensor(test_path).numpy().decode('utf-8').split('\\\\')[-1].split('.')[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "id": "eb602c71-8560-4f9e-b26b-08202febb937",
+   "metadata": {
+    "scrolled": true,
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "frames, alignments = load_data(tf.convert_to_tensor(test_path))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "id": "0e3184a1-6b02-4b4f-84a8-a0a65f951ea2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       ""
+      ]
+     },
+     "execution_count": 72,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "
"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.imshow(frames[40])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "id": "d7ec0833-d54b-4073-84cf-92d011c60ec1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       ""
+      ]
+     },
+     "execution_count": 73,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "alignments"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "id": "fe1ad370-b287-4b46-85a2-7c45b0bd9b10",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       ""
+      ]
+     },
+     "execution_count": 74,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tf.strings.reduce_join([bytes.decode(x) for x in num_to_char(alignments.numpy()).numpy()])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "id": "6871031a-b0ba-4c76-a852-f6329b0f2606",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "def mappable_function(path:str) ->List[str]:\n",
+    "    result = tf.py_function(load_data, [path], (tf.float32, tf.int64))\n",
+    "    return result"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c40a7eb4-0c3e-4eab-9291-5611cb68ce08",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "# 2. Create Data Pipeline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "id": "7686355d-45aa-4c85-ad9c-053e6a9b4d81",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from matplotlib import pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "id": "f066fea2-91b1-42ed-a67d-00566a1a53ff",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "data = tf.data.Dataset.list_files('./data/s1/*.mpg')\n",
+    "data = data.shuffle(500, reshuffle_each_iteration=False)\n",
+    "data = data.map(mappable_function)\n",
+    "data = data.padded_batch(2, padded_shapes=([75,None,None,None],[40]))\n",
+    "data = data.prefetch(tf.data.AUTOTUNE)\n",
+    "# Added for split\n",
+    "train = data.take(450)\n",
+    "test = data.skip(450)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "id": "6b1365bd-7742-41d1-95d4-247021751c3a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "50"
+      ]
+     },
+     "execution_count": 78,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "id": "5281bde8-fdc8-4da1-bd55-5a7929a9e80c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "frames, alignments = data.as_numpy_iterator().next()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "id": "cbebe683-6afd-47fd-bba4-c83b4b13bb32",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2"
+      ]
+     },
+     "execution_count": 80,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(frames)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "id": "5cf2d676-93a9-434c-b3c7-bdcc2577b2e7",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "sample = data.as_numpy_iterator()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "id": "efa6cd46-7079-46c0-b45b-832f339f6cb0",
+   "metadata": {
+    "scrolled": true,
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[[[[1.5723375 ],\n",
+       "          [1.6097741 ],\n",
+       "          [1.5723375 ],\n",
+       "          ...,\n",
+       "          [0.33692947],\n",
+       "          [0.33692947],\n",
+       "          [0.33692947]],\n",
+       "\n",
+       "         [[1.6097741 ],\n",
+       "          [1.6472107 ],\n",
+       "          [1.6097741 ],\n",
+       "          ...,\n",
+       "          [0.33692947],\n",
+       "          [0.33692947],\n",
+       "          [0.33692947]],\n",
+       "\n",
+       "         [[1.4974643 ],\n",
+       "          [1.4974643 ],\n",
+       "          [1.4974643 ],\n",
+       "          ...,\n",
+       "          [0.37436607],\n",
+       "          [0.29949287],\n",
+       "          [0.29949287]],\n",
+       "\n",
+       "         ...,\n",
+       "\n",
+       "         [[0.9733518 ],\n",
+       "          [0.9733518 ],\n",
+       "          [0.9733518 ],\n",
+       "          ...,\n",
+       "          [0.07487322],\n",
+       "          [0.07487322],\n",
+       "          [0.07487322]],\n",
+       "\n",
+       "         [[0.9733518 ],\n",
+       "          [0.9733518 ],\n",
+       "          [0.9733518 ],\n",
+       "          ...,\n",
+       "          [0.07487322],\n",
+       "          [0.07487322],\n",
+       "          [0.07487322]],\n",
+       "\n",
+       "         [[0.9733518 ],\n",
+       "          [0.9733518 ],\n",
+       "          [0.9733518 ],\n",
+       "          ...,\n",
+       "          [0.07487322],\n",
+       "          [0.07487322],\n",
+       "          [0.03743661]]],\n",
+       "\n",
+       "\n",
+       "        [[[1.5349009 ],\n",
+       "          [1.6097741 ],\n",
+       "          [1.4600277 ],\n",
+       "          ...,\n",
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+      ]
+     },
+     "execution_count": 82,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "val = sample.next(); val[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "id": "acf5eb4f-a0da-4a9a-bf24-af13e9cc2fbe",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Lossy conversion from float32 to uint8. Range [0.0, 9.546335220336914]. Convert image to uint8 prior to saving to suppress this warning.\n",
+      "Lossy conversion from float32 to uint8. Range [0.0, 9.546335220336914]. Convert image to uint8 prior to saving to suppress this warning.\n",
+      "Lossy conversion from float32 to uint8. Range [0.0, 9.546335220336914]. Convert image to uint8 prior to saving to suppress this warning.\n",
+      "Lossy conversion from float32 to uint8. Range [0.0, 9.546335220336914]. Convert image to uint8 prior to saving to suppress this warning.\n",
+      "Lossy conversion from float32 to uint8. Range [0.0, 9.546335220336914]. Convert image to uint8 prior to saving to suppress this warning.\n",
+      "Lossy conversion from float32 to uint8. Range [0.0, 9.546335220336914]. Convert image to uint8 prior to saving to suppress this warning.\n",
+      "Lossy conversion from float32 to uint8. Range [0.0, 9.546335220336914]. Convert image to uint8 prior to saving to suppress this warning.\n",
+      "Lossy conversion from float32 to uint8. Range [0.0, 9.546335220336914]. Convert image to uint8 prior to saving to suppress this warning.\n",
+      "Lossy conversion from float32 to uint8. Range [0.0, 9.546335220336914]. Convert image to uint8 prior to saving to suppress this warning.\n",
+      "Lossy conversion from float32 to uint8. Range [0.0, 9.546335220336914]. Convert image to uint8 prior to saving to suppress this warning.\n",
+      "Lossy conversion from float32 to uint8. Range [0.0, 9.546335220336914]. Convert image to uint8 prior to saving to suppress this warning.\n",
+      "Lossy conversion from float32 to uint8. Range [0.0, 9.546335220336914]. Convert image to uint8 prior to saving to suppress this warning.\n",
+      "Lossy conversion from float32 to uint8. Range [0.0, 9.546335220336914]. Convert image to uint8 prior to saving to suppress this warning.\n",
+      "Lossy conversion from float32 to uint8. Range [0.0, 9.546335220336914]. Convert image to uint8 prior to saving to suppress this warning.\n",
+      "Lossy conversion from float32 to uint8. Range [0.0, 9.546335220336914]. Convert image to uint8 prior to saving to suppress this warning.\n",
+      "Lossy conversion from float32 to uint8. Range [0.0, 9.546335220336914]. Convert image to uint8 prior to saving to suppress this warning.\n",
+      "Lossy conversion from float32 to uint8. Range [0.0, 9.546335220336914]. Convert image to uint8 prior to saving to suppress this warning.\n",
+      "Lossy conversion from float32 to uint8. Range [0.0, 9.546335220336914]. Convert image to uint8 prior to saving to suppress this warning.\n",
+      "Lossy conversion from float32 to uint8. Range [0.0, 9.546335220336914]. Convert image to uint8 prior to saving to suppress this warning.\n",
+      "Lossy conversion from float32 to uint8. Range [0.0, 9.546335220336914]. Convert image to uint8 prior to saving to suppress this warning.\n",
+      "Lossy conversion from float32 to uint8. Range [0.0, 9.546335220336914]. Convert image to uint8 prior to saving to suppress this warning.\n",
+      "Lossy conversion from float32 to uint8. Range [0.0, 9.546335220336914]. Convert image to uint8 prior to saving to suppress this warning.\n",
+      "Lossy conversion from float32 to uint8. Range [0.0, 9.546335220336914]. Convert image to uint8 prior to saving to suppress this warning.\n",
+      "Lossy conversion from float32 to uint8. Range [0.0, 9.546335220336914]. Convert image to uint8 prior to saving to suppress this warning.\n",
+      "Lossy conversion from float32 to uint8. Range [0.0, 9.546335220336914]. Convert image to uint8 prior to saving to suppress this warning.\n",
+      "Lossy conversion from float32 to uint8. Range [0.0, 9.546335220336914]. Convert image to uint8 prior to saving to suppress this warning.\n",
+      "Lossy conversion from float32 to uint8. Range [0.0, 9.546335220336914]. Convert image to uint8 prior to saving to suppress this warning.\n",
+      "Lossy conversion from float32 to uint8. Range [0.0, 9.546335220336914]. Convert image to uint8 prior to saving to suppress this warning.\n",
+      "Lossy conversion from float32 to uint8. Range [0.0, 9.546335220336914]. Convert image to uint8 prior to saving to suppress this warning.\n",
+      "Lossy conversion from float32 to uint8. Range [0.0, 9.546335220336914]. Convert image to uint8 prior to saving to suppress this warning.\n",
+      "Lossy conversion from float32 to uint8. Range [0.0, 9.546335220336914]. Convert image to uint8 prior to saving to suppress this warning.\n",
+      "Lossy conversion from float32 to uint8. Range [0.0, 9.546335220336914]. Convert image to uint8 prior to saving to suppress this warning.\n",
+      "Lossy conversion from float32 to uint8. Range [0.0, 9.546335220336914]. Convert image to uint8 prior to saving to suppress this warning.\n",
+      "Lossy conversion from float32 to uint8. Range [0.0, 9.546335220336914]. Convert image to uint8 prior to saving to suppress this warning.\n",
+      "Lossy conversion from float32 to uint8. Range [0.0, 9.546335220336914]. Convert image to uint8 prior to saving to suppress this warning.\n",
+      "Lossy conversion from float32 to uint8. Range [0.0, 9.546335220336914]. Convert image to uint8 prior to saving to suppress this warning.\n",
+      "Lossy conversion from float32 to uint8. Range [0.0, 9.546335220336914]. Convert image to uint8 prior to saving to suppress this warning.\n",
+      "Lossy conversion from float32 to uint8. Range [0.0, 9.546335220336914]. Convert image to uint8 prior to saving to suppress this warning.\n",
+      "Lossy conversion from float32 to uint8. Range [0.0, 9.546335220336914]. Convert image to uint8 prior to saving to suppress this warning.\n",
+      "Lossy conversion from float32 to uint8. Range [0.0, 9.546335220336914]. Convert image to uint8 prior to saving to suppress this warning.\n",
+      "Lossy conversion from float32 to uint8. Range [0.0, 9.546335220336914]. Convert image to uint8 prior to saving to suppress this warning.\n",
+      "Lossy conversion from float32 to uint8. Range [0.0, 9.546335220336914]. Convert image to uint8 prior to saving to suppress this warning.\n",
+      "Lossy conversion from float32 to uint8. Range [0.0, 9.546335220336914]. Convert image to uint8 prior to saving to suppress this warning.\n",
+      "Lossy conversion from float32 to uint8. Range [0.0, 9.546335220336914]. Convert image to uint8 prior to saving to suppress this warning.\n",
+      "Lossy conversion from float32 to uint8. Range [0.0, 9.546335220336914]. Convert image to uint8 prior to saving to suppress this warning.\n",
+      "Lossy conversion from float32 to uint8. Range [0.0, 9.546335220336914]. Convert image to uint8 prior to saving to suppress this warning.\n",
+      "Lossy conversion from float32 to uint8. Range [0.0, 9.546335220336914]. Convert image to uint8 prior to saving to suppress this warning.\n",
+      "Lossy conversion from float32 to uint8. Range [0.0, 9.546335220336914]. Convert image to uint8 prior to saving to suppress this warning.\n",
+      "Lossy conversion from float32 to uint8. Range [0.0, 9.546335220336914]. Convert image to uint8 prior to saving to suppress this warning.\n",
+      "Lossy conversion from float32 to uint8. Range [0.0, 9.546335220336914]. Convert image to uint8 prior to saving to suppress this warning.\n",
+      "Lossy conversion from float32 to uint8. Range [0.0, 9.546335220336914]. Convert image to uint8 prior to saving to suppress this warning.\n",
+      "Lossy conversion from float32 to uint8. Range [0.0, 9.546335220336914]. Convert image to uint8 prior to saving to suppress this warning.\n",
+      "Lossy conversion from float32 to uint8. Range [0.0, 9.546335220336914]. Convert image to uint8 prior to saving to suppress this warning.\n",
+      "Lossy conversion from float32 to uint8. Range [0.0, 9.546335220336914]. Convert image to uint8 prior to saving to suppress this warning.\n",
+      "Lossy conversion from float32 to uint8. Range [0.0, 9.546335220336914]. Convert image to uint8 prior to saving to suppress this warning.\n",
+      "Lossy conversion from float32 to uint8. Range [0.0, 9.546335220336914]. Convert image to uint8 prior to saving to suppress this warning.\n",
+      "Lossy conversion from float32 to uint8. Range [0.0, 9.546335220336914]. Convert image to uint8 prior to saving to suppress this warning.\n",
+      "Lossy conversion from float32 to uint8. Range [0.0, 9.546335220336914]. Convert image to uint8 prior to saving to suppress this warning.\n",
+      "Lossy conversion from float32 to uint8. Range [0.0, 9.546335220336914]. Convert image to uint8 prior to saving to suppress this warning.\n",
+      "Lossy conversion from float32 to uint8. Range [0.0, 9.546335220336914]. Convert image to uint8 prior to saving to suppress this warning.\n",
+      "Lossy conversion from float32 to uint8. Range [0.0, 9.546335220336914]. Convert image to uint8 prior to saving to suppress this warning.\n",
+      "Lossy conversion from float32 to uint8. Range [0.0, 9.546335220336914]. Convert image to uint8 prior to saving to suppress this warning.\n",
+      "Lossy conversion from float32 to uint8. Range [0.0, 9.546335220336914]. Convert image to uint8 prior to saving to suppress this warning.\n",
+      "Lossy conversion from float32 to uint8. Range [0.0, 9.546335220336914]. Convert image to uint8 prior to saving to suppress this warning.\n",
+      "Lossy conversion from float32 to uint8. Range [0.0, 9.546335220336914]. Convert image to uint8 prior to saving to suppress this warning.\n",
+      "Lossy conversion from float32 to uint8. Range [0.0, 9.546335220336914]. Convert image to uint8 prior to saving to suppress this warning.\n",
+      "Lossy conversion from float32 to uint8. Range [0.0, 9.546335220336914]. Convert image to uint8 prior to saving to suppress this warning.\n",
+      "Lossy conversion from float32 to uint8. Range [0.0, 9.546335220336914]. Convert image to uint8 prior to saving to suppress this warning.\n",
+      "Lossy conversion from float32 to uint8. Range [0.0, 9.546335220336914]. Convert image to uint8 prior to saving to suppress this warning.\n",
+      "Lossy conversion from float32 to uint8. Range [0.0, 9.546335220336914]. Convert image to uint8 prior to saving to suppress this warning.\n",
+      "Lossy conversion from float32 to uint8. Range [0.0, 9.546335220336914]. Convert image to uint8 prior to saving to suppress this warning.\n",
+      "Lossy conversion from float32 to uint8. Range [0.0, 9.546335220336914]. Convert image to uint8 prior to saving to suppress this warning.\n",
+      "Lossy conversion from float32 to uint8. Range [0.0, 9.546335220336914]. Convert image to uint8 prior to saving to suppress this warning.\n",
+      "Lossy conversion from float32 to uint8. Range [0.0, 9.546335220336914]. Convert image to uint8 prior to saving to suppress this warning.\n",
+      "Lossy conversion from float32 to uint8. Range [0.0, 9.546335220336914]. Convert image to uint8 prior to saving to suppress this warning.\n"
+     ]
+    }
+   ],
+   "source": [
+    "imageio.mimsave('./animation.gif', val[0][0], fps=10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "id": "c33a87a2-d5e0-4ec9-b174-73ebf41bf03a",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       ""
+      ]
+     },
+     "execution_count": 84,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "
"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# 0:videos, 0: 1st video out of the batch,  0: return the first frame in the video\n",
+    "plt.imshow(val[0][0][35])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "id": "84593332-133c-4205-b7a6-8e235d5e2b3b",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       ""
+      ]
+     },
+     "execution_count": 85,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tf.strings.reduce_join([num_to_char(word) for word in val[1][0]])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0f47733c-83bc-465c-b118-b198b492ad37",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "# 3. Design the Deep Neural Network"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "id": "d8e9a497-191b-4842-afbd-26f5e13c43ba",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import tensorflow\n",
+    "from tensorflow import keras\n",
+    "from keras.models import Sequential\n",
+    "from keras.layers import Conv3D, LSTM, Dense, Dropout, Bidirectional, MaxPool3D, Activation, Reshape, SpatialDropout3D, BatchNormalization, TimeDistributed, Flatten\n",
+    "from keras.optimizers import Adam\n",
+    "from keras.callbacks import ModelCheckpoint, LearningRateScheduler"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 93,
+   "id": "3f753ed2-70b9-4236-8c1c-08ca065dc8bf",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(75, 46, 140, 1)"
+      ]
+     },
+     "execution_count": 93,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.as_numpy_iterator().next()[0][0].shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 94,
+   "id": "f9171056-a352-491a-9ed9-92b28ced268e",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "model = Sequential()\n",
+    "model.add(Conv3D(128, 3, input_shape=(75,46,140,1), padding='same'))\n",
+    "model.add(Activation('relu'))\n",
+    "model.add(MaxPool3D((1,2,2)))\n",
+    "\n",
+    "model.add(Conv3D(256, 3, padding='same'))\n",
+    "model.add(Activation('relu'))\n",
+    "model.add(MaxPool3D((1,2,2)))\n",
+    "\n",
+    "model.add(Conv3D(75, 3, padding='same'))\n",
+    "model.add(Activation('relu'))\n",
+    "model.add(MaxPool3D((1,2,2)))\n",
+    "\n",
+    "model.add(TimeDistributed(Flatten()))\n",
+    "\n",
+    "initializer = keras.initializers.Orthogonal()\n",
+    "model.add(Bidirectional(LSTM(128, kernel_initializer=initializer, return_sequences=True)))\n",
+    "model.add(Dropout(.5))\n",
+    "\n",
+    "model.add(Bidirectional(LSTM(128, kernel_initializer=initializer, return_sequences=True)))\n",
+    "model.add(Dropout(.5))\n",
+    "\n",
+    "model.add(Dense(char_to_num.vocabulary_size()+1, kernel_initializer='he_normal', activation='softmax'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "id": "78851825-2bcd-42a9-b7f2-28bb5a6bf43a",
+   "metadata": {
+    "collapsed": true,
+    "jupyter": {
+     "outputs_hidden": true
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "
Model: \"sequential_3\"\n",
+       "
\n"
       ],
-      "source": [
-        "model.input_shape"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 43,
-      "id": "98b316a4-5322-4782-8e36-4b3c1a696d85",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "98b316a4-5322-4782-8e36-4b3c1a696d85",
-        "outputId": "5f9ebad7-b4cc-4ac8-c5e6-f5471120f50f",
-        "tags": []
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              "(None, 75, 41)"
-            ]
-          },
-          "execution_count": 43,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
+      "text/plain": [
+       "\u001b[1mModel: \"sequential_3\"\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+       "┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃\n",
+       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+       "│ conv3d_9 (Conv3D)               │ (None, 75, 46, 140,    │         3,584 │\n",
+       "│                                 │ 128)                   │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ activation_9 (Activation)       │ (None, 75, 46, 140,    │             0 │\n",
+       "│                                 │ 128)                   │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ max_pooling3d_9 (MaxPooling3D)  │ (None, 75, 23, 70,     │             0 │\n",
+       "│                                 │ 128)                   │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ conv3d_10 (Conv3D)              │ (None, 75, 23, 70,     │       884,992 │\n",
+       "│                                 │ 256)                   │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ activation_10 (Activation)      │ (None, 75, 23, 70,     │             0 │\n",
+       "│                                 │ 256)                   │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ max_pooling3d_10 (MaxPooling3D) │ (None, 75, 11, 35,     │             0 │\n",
+       "│                                 │ 256)                   │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ conv3d_11 (Conv3D)              │ (None, 75, 11, 35, 75) │       518,475 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ activation_11 (Activation)      │ (None, 75, 11, 35, 75) │             0 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ max_pooling3d_11 (MaxPooling3D) │ (None, 75, 5, 17, 75)  │             0 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ time_distributed_3              │ (None, 75, 6375)       │             0 │\n",
+       "│ (TimeDistributed)               │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ bidirectional_3 (Bidirectional) │ (None, 75, 256)        │     6,660,096 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dropout_2 (Dropout)             │ (None, 75, 256)        │             0 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ bidirectional_4 (Bidirectional) │ (None, 75, 256)        │       394,240 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dropout_3 (Dropout)             │ (None, 75, 256)        │             0 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_1 (Dense)                 │ (None, 75, 41)         │        10,537 │\n",
+       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
+       "
\n"
       ],
-      "source": [
-        "model.output_shape"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "2ec02176-5c26-46c3-aff7-8352e6563c7d",
-      "metadata": {
-        "id": "2ec02176-5c26-46c3-aff7-8352e6563c7d",
-        "tags": []
-      },
-      "source": [
-        "# 4. Setup Training Options and Train"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 44,
-      "id": "ab015fd0-7fb4-4d5d-9fa2-30a05dbd515a",
-      "metadata": {
-        "id": "ab015fd0-7fb4-4d5d-9fa2-30a05dbd515a",
-        "tags": []
-      },
-      "outputs": [],
-      "source": [
-        "def scheduler(epoch, lr):\n",
-        "    if epoch < 30:\n",
-        "        return lr\n",
-        "    else:\n",
-        "        return lr * tf.math.exp(-0.1)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 45,
-      "id": "c564d5c9-db54-4e88-b311-9aeab7fb3e69",
-      "metadata": {
-        "id": "c564d5c9-db54-4e88-b311-9aeab7fb3e69",
-        "tags": []
-      },
-      "outputs": [],
-      "source": [
-        "def CTCLoss(y_true, y_pred):\n",
-        "    batch_len = tf.cast(tf.shape(y_true)[0], dtype=\"int64\")\n",
-        "    input_length = tf.cast(tf.shape(y_pred)[1], dtype=\"int64\")\n",
-        "    label_length = tf.cast(tf.shape(y_true)[1], dtype=\"int64\")\n",
-        "\n",
-        "    input_length = input_length * tf.ones(shape=(batch_len, 1), dtype=\"int64\")\n",
-        "    label_length = label_length * tf.ones(shape=(batch_len, 1), dtype=\"int64\")\n",
-        "\n",
-        "    # loss = tf.keras.backend.ctc_batch_cost(y_true, y_pred, input_length, label_length)\n",
-        "    loss = keras.backend.ctc_batch_cost(y_true, y_pred, input_length, label_length)\n",
-        "    return loss"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 46,
-      "id": "a26dc3fc-a19c-4378-bd8c-e2b597a1d15c",
-      "metadata": {
-        "id": "a26dc3fc-a19c-4378-bd8c-e2b597a1d15c",
-        "tags": []
-      },
-      "outputs": [],
-      "source": [
-        "class ProduceExample(keras.callbacks.Callback):\n",
-        "    def __init__(self, dataset) -> None:\n",
-        "        self.dataset = dataset.as_numpy_iterator()\n",
-        "\n",
-        "    def on_epoch_end(self, epoch, logs=None) -> None:\n",
-        "        data = self.dataset.next()\n",
-        "        yhat = self.model.predict(data[0])\n",
-        "        decoded = keras.backend.ctc_decode(yhat, [75,75], greedy=False)[0][0].numpy()\n",
-        "        for x in range(len(yhat)):\n",
-        "            print('Original:', tf.strings.reduce_join(num_to_char(data[1][x])).numpy().decode('utf-8'))\n",
-        "            print('Prediction:', tf.strings.reduce_join(num_to_char(decoded[x])).numpy().decode('utf-8'))\n",
-        "            print('~'*100)\n",
-        "\n",
-        "# class ProduceExample(tf.keras.callbacks.Callback):\n",
-        "#     def __init__(self, dataset) -> None:\n",
-        "#         self.dataset = dataset.as_numpy_iterator()\n",
-        "\n",
-        "#     def on_epoch_end(self, epoch, logs=None) -> None:\n",
-        "#         data = self.dataset.next()\n",
-        "#         yhat = self.model.predict(data[0])\n",
-        "#         decoded = tf.keras.backend.ctc_decode(yhat, [75,75], greedy=False)[0][0].numpy()\n",
-        "#         for x in range(len(yhat)):\n",
-        "#             print('Original:', tf.strings.reduce_join(num_to_char(data[1][x])).numpy().decode('utf-8'))\n",
-        "#             print('Prediction:', tf.strings.reduce_join(num_to_char(decoded[x])).numpy().decode('utf-8'))\n",
-        "#             print('~'*100)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 47,
-      "id": "04be90d8-2482-46f9-b513-d5f4f8001c7e",
-      "metadata": {
-        "id": "04be90d8-2482-46f9-b513-d5f4f8001c7e",
-        "tags": []
-      },
-      "outputs": [],
-      "source": [
-        "model.compile(optimizer=Adam(learning_rate=0.0001), loss=CTCLoss)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 48,
-      "id": "eab49367-3f1e-4464-ae76-dbd07549d97e",
-      "metadata": {
-        "id": "eab49367-3f1e-4464-ae76-dbd07549d97e",
-        "tags": []
-      },
-      "outputs": [],
-      "source": [
-        "# checkpoint_callback = ModelCheckpoint(os.path.join('models','checkpoint'), monitor='loss', save_weights_only=True)\n",
-        "\n",
-        "\n",
-        "# Adjust the file path to end with '.weights.h5'\n",
-        "checkpoint_path = os.path.join('models', 'checkpoint.weights.h5')\n",
-        "\n",
-        "# Create the ModelCheckpoint callback\n",
-        "checkpoint_callback = ModelCheckpoint(\n",
-        "    filepath=checkpoint_path,\n",
-        "    monitor='loss',\n",
-        "    save_weights_only=True\n",
-        ")\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 49,
-      "id": "e085a632-d464-46ef-8777-959cad4adb2c",
-      "metadata": {
-        "id": "e085a632-d464-46ef-8777-959cad4adb2c",
-        "tags": []
-      },
-      "outputs": [],
-      "source": [
-        "schedule_callback = LearningRateScheduler(scheduler)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 50,
-      "id": "48eca991-90ab-4592-8a79-b50e9ca015b6",
-      "metadata": {
-        "id": "48eca991-90ab-4592-8a79-b50e9ca015b6",
-        "tags": []
-      },
-      "outputs": [],
-      "source": [
-        "example_callback = ProduceExample(test)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 52,
-      "id": "8ffba483-aa61-4bbe-a15f-a73e1ddf097c",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "8ffba483-aa61-4bbe-a15f-a73e1ddf097c",
-        "outputId": "5ac8771e-cd3b-4d4d-eaf5-15add3c1e8ae",
-        "tags": []
-      },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Epoch 1/100\n"
-          ]
-        },
-        {
-          "ename": "InvalidArgumentError",
-          "evalue": "Graph execution error:\n\nDetected at node sequential_1/time_distributed_1/Reshape_43 defined at (most recent call last):\n  File \"\", line 198, in _run_module_as_main\n\n  File \"\", line 88, in _run_code\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel_launcher.py\", line 18, in \n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\traitlets\\config\\application.py\", line 1075, in launch_instance\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel\\kernelapp.py\", line 739, in start\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\tornado\\platform\\asyncio.py\", line 205, in start\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\asyncio\\base_events.py\", line 641, in run_forever\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\asyncio\\base_events.py\", line 1987, in _run_once\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\asyncio\\events.py\", line 88, in _run\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 545, in dispatch_queue\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 534, in process_one\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 437, in dispatch_shell\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel\\ipkernel.py\", line 362, in execute_request\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 778, in execute_request\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel\\ipkernel.py\", line 449, in do_execute\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel\\zmqshell.py\", line 549, in run_cell\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3075, in run_cell\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3130, in _run_cell\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\async_helpers.py\", line 129, in _pseudo_sync_runner\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3334, in run_cell_async\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3517, in run_ast_nodes\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3577, in run_code\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Temp\\ipykernel_17328\\3536403359.py\", line 1, in \n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 117, in error_handler\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\backend\\tensorflow\\trainer.py\", line 329, in fit\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\backend\\tensorflow\\trainer.py\", line 122, in one_step_on_iterator\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\backend\\tensorflow\\trainer.py\", line 110, in one_step_on_data\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\backend\\tensorflow\\trainer.py\", line 57, in train_step\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 117, in error_handler\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\layers\\layer.py\", line 825, in __call__\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 117, in error_handler\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\ops\\operation.py\", line 48, in __call__\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 156, in error_handler\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\models\\sequential.py\", line 202, in call\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\models\\functional.py\", line 195, in call\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\ops\\function.py\", line 151, in _run_through_graph\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\models\\functional.py\", line 579, in call\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 117, in error_handler\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\layers\\layer.py\", line 825, in __call__\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 117, in error_handler\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\ops\\operation.py\", line 48, in __call__\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 156, in error_handler\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\layers\\rnn\\time_distributed.py\", line 108, in call\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\layers\\rnn\\time_distributed.py\", line 102, in step_function\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\layers\\reshaping\\flatten.py\", line 54, in call\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\ops\\numpy.py\", line 4522, in reshape\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\backend\\tensorflow\\numpy.py\", line 1618, in reshape\n\nOnly one input size may be -1, not both 0 and 1\n\t [[{{node sequential_1/time_distributed_1/Reshape_43}}]] [Op:__inference_one_step_on_iterator_27512]",
-          "output_type": "error",
-          "traceback": [
-            "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-            "\u001b[1;31mInvalidArgumentError\u001b[0m                      Traceback (most recent call last)",
-            "Cell \u001b[1;32mIn[52], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtrain\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalidation_data\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtest\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mepochs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m100\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcallbacks\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[43mcheckpoint_callback\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mschedule_callback\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mexample_callback\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n",
-            "File \u001b[1;32mc:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\utils\\traceback_utils.py:122\u001b[0m, in \u001b[0;36mfilter_traceback..error_handler\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    119\u001b[0m     filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n\u001b[0;32m    120\u001b[0m     \u001b[38;5;66;03m# To get the full stack trace, call:\u001b[39;00m\n\u001b[0;32m    121\u001b[0m     \u001b[38;5;66;03m# `keras.config.disable_traceback_filtering()`\u001b[39;00m\n\u001b[1;32m--> 122\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m e\u001b[38;5;241m.\u001b[39mwith_traceback(filtered_tb) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m    123\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[0;32m    124\u001b[0m     \u001b[38;5;28;01mdel\u001b[39;00m filtered_tb\n",
-            "File \u001b[1;32mc:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\tensorflow\\python\\eager\\execute.py:53\u001b[0m, in \u001b[0;36mquick_execute\u001b[1;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[0;32m     51\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m     52\u001b[0m   ctx\u001b[38;5;241m.\u001b[39mensure_initialized()\n\u001b[1;32m---> 53\u001b[0m   tensors \u001b[38;5;241m=\u001b[39m pywrap_tfe\u001b[38;5;241m.\u001b[39mTFE_Py_Execute(ctx\u001b[38;5;241m.\u001b[39m_handle, device_name, op_name,\n\u001b[0;32m     54\u001b[0m                                       inputs, attrs, num_outputs)\n\u001b[0;32m     55\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m core\u001b[38;5;241m.\u001b[39m_NotOkStatusException \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m     56\u001b[0m   \u001b[38;5;28;01mif\u001b[39;00m name \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
-            "\u001b[1;31mInvalidArgumentError\u001b[0m: Graph execution error:\n\nDetected at node sequential_1/time_distributed_1/Reshape_43 defined at (most recent call last):\n  File \"\", line 198, in _run_module_as_main\n\n  File \"\", line 88, in _run_code\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel_launcher.py\", line 18, in \n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\traitlets\\config\\application.py\", line 1075, in launch_instance\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel\\kernelapp.py\", line 739, in start\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\tornado\\platform\\asyncio.py\", line 205, in start\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\asyncio\\base_events.py\", line 641, in run_forever\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\asyncio\\base_events.py\", line 1987, in _run_once\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\asyncio\\events.py\", line 88, in _run\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 545, in dispatch_queue\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 534, in process_one\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 437, in dispatch_shell\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel\\ipkernel.py\", line 362, in execute_request\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 778, in execute_request\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel\\ipkernel.py\", line 449, in do_execute\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel\\zmqshell.py\", line 549, in run_cell\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3075, in run_cell\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3130, in _run_cell\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\async_helpers.py\", line 129, in _pseudo_sync_runner\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3334, in run_cell_async\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3517, in run_ast_nodes\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3577, in run_code\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Temp\\ipykernel_17328\\3536403359.py\", line 1, in \n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 117, in error_handler\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\backend\\tensorflow\\trainer.py\", line 329, in fit\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\backend\\tensorflow\\trainer.py\", line 122, in one_step_on_iterator\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\backend\\tensorflow\\trainer.py\", line 110, in one_step_on_data\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\backend\\tensorflow\\trainer.py\", line 57, in train_step\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 117, in error_handler\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\layers\\layer.py\", line 825, in __call__\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 117, in error_handler\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\ops\\operation.py\", line 48, in __call__\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 156, in error_handler\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\models\\sequential.py\", line 202, in call\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\models\\functional.py\", line 195, in call\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\ops\\function.py\", line 151, in _run_through_graph\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\models\\functional.py\", line 579, in call\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 117, in error_handler\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\layers\\layer.py\", line 825, in __call__\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 117, in error_handler\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\ops\\operation.py\", line 48, in __call__\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 156, in error_handler\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\layers\\rnn\\time_distributed.py\", line 108, in call\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\layers\\rnn\\time_distributed.py\", line 102, in step_function\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\layers\\reshaping\\flatten.py\", line 54, in call\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\ops\\numpy.py\", line 4522, in reshape\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\backend\\tensorflow\\numpy.py\", line 1618, in reshape\n\nOnly one input size may be -1, not both 0 and 1\n\t [[{{node sequential_1/time_distributed_1/Reshape_43}}]] [Op:__inference_one_step_on_iterator_27512]"
-          ]
-        }
+      "text/plain": [
+       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
+       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+       "│ conv3d_9 (\u001b[38;5;33mConv3D\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m46\u001b[0m, \u001b[38;5;34m140\u001b[0m,    │         \u001b[38;5;34m3,584\u001b[0m │\n",
+       "│                                 │ \u001b[38;5;34m128\u001b[0m)                   │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ activation_9 (\u001b[38;5;33mActivation\u001b[0m)       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m46\u001b[0m, \u001b[38;5;34m140\u001b[0m,    │             \u001b[38;5;34m0\u001b[0m │\n",
+       "│                                 │ \u001b[38;5;34m128\u001b[0m)                   │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ max_pooling3d_9 (\u001b[38;5;33mMaxPooling3D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m23\u001b[0m, \u001b[38;5;34m70\u001b[0m,     │             \u001b[38;5;34m0\u001b[0m │\n",
+       "│                                 │ \u001b[38;5;34m128\u001b[0m)                   │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ conv3d_10 (\u001b[38;5;33mConv3D\u001b[0m)              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m23\u001b[0m, \u001b[38;5;34m70\u001b[0m,     │       \u001b[38;5;34m884,992\u001b[0m │\n",
+       "│                                 │ \u001b[38;5;34m256\u001b[0m)                   │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ activation_10 (\u001b[38;5;33mActivation\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m23\u001b[0m, \u001b[38;5;34m70\u001b[0m,     │             \u001b[38;5;34m0\u001b[0m │\n",
+       "│                                 │ \u001b[38;5;34m256\u001b[0m)                   │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ max_pooling3d_10 (\u001b[38;5;33mMaxPooling3D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m11\u001b[0m, \u001b[38;5;34m35\u001b[0m,     │             \u001b[38;5;34m0\u001b[0m │\n",
+       "│                                 │ \u001b[38;5;34m256\u001b[0m)                   │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ conv3d_11 (\u001b[38;5;33mConv3D\u001b[0m)              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m11\u001b[0m, \u001b[38;5;34m35\u001b[0m, \u001b[38;5;34m75\u001b[0m) │       \u001b[38;5;34m518,475\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ activation_11 (\u001b[38;5;33mActivation\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m11\u001b[0m, \u001b[38;5;34m35\u001b[0m, \u001b[38;5;34m75\u001b[0m) │             \u001b[38;5;34m0\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ max_pooling3d_11 (\u001b[38;5;33mMaxPooling3D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m17\u001b[0m, \u001b[38;5;34m75\u001b[0m)  │             \u001b[38;5;34m0\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ time_distributed_3              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m6375\u001b[0m)       │             \u001b[38;5;34m0\u001b[0m │\n",
+       "│ (\u001b[38;5;33mTimeDistributed\u001b[0m)               │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ bidirectional_3 (\u001b[38;5;33mBidirectional\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m256\u001b[0m)        │     \u001b[38;5;34m6,660,096\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dropout_2 (\u001b[38;5;33mDropout\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m256\u001b[0m)        │             \u001b[38;5;34m0\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ bidirectional_4 (\u001b[38;5;33mBidirectional\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m256\u001b[0m)        │       \u001b[38;5;34m394,240\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dropout_3 (\u001b[38;5;33mDropout\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m256\u001b[0m)        │             \u001b[38;5;34m0\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_1 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m41\u001b[0m)         │        \u001b[38;5;34m10,537\u001b[0m │\n",
+       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
 Total params: 8,471,924 (32.32 MB)\n",
+       "
\n"
       ],
-      "source": [
-        "model.fit(train, validation_data=test, epochs=100, callbacks=[checkpoint_callback, schedule_callback, example_callback])"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "fa8ee94b-89f7-4733-8a0c-a86f86ff590a",
-      "metadata": {
-        "id": "fa8ee94b-89f7-4733-8a0c-a86f86ff590a",
-        "tags": []
-      },
-      "source": [
-        "# 5. Make a Prediction"
+      "text/plain": [
+       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m8,471,924\u001b[0m (32.32 MB)\n"
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-      "cell_type": "code",
-      "execution_count": null,
-      "id": "01fa7204-ce0e-49a8-8dbd-14fe5dfead40",
-      "metadata": {
-        "id": "01fa7204-ce0e-49a8-8dbd-14fe5dfead40",
-        "tags": []
-      },
-      "outputs": [
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            "Downloading...\n",
-            "From (original): https://drive.google.com/uc?id=1vWscXs4Vt0a_1IH1-ct2TCgXAZT-N3_Y\n",
-            "From (redirected): https://drive.google.com/uc?id=1vWscXs4Vt0a_1IH1-ct2TCgXAZT-N3_Y&confirm=t&uuid=28aca149-41af-49fd-b783-ca9b4b4cc840\n",
-            "To: c:\\haidun.li\\NSCC\\LipNet\\checkpoints.zip\n",
-            "100%|██████████| 94.5M/94.5M [00:05<00:00, 17.1MB/s]\n"
-          ]
-        },
-        {
-          "data": {
-            "text/plain": [
-              "['models\\\\checkpoint.index',\n",
-              " 'models\\\\__MACOSX/._checkpoint.index',\n",
-              " 'models\\\\checkpoint.data-00000-of-00001',\n",
-              " 'models\\\\__MACOSX/._checkpoint.data-00000-of-00001',\n",
-              " 'models\\\\checkpoint',\n",
-              " 'models\\\\__MACOSX/._checkpoint']"
-            ]
-          },
-          "execution_count": 54,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
+     "data": {
+      "text/html": [
+       "
 Trainable params: 8,471,924 (32.32 MB)\n",
+       "
\n"
       ],
-      "source": [
-        "# url = 'https://drive.google.com/uc?id=1vWscXs4Vt0a_1IH1-ct2TCgXAZT-N3_Y'\n",
-        "# output = 'checkpoints.zip'\n",
-        "# gdown.download(url, output, quiet=False)\n",
-        "# gdown.extractall('checkpoints.zip', 'models')"
+      "text/plain": [
+       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m8,471,924\u001b[0m (32.32 MB)\n"
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-      "cell_type": "code",
-      "execution_count": null,
-      "id": "247f664d-3c87-4e96-946e-930dad0e1c2c",
-      "metadata": {
-        "id": "247f664d-3c87-4e96-946e-930dad0e1c2c",
-        "tags": []
-      },
-      "outputs": [
-        {
-          "ename": "ValueError",
-          "evalue": "File format not supported: filepath=./models/checkpoint. Keras 3 only supports V3 `.keras` and `.weights.h5` files, or legacy V1/V2 `.h5` files.",
-          "output_type": "error",
-          "traceback": [
-            "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-            "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
-            "Cell \u001b[1;32mIn[57], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mload_weights\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m./models/checkpoint\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n",
-            "File \u001b[1;32mc:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\utils\\traceback_utils.py:122\u001b[0m, in \u001b[0;36mfilter_traceback..error_handler\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    119\u001b[0m     filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n\u001b[0;32m    120\u001b[0m     \u001b[38;5;66;03m# To get the full stack trace, call:\u001b[39;00m\n\u001b[0;32m    121\u001b[0m     \u001b[38;5;66;03m# `keras.config.disable_traceback_filtering()`\u001b[39;00m\n\u001b[1;32m--> 122\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m e\u001b[38;5;241m.\u001b[39mwith_traceback(filtered_tb) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m    123\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[0;32m    124\u001b[0m     \u001b[38;5;28;01mdel\u001b[39;00m filtered_tb\n",
-            "File \u001b[1;32mc:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\saving\\saving_api.py:260\u001b[0m, in \u001b[0;36mload_weights\u001b[1;34m(model, filepath, skip_mismatch, **kwargs)\u001b[0m\n\u001b[0;32m    258\u001b[0m             legacy_h5_format\u001b[38;5;241m.\u001b[39mload_weights_from_hdf5_group(f, model)\n\u001b[0;32m    259\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 260\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m    261\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFile format not supported: filepath=\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfilepath\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m. \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    262\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mKeras 3 only supports V3 `.keras` and `.weights.h5` \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    263\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfiles, or legacy V1/V2 `.h5` files.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    264\u001b[0m     )\n",
-            "\u001b[1;31mValueError\u001b[0m: File format not supported: filepath=./models/checkpoint. Keras 3 only supports V3 `.keras` and `.weights.h5` files, or legacy V1/V2 `.h5` files."
-          ]
-        }
+     "data": {
+      "text/html": [
+       "
 Non-trainable params: 0 (0.00 B)\n",
+       "
\n"
       ],
-      "source": [
-        "model.load_weights('./models/checkpoint')"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "id": "7f8d689f-b7bb-443c-9b88-e40c1d800828",
-      "metadata": {
-        "id": "7f8d689f-b7bb-443c-9b88-e40c1d800828",
-        "tags": []
-      },
-      "outputs": [],
-      "source": [
-        "test_data = test.as_numpy_iterator()"
+      "text/plain": [
+       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
       ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "id": "38546dc2-bee9-4837-864b-8a884df40ad7",
-      "metadata": {
-        "id": "38546dc2-bee9-4837-864b-8a884df40ad7",
-        "tags": []
-      },
-      "outputs": [],
-      "source": [
-        "sample = test_data.next()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "id": "a43621f0-229d-4c0d-9554-9c3a3da9c61a",
-      "metadata": {
-        "id": "a43621f0-229d-4c0d-9554-9c3a3da9c61a",
-        "tags": []
-      },
-      "outputs": [],
-      "source": [
-        "yhat = model.predict(sample[0])"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "id": "ea462999-f87e-4a7e-a057-5be7b6d8f7d5",
-      "metadata": {
-        "id": "ea462999-f87e-4a7e-a057-5be7b6d8f7d5",
-        "tags": []
-      },
-      "outputs": [],
-      "source": [
-        "print('~'*100, 'REAL TEXT')\n",
-        "[tf.strings.reduce_join([num_to_char(word) for word in sentence]) for sentence in sample[1]]"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "id": "82bd4c10-dd6e-411e-834b-2a3b43fd12c5",
-      "metadata": {
-        "id": "82bd4c10-dd6e-411e-834b-2a3b43fd12c5",
-        "tags": []
-      },
-      "outputs": [],
-      "source": [
-        "# decoded = tf.keras.backend.ctc_decode(yhat, input_length=[75,75], greedy=True)[0][0].numpy()\n",
-        "decoded = keras.backend.ctc_decode(yhat, input_length=[75,75], greedy=True)[0][0].numpy()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "id": "5d68ac46-c90b-4eab-a709-f19aee569ff5",
-      "metadata": {
-        "id": "5d68ac46-c90b-4eab-a709-f19aee569ff5",
-        "tags": []
-      },
-      "outputs": [],
-      "source": [
-        "print('~'*100, 'PREDICTIONS')\n",
-        "[tf.strings.reduce_join([num_to_char(word) for word in sentence]) for sentence in decoded]"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "64622f98-e99b-4fed-a2cc-f0da82eb5431",
-      "metadata": {
-        "id": "64622f98-e99b-4fed-a2cc-f0da82eb5431"
-      },
-      "source": [
-        "# Test on a Video"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "id": "a8b0c4d0-2031-4331-b91d-d87b1ae6f6e2",
-      "metadata": {
-        "id": "a8b0c4d0-2031-4331-b91d-d87b1ae6f6e2"
-      },
-      "outputs": [],
-      "source": [
-        "sample = load_data(tf.convert_to_tensor('.\\\\data\\\\s1\\\\bras9a.mpg'))"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "id": "0cca60e4-47a9-4683-8a75-48f4684f723d",
-      "metadata": {
-        "id": "0cca60e4-47a9-4683-8a75-48f4684f723d"
-      },
-      "outputs": [],
-      "source": [
-        "print('~'*100, 'REAL TEXT')\n",
-        "[tf.strings.reduce_join([num_to_char(word) for word in sentence]) for sentence in [sample[1]]]"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "id": "8cc5037c-1e32-435c-b0cc-01e1fb3b863c",
-      "metadata": {
-        "id": "8cc5037c-1e32-435c-b0cc-01e1fb3b863c"
-      },
-      "outputs": [],
-      "source": [
-        "yhat = model.predict(tf.expand_dims(sample[0], axis=0))"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "id": "22c4f77d-715d-409f-bc5e-3ebe48704e8f",
-      "metadata": {
-        "id": "22c4f77d-715d-409f-bc5e-3ebe48704e8f"
-      },
-      "outputs": [],
-      "source": [
-        "# decoded = tf.keras.backend.ctc_decode(yhat, input_length=[75], greedy=True)[0][0].numpy()\n",
-        "decoded = keras.backend.ctc_decode(yhat, input_length=[75], greedy=True)[0][0].numpy()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "id": "e4d12ecc-b634-499e-a4bc-db9f010835fb",
-      "metadata": {
-        "id": "e4d12ecc-b634-499e-a4bc-db9f010835fb"
-      },
-      "outputs": [],
-      "source": [
-        "print('~'*100, 'PREDICTIONS')\n",
-        "[tf.strings.reduce_join([num_to_char(word) for word in sentence]) for sentence in decoded]"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "id": "551dfea2-de6b-4400-b71a-a17631529e3f",
-      "metadata": {
-        "id": "551dfea2-de6b-4400-b71a-a17631529e3f"
-      },
-      "outputs": [],
-      "source": []
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "id": "fa95863d-3832-47bf-8a77-ebaa38054ace",
-      "metadata": {
-        "id": "fa95863d-3832-47bf-8a77-ebaa38054ace"
-      },
-      "outputs": [],
-      "source": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
-  ],
-  "metadata": {
-    "accelerator": "GPU",
-    "colab": {
-      "gpuType": "T4",
-      "include_colab_link": true,
-      "provenance": []
-    },
-    "kernelspec": {
-      "display_name": "Python 3",
-      "name": "python3"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.12.3"
+   ],
+   "source": [
+    "model.summary()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 96,
+   "id": "f4b4798c-a65a-4c47-9e2a-3b09dc98d320",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "6375"
+      ]
+     },
+     "execution_count": 96,
+     "metadata": {},
+     "output_type": "execute_result"
     }
+   ],
+   "source": [
+    "5*17*75"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 97,
+   "id": "e5c2eae0-c359-41a4-97a0-75c44dccb7d1",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2s/step\n"
+     ]
+    }
+   ],
+   "source": [
+    "yhat = model.predict(val[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 98,
+   "id": "ffdc7319-0d69-4f7e-a6d4-ce72deb81c0b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       ""
+      ]
+     },
+     "execution_count": 98,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tf.strings.reduce_join([num_to_char(x) for x in tf.argmax(yhat[0],axis=1)])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 99,
+   "id": "6ed47531-8317-4255-9a12-b757642258e6",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       ""
+      ]
+     },
+     "execution_count": 99,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tf.strings.reduce_join([num_to_char(tf.argmax(x)) for x in yhat[0]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 100,
+   "id": "7c37b9b9-5298-4038-9c33-5031d1b457f0",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(None, 75, 46, 140, 1)"
+      ]
+     },
+     "execution_count": 100,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.input_shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 101,
+   "id": "98b316a4-5322-4782-8e36-4b3c1a696d85",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(None, 75, 41)"
+      ]
+     },
+     "execution_count": 101,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.output_shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2ec02176-5c26-46c3-aff7-8352e6563c7d",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "# 4. Setup Training Options and Train"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 102,
+   "id": "ab015fd0-7fb4-4d5d-9fa2-30a05dbd515a",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "def scheduler(epoch, lr):\n",
+    "    if epoch < 30:\n",
+    "        return lr\n",
+    "    else:\n",
+    "        return lr * tf.math.exp(-0.1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 103,
+   "id": "c564d5c9-db54-4e88-b311-9aeab7fb3e69",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "def CTCLoss(y_true, y_pred):\n",
+    "    batch_len = tf.cast(tf.shape(y_true)[0], dtype=\"int64\")\n",
+    "    input_length = tf.cast(tf.shape(y_pred)[1], dtype=\"int64\")\n",
+    "    label_length = tf.cast(tf.shape(y_true)[1], dtype=\"int64\")\n",
+    "\n",
+    "    input_length = input_length * tf.ones(shape=(batch_len, 1), dtype=\"int64\")\n",
+    "    label_length = label_length * tf.ones(shape=(batch_len, 1), dtype=\"int64\")\n",
+    "\n",
+    "    loss = tf.keras.backend.ctc_batch_cost(y_true, y_pred, input_length, label_length)\n",
+    "    return loss"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 104,
+   "id": "a26dc3fc-a19c-4378-bd8c-e2b597a1d15c",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "class ProduceExample(tf.keras.callbacks.Callback):\n",
+    "    def __init__(self, dataset) -> None:\n",
+    "        self.dataset = dataset.as_numpy_iterator()\n",
+    "\n",
+    "    def on_epoch_end(self, epoch, logs=None) -> None:\n",
+    "        data = self.dataset.next()\n",
+    "        yhat = self.model.predict(data[0])\n",
+    "        decoded = tf.keras.backend.ctc_decode(yhat, [75,75], greedy=False)[0][0].numpy()\n",
+    "        for x in range(len(yhat)):\n",
+    "            print('Original:', tf.strings.reduce_join(num_to_char(data[1][x])).numpy().decode('utf-8'))\n",
+    "            print('Prediction:', tf.strings.reduce_join(num_to_char(decoded[x])).numpy().decode('utf-8'))\n",
+    "            print('~'*100)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 105,
+   "id": "04be90d8-2482-46f9-b513-d5f4f8001c7e",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "model.compile(optimizer=Adam(learning_rate=0.0001), loss=CTCLoss)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 109,
+   "id": "eab49367-3f1e-4464-ae76-dbd07549d97e",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "When using `save_weights_only=True` in `ModelCheckpoint`, the filepath provided must end in `.weights.h5` (Keras weights format). Received: filepath=models\\checkpoint",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[109], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m checkpoint_callback \u001b[38;5;241m=\u001b[39m \u001b[43mModelCheckpoint\u001b[49m\u001b[43m(\u001b[49m\u001b[43mos\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpath\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mjoin\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mmodels\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mcheckpoint\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmonitor\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mloss\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msave_weights_only\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32mc:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\callbacks\\model_checkpoint.py:183\u001b[0m, in \u001b[0;36mModelCheckpoint.__init__\u001b[1;34m(self, filepath, monitor, verbose, save_best_only, save_weights_only, mode, save_freq, initial_value_threshold)\u001b[0m\n\u001b[0;32m    181\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m save_weights_only:\n\u001b[0;32m    182\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfilepath\u001b[38;5;241m.\u001b[39mendswith(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m.weights.h5\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[1;32m--> 183\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m    184\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mWhen using `save_weights_only=True` in `ModelCheckpoint`\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    185\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m, the filepath provided must end in `.weights.h5` \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    186\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m(Keras weights format). Received: \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    187\u001b[0m             \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfilepath=\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfilepath\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    188\u001b[0m         )\n\u001b[0;32m    189\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    190\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfilepath\u001b[38;5;241m.\u001b[39mendswith(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m.keras\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n",
+      "\u001b[1;31mValueError\u001b[0m: When using `save_weights_only=True` in `ModelCheckpoint`, the filepath provided must end in `.weights.h5` (Keras weights format). Received: filepath=models\\checkpoint"
+     ]
+    }
+   ],
+   "source": [
+    "checkpoint_callback = ModelCheckpoint(os.path.join('models','checkpoint'), monitor='loss', save_weights_only=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e085a632-d464-46ef-8777-959cad4adb2c",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "schedule_callback = LearningRateScheduler(scheduler)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "48eca991-90ab-4592-8a79-b50e9ca015b6",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "example_callback = ProduceExample(test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8ffba483-aa61-4bbe-a15f-a73e1ddf097c",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/100\n",
+      "  2/450 [..............................] - ETA: 3:03 - loss: 213.9969 "
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "KeyboardInterrupt\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "model.fit(train, validation_data=test, epochs=100, callbacks=[checkpoint_callback, schedule_callback, example_callback])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fa8ee94b-89f7-4733-8a0c-a86f86ff590a",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "# 5. Make a Prediction "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "01fa7204-ce0e-49a8-8dbd-14fe5dfead40",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "url = 'https://drive.google.com/uc?id=1vWscXs4Vt0a_1IH1-ct2TCgXAZT-N3_Y'\n",
+    "output = 'checkpoints.zip'\n",
+    "gdown.download(url, output, quiet=False)\n",
+    "gdown.extractall('checkpoints.zip', 'models')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "247f664d-3c87-4e96-946e-930dad0e1c2c",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       ""
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.load_weights('models/checkpoint')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7f8d689f-b7bb-443c-9b88-e40c1d800828",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "test_data = test.as_numpy_iterator()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "38546dc2-bee9-4837-864b-8a884df40ad7",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "sample = test_data.next()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a43621f0-229d-4c0d-9554-9c3a3da9c61a",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1/1 [==============================] - 1s 973ms/step\n"
+     ]
+    }
+   ],
+   "source": [
+    "yhat = model.predict(sample[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ea462999-f87e-4a7e-a057-5be7b6d8f7d5",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ REAL TEXT\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[,\n",
+       " ]"
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "print('~'*100, 'REAL TEXT')\n",
+    "[tf.strings.reduce_join([num_to_char(word) for word in sentence]) for sentence in sample[1]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "82bd4c10-dd6e-411e-834b-2a3b43fd12c5",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "decoded = tf.keras.backend.ctc_decode(yhat, input_length=[75,75], greedy=True)[0][0].numpy()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5d68ac46-c90b-4eab-a709-f19aee569ff5",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ PREDICTIONS\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[,\n",
+       " ]"
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "print('~'*100, 'PREDICTIONS')\n",
+    "[tf.strings.reduce_join([num_to_char(word) for word in sentence]) for sentence in decoded]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "64622f98-e99b-4fed-a2cc-f0da82eb5431",
+   "metadata": {},
+   "source": [
+    "# Test on a Video"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a8b0c4d0-2031-4331-b91d-d87b1ae6f6e2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sample = load_data(tf.convert_to_tensor('.\\\\data\\\\s1\\\\bras9a.mpg'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0cca60e4-47a9-4683-8a75-48f4684f723d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ REAL TEXT\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[]"
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "print('~'*100, 'REAL TEXT')\n",
+    "[tf.strings.reduce_join([num_to_char(word) for word in sentence]) for sentence in [sample[1]]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8cc5037c-1e32-435c-b0cc-01e1fb3b863c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1/1 [==============================] - 1s 720ms/step\n"
+     ]
+    }
+   ],
+   "source": [
+    "yhat = model.predict(tf.expand_dims(sample[0], axis=0))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "22c4f77d-715d-409f-bc5e-3ebe48704e8f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "decoded = tf.keras.backend.ctc_decode(yhat, input_length=[75], greedy=True)[0][0].numpy()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e4d12ecc-b634-499e-a4bc-db9f010835fb",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ PREDICTIONS\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[]"
+      ]
+     },
+     "execution_count": 65,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "print('~'*100, 'PREDICTIONS')\n",
+    "[tf.strings.reduce_join([num_to_char(word) for word in sentence]) for sentence in decoded]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "551dfea2-de6b-4400-b71a-a17631529e3f",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fa95863d-3832-47bf-8a77-ebaa38054ace",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "lips",
+   "language": "python",
+   "name": "lips"
   },
-  "nbformat": 4,
-  "nbformat_minor": 5
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
 }
diff --git a/LipNet_my.ipynb b/LipNet_my.ipynb
new file mode 100644
index 00000000..0141a560
--- /dev/null
+++ b/LipNet_my.ipynb
@@ -0,0 +1,2590 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "colab_type": "text",
+        "id": "view-in-github"
+      },
+      "source": [
+        "\"Open"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "a3573a47-3689-4668-b62f-5c8451b2b4e9",
+      "metadata": {
+        "id": "a3573a47-3689-4668-b62f-5c8451b2b4e9",
+        "tags": []
+      },
+      "source": [
+        "# 0. Install and Import Dependencies"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "id": "ddfbccbe-41ae-4c23-98b1-a13868e2b499",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "ddfbccbe-41ae-4c23-98b1-a13868e2b499",
+        "outputId": "964dae10-91f7-4769-b15c-4a00ca9db312",
+        "scrolled": true,
+        "tags": []
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Package                   Version\n",
+            "------------------------- --------------\n",
+            "absl-py                   2.1.0\n",
+            "altair                    5.3.0\n",
+            "anyio                     4.3.0\n",
+            "argon2-cffi               23.1.0\n",
+            "argon2-cffi-bindings      21.2.0\n",
+            "arrow                     1.3.0\n",
+            "asttokens                 2.4.1\n",
+            "astunparse                1.6.3\n",
+            "async-lru                 2.0.4\n",
+            "attrs                     23.2.0\n",
+            "av                        12.0.0\n",
+            "Babel                     2.14.0\n",
+            "beautifulsoup4            4.12.3\n",
+            "bleach                    6.1.0\n",
+            "blinker                   1.7.0\n",
+            "cachetools                5.3.3\n",
+            "certifi                   2024.2.2\n",
+            "cffi                      1.16.0\n",
+            "charset-normalizer        3.3.2\n",
+            "click                     8.1.7\n",
+            "colorama                  0.4.6\n",
+            "comm                      0.2.2\n",
+            "contourpy                 1.2.1\n",
+            "cycler                    0.12.1\n",
+            "debugpy                   1.8.1\n",
+            "decorator                 5.1.1\n",
+            "defusedxml                0.7.1\n",
+            "executing                 2.0.1\n",
+            "fastjsonschema            2.19.1\n",
+            "filelock                  3.13.4\n",
+            "flatbuffers               24.3.25\n",
+            "fonttools                 4.51.0\n",
+            "fqdn                      1.5.1\n",
+            "fsspec                    2024.3.1\n",
+            "gast                      0.5.4\n",
+            "gdown                     5.1.0\n",
+            "gitdb                     4.0.11\n",
+            "GitPython                 3.1.43\n",
+            "google-pasta              0.2.0\n",
+            "grpcio                    1.62.1\n",
+            "h11                       0.14.0\n",
+            "h5py                      3.11.0\n",
+            "httpcore                  1.0.5\n",
+            "httpx                     0.27.0\n",
+            "idna                      3.7\n",
+            "imageio                   2.23.0\n",
+            "ipykernel                 6.29.4\n",
+            "ipynb-py-convert          0.4.6\n",
+            "ipython                   8.23.0\n",
+            "ipywidgets                8.1.2\n",
+            "isoduration               20.11.0\n",
+            "jedi                      0.19.1\n",
+            "Jinja2                    3.1.3\n",
+            "json5                     0.9.24\n",
+            "jsonpointer               2.4\n",
+            "jsonschema                4.21.1\n",
+            "jsonschema-specifications 2023.12.1\n",
+            "jupyter                   1.0.0\n",
+            "jupyter_client            8.6.1\n",
+            "jupyter-console           6.6.3\n",
+            "jupyter_core              5.7.2\n",
+            "jupyter-events            0.10.0\n",
+            "jupyter-lsp               2.2.5\n",
+            "jupyter_server            2.13.0\n",
+            "jupyter_server_terminals  0.5.3\n",
+            "jupyterlab                4.1.6\n",
+            "jupyterlab_pygments       0.3.0\n",
+            "jupyterlab_server         2.26.0\n",
+            "jupyterlab_widgets        3.0.10\n",
+            "kiwisolver                1.4.5\n",
+            "libclang                  18.1.1\n",
+            "Markdown                  3.6\n",
+            "markdown-it-py            3.0.0\n",
+            "MarkupSafe                2.1.5\n",
+            "matplotlib                3.8.4\n",
+            "matplotlib-inline         0.1.6\n",
+            "mdurl                     0.1.2\n",
+            "mistune                   3.0.2\n",
+            "ml-dtypes                 0.3.2\n",
+            "mpmath                    1.3.0\n",
+            "namex                     0.0.7\n",
+            "nbclient                  0.10.0\n",
+            "nbconvert                 7.16.3\n",
+            "nbformat                  5.10.4\n",
+            "nest-asyncio              1.6.0\n",
+            "networkx                  3.3\n",
+            "notebook                  7.1.2\n",
+            "notebook_shim             0.2.4\n",
+            "numpy                     1.26.4\n",
+            "opencv-python             4.6.0.66\n",
+            "opt-einsum                3.3.0\n",
+            "optree                    0.11.0\n",
+            "overrides                 7.7.0\n",
+            "packaging                 24.0\n",
+            "pandas                    2.2.1\n",
+            "pandocfilters             1.5.1\n",
+            "parso                     0.8.4\n",
+            "pillow                    10.3.0\n",
+            "pip                       24.0\n",
+            "platformdirs              4.2.0\n",
+            "prometheus_client         0.20.0\n",
+            "prompt-toolkit            3.0.43\n",
+            "protobuf                  4.25.3\n",
+            "psutil                    5.9.8\n",
+            "pure-eval                 0.2.2\n",
+            "pyarrow                   15.0.2\n",
+            "pycparser                 2.22\n",
+            "pydeck                    0.8.1b0\n",
+            "Pygments                  2.17.2\n",
+            "pyparsing                 3.1.2\n",
+            "PySocks                   1.7.1\n",
+            "python-dateutil           2.9.0.post0\n",
+            "python-json-logger        2.0.7\n",
+            "pytz                      2024.1\n",
+            "pywin32                   306\n",
+            "pywinpty                  2.0.13\n",
+            "PyYAML                    6.0.1\n",
+            "pyzmq                     25.1.2\n",
+            "qtconsole                 5.5.1\n",
+            "QtPy                      2.4.1\n",
+            "referencing               0.34.0\n",
+            "requests                  2.31.0\n",
+            "rfc3339-validator         0.1.4\n",
+            "rfc3986-validator         0.1.1\n",
+            "rich                      13.7.1\n",
+            "rpds-py                   0.18.0\n",
+            "Send2Trash                1.8.3\n",
+            "setuptools                69.2.0\n",
+            "six                       1.16.0\n",
+            "smmap                     5.0.1\n",
+            "sniffio                   1.3.1\n",
+            "soupsieve                 2.5\n",
+            "stack-data                0.6.3\n",
+            "streamlit                 1.33.0\n",
+            "sympy                     1.12\n",
+            "tenacity                  8.2.3\n",
+            "tensorboard               2.16.2\n",
+            "tensorboard-data-server   0.7.2\n",
+            "tensorflow                2.16.1\n",
+            "tensorflow-intel          2.16.1\n",
+            "termcolor                 2.4.0\n",
+            "terminado                 0.18.1\n",
+            "tinycss2                  1.2.1\n",
+            "toml                      0.10.2\n",
+            "toolz                     0.12.1\n",
+            "torch                     2.2.2\n",
+            "torchvision               0.17.2\n",
+            "tornado                   6.4\n",
+            "tqdm                      4.66.2\n",
+            "traitlets                 5.14.2\n",
+            "types-python-dateutil     2.9.0.20240316\n",
+            "typing_extensions         4.11.0\n",
+            "tzdata                    2024.1\n",
+            "uri-template              1.3.0\n",
+            "urllib3                   2.2.1\n",
+            "watchdog                  4.0.0\n",
+            "wcwidth                   0.2.13\n",
+            "webcolors                 1.13\n",
+            "webencodings              0.5.1\n",
+            "websocket-client          1.7.0\n",
+            "Werkzeug                  3.0.2\n",
+            "wheel                     0.43.0\n",
+            "widgetsnbextension        4.0.10\n",
+            "wrapt                     1.16.0\n"
+          ]
+        }
+      ],
+      "source": [
+        "!pip list"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "id": "02f907ea-f669-46c7-adcf-7f257e663448",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "02f907ea-f669-46c7-adcf-7f257e663448",
+        "outputId": "e444b2f9-189a-40bd-a2f9-925f13917c35",
+        "tags": []
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Requirement already satisfied: opencv-python in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (4.6.0.66)\n",
+            "Requirement already satisfied: matplotlib in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (3.8.4)\n",
+            "Requirement already satisfied: imageio in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (2.23.0)\n",
+            "Requirement already satisfied: gdown in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (5.1.0)\n",
+            "Requirement already satisfied: tensorflow in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (2.16.1)\n",
+            "Requirement already satisfied: numpy>=1.21.2 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from opencv-python) (1.26.4)\n",
+            "Requirement already satisfied: contourpy>=1.0.1 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from matplotlib) (1.2.1)\n",
+            "Requirement already satisfied: cycler>=0.10 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from matplotlib) (0.12.1)\n",
+            "Requirement already satisfied: fonttools>=4.22.0 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from matplotlib) (4.51.0)\n",
+            "Requirement already satisfied: kiwisolver>=1.3.1 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from matplotlib) (1.4.5)\n",
+            "Requirement already satisfied: packaging>=20.0 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from matplotlib) (24.0)\n",
+            "Requirement already satisfied: pillow>=8 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from matplotlib) (10.3.0)\n",
+            "Requirement already satisfied: pyparsing>=2.3.1 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from matplotlib) (3.1.2)\n",
+            "Requirement already satisfied: python-dateutil>=2.7 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from matplotlib) (2.9.0.post0)\n",
+            "Requirement already satisfied: beautifulsoup4 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from gdown) (4.12.3)\n",
+            "Requirement already satisfied: filelock in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from gdown) (3.13.4)\n",
+            "Requirement already satisfied: requests[socks] in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from gdown) (2.31.0)\n",
+            "Requirement already satisfied: tqdm in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from gdown) (4.66.2)\n",
+            "Requirement already satisfied: tensorflow-intel==2.16.1 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from tensorflow) (2.16.1)\n",
+            "Requirement already satisfied: absl-py>=1.0.0 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from tensorflow-intel==2.16.1->tensorflow) (2.1.0)\n",
+            "Requirement already satisfied: astunparse>=1.6.0 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from tensorflow-intel==2.16.1->tensorflow) (1.6.3)\n",
+            "Requirement already satisfied: flatbuffers>=23.5.26 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from tensorflow-intel==2.16.1->tensorflow) (24.3.25)\n",
+            "Requirement already satisfied: gast!=0.5.0,!=0.5.1,!=0.5.2,>=0.2.1 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from tensorflow-intel==2.16.1->tensorflow) (0.5.4)\n",
+            "Requirement already satisfied: google-pasta>=0.1.1 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from tensorflow-intel==2.16.1->tensorflow) (0.2.0)\n",
+            "Requirement already satisfied: h5py>=3.10.0 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from tensorflow-intel==2.16.1->tensorflow) (3.11.0)\n",
+            "Requirement already satisfied: libclang>=13.0.0 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from tensorflow-intel==2.16.1->tensorflow) (18.1.1)\n",
+            "Requirement already satisfied: ml-dtypes~=0.3.1 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from tensorflow-intel==2.16.1->tensorflow) (0.3.2)\n",
+            "Requirement already satisfied: opt-einsum>=2.3.2 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from tensorflow-intel==2.16.1->tensorflow) (3.3.0)\n",
+            "Requirement already satisfied: protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.20.3 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from tensorflow-intel==2.16.1->tensorflow) (4.25.3)\n",
+            "Requirement already satisfied: setuptools in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from tensorflow-intel==2.16.1->tensorflow) (69.2.0)\n",
+            "Requirement already satisfied: six>=1.12.0 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from tensorflow-intel==2.16.1->tensorflow) (1.16.0)\n",
+            "Requirement already satisfied: termcolor>=1.1.0 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from tensorflow-intel==2.16.1->tensorflow) (2.4.0)\n",
+            "Requirement already satisfied: typing-extensions>=3.6.6 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from tensorflow-intel==2.16.1->tensorflow) (4.11.0)\n",
+            "Requirement already satisfied: wrapt>=1.11.0 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from tensorflow-intel==2.16.1->tensorflow) (1.16.0)\n",
+            "Requirement already satisfied: grpcio<2.0,>=1.24.3 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from tensorflow-intel==2.16.1->tensorflow) (1.62.1)\n",
+            "Requirement already satisfied: tensorboard<2.17,>=2.16 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from tensorflow-intel==2.16.1->tensorflow) (2.16.2)\n",
+            "Collecting keras>=3.0.0 (from tensorflow-intel==2.16.1->tensorflow)\n",
+            "  Using cached keras-3.2.1-py3-none-any.whl.metadata (5.6 kB)\n",
+            "Requirement already satisfied: soupsieve>1.2 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from beautifulsoup4->gdown) (2.5)\n",
+            "Requirement already satisfied: charset-normalizer<4,>=2 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from requests[socks]->gdown) (3.3.2)\n",
+            "Requirement already satisfied: idna<4,>=2.5 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from requests[socks]->gdown) (3.7)\n",
+            "Requirement already satisfied: urllib3<3,>=1.21.1 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from requests[socks]->gdown) (2.2.1)\n",
+            "Requirement already satisfied: certifi>=2017.4.17 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from requests[socks]->gdown) (2024.2.2)\n",
+            "Requirement already satisfied: PySocks!=1.5.7,>=1.5.6 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from requests[socks]->gdown) (1.7.1)\n",
+            "Requirement already satisfied: colorama in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from tqdm->gdown) (0.4.6)\n",
+            "Requirement already satisfied: wheel<1.0,>=0.23.0 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from astunparse>=1.6.0->tensorflow-intel==2.16.1->tensorflow) (0.43.0)\n",
+            "Requirement already satisfied: rich in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from keras>=3.0.0->tensorflow-intel==2.16.1->tensorflow) (13.7.1)\n",
+            "Requirement already satisfied: namex in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from keras>=3.0.0->tensorflow-intel==2.16.1->tensorflow) (0.0.7)\n",
+            "Requirement already satisfied: optree in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from keras>=3.0.0->tensorflow-intel==2.16.1->tensorflow) (0.11.0)\n",
+            "Requirement already satisfied: markdown>=2.6.8 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from tensorboard<2.17,>=2.16->tensorflow-intel==2.16.1->tensorflow) (3.6)\n",
+            "Requirement already satisfied: tensorboard-data-server<0.8.0,>=0.7.0 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from tensorboard<2.17,>=2.16->tensorflow-intel==2.16.1->tensorflow) (0.7.2)\n",
+            "Requirement already satisfied: werkzeug>=1.0.1 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from tensorboard<2.17,>=2.16->tensorflow-intel==2.16.1->tensorflow) (3.0.2)\n",
+            "Requirement already satisfied: MarkupSafe>=2.1.1 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from werkzeug>=1.0.1->tensorboard<2.17,>=2.16->tensorflow-intel==2.16.1->tensorflow) (2.1.5)\n",
+            "Requirement already satisfied: markdown-it-py>=2.2.0 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from rich->keras>=3.0.0->tensorflow-intel==2.16.1->tensorflow) (3.0.0)\n",
+            "Requirement already satisfied: pygments<3.0.0,>=2.13.0 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from rich->keras>=3.0.0->tensorflow-intel==2.16.1->tensorflow) (2.17.2)\n",
+            "Requirement already satisfied: mdurl~=0.1 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from markdown-it-py>=2.2.0->rich->keras>=3.0.0->tensorflow-intel==2.16.1->tensorflow) (0.1.2)\n",
+            "Using cached keras-3.2.1-py3-none-any.whl (1.1 MB)\n",
+            "Installing collected packages: keras\n",
+            "Successfully installed keras-3.2.1\n"
+          ]
+        }
+      ],
+      "source": [
+        "!pip install opencv-python matplotlib imageio gdown tensorflow"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "id": "b24af50c-20b8-409d-ad78-30a933fdd669",
+      "metadata": {
+        "id": "b24af50c-20b8-409d-ad78-30a933fdd669",
+        "tags": []
+      },
+      "outputs": [],
+      "source": [
+        "import os\n",
+        "import cv2\n",
+        "import tensorflow as tf\n",
+        "import numpy as np\n",
+        "from typing import List\n",
+        "from matplotlib import pyplot as plt\n",
+        "import imageio"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 52,
+      "id": "1e3db0b0-e559-4ad6-91fd-e7414b7d75e6",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "1e3db0b0-e559-4ad6-91fd-e7414b7d75e6",
+        "outputId": "c1e41844-c8fd-4efc-eb22-11f3817b0c9f"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "[]"
+            ]
+          },
+          "execution_count": 52,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "tf.config.list_physical_devices('GPU')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "id": "378d045a-3003-4f93-b7d2-a25a97774a68",
+      "metadata": {
+        "id": "378d045a-3003-4f93-b7d2-a25a97774a68",
+        "tags": []
+      },
+      "outputs": [],
+      "source": [
+        "physical_devices = tf.config.list_physical_devices('GPU')\n",
+        "try:\n",
+        "    tf.config.experimental.set_memory_growth(physical_devices[0], True)\n",
+        "except:\n",
+        "    pass"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "7a19e88e-c7b9-45c1-ae1e-f2109329c71b",
+      "metadata": {
+        "id": "7a19e88e-c7b9-45c1-ae1e-f2109329c71b",
+        "tags": []
+      },
+      "source": [
+        "# 1. Build Data Loading Functions"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "id": "8fb99c90-e05a-437f-839d-6e772f8c1dd5",
+      "metadata": {
+        "id": "8fb99c90-e05a-437f-839d-6e772f8c1dd5",
+        "tags": []
+      },
+      "outputs": [],
+      "source": [
+        "import gdown"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "id": "c019e4c6-2af3-4160-99ea-5c8cb009f1a7",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "c019e4c6-2af3-4160-99ea-5c8cb009f1a7",
+        "outputId": "49a7c7ff-ab7d-480e-bcfd-ee652ad89af1",
+        "tags": []
+      },
+      "outputs": [],
+      "source": [
+        "# url = 'https://drive.google.com/uc?id=1YlvpDLix3S-U8fd-gqRwPcWXAXm8JwjL'\n",
+        "# output = 'data.zip'\n",
+        "# gdown.download(url, output, quiet=False)\n",
+        "# gdown.extractall('data.zip')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "id": "8548cc59-6dfc-4acc-abc3-3e65212db02e",
+      "metadata": {
+        "id": "8548cc59-6dfc-4acc-abc3-3e65212db02e",
+        "tags": []
+      },
+      "outputs": [],
+      "source": [
+        "def load_video(path:str) -> List[float]:\n",
+        "\n",
+        "    cap = cv2.VideoCapture(path)\n",
+        "    frames = []\n",
+        "    for _ in range(int(cap.get(cv2.CAP_PROP_FRAME_COUNT))):\n",
+        "        ret, frame = cap.read()\n",
+        "        frame = tf.image.rgb_to_grayscale(frame)\n",
+        "        frames.append(frame[190:236,80:220,:])\n",
+        "    cap.release()\n",
+        "\n",
+        "    mean = tf.math.reduce_mean(frames)\n",
+        "    std = tf.math.reduce_std(tf.cast(frames, tf.float32))\n",
+        "    return tf.cast((frames - mean), tf.float32) / std"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 9,
+      "id": "ec735e0b-ec98-4eb0-8f49-c35527d6670a",
+      "metadata": {
+        "id": "ec735e0b-ec98-4eb0-8f49-c35527d6670a",
+        "tags": []
+      },
+      "outputs": [],
+      "source": [
+        "vocab = [x for x in \"abcdefghijklmnopqrstuvwxyz'?!123456789 \"]"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 10,
+      "id": "be04e972-d7a5-4a72-82d8-a6bdde1f3ce6",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "be04e972-d7a5-4a72-82d8-a6bdde1f3ce6",
+        "outputId": "01f490ee-dabf-4e94-c6ed-181e8c1ae2c4",
+        "tags": []
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "The vocabulary is: ['', 'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z', \"'\", '?', '!', '1', '2', '3', '4', '5', '6', '7', '8', '9', ' '] (size =40)\n"
+          ]
+        }
+      ],
+      "source": [
+        "char_to_num = tf.keras.layers.StringLookup(vocabulary=vocab, oov_token=\"\")\n",
+        "num_to_char = tf.keras.layers.StringLookup(\n",
+        "    vocabulary=char_to_num.get_vocabulary(), oov_token=\"\", invert=True\n",
+        ")\n",
+        "\n",
+        "print(\n",
+        "    f\"The vocabulary is: {char_to_num.get_vocabulary()} \"\n",
+        "    f\"(size ={char_to_num.vocabulary_size()})\"\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 11,
+      "id": "559f7420-6802-45fa-9ca0-b1ff209b461c",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "559f7420-6802-45fa-9ca0-b1ff209b461c",
+        "outputId": "90f0547b-3308-478e-be4a-c89cef1d58e6",
+        "tags": []
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "['',\n",
+              " 'a',\n",
+              " 'b',\n",
+              " 'c',\n",
+              " 'd',\n",
+              " 'e',\n",
+              " 'f',\n",
+              " 'g',\n",
+              " 'h',\n",
+              " 'i',\n",
+              " 'j',\n",
+              " 'k',\n",
+              " 'l',\n",
+              " 'm',\n",
+              " 'n',\n",
+              " 'o',\n",
+              " 'p',\n",
+              " 'q',\n",
+              " 'r',\n",
+              " 's',\n",
+              " 't',\n",
+              " 'u',\n",
+              " 'v',\n",
+              " 'w',\n",
+              " 'x',\n",
+              " 'y',\n",
+              " 'z',\n",
+              " \"'\",\n",
+              " '?',\n",
+              " '!',\n",
+              " '1',\n",
+              " '2',\n",
+              " '3',\n",
+              " '4',\n",
+              " '5',\n",
+              " '6',\n",
+              " '7',\n",
+              " '8',\n",
+              " '9',\n",
+              " ' ']"
+            ]
+          },
+          "execution_count": 11,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "char_to_num.get_vocabulary()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 12,
+      "id": "797ff78b-b48f-4e14-bb62-8cd0ebf9501a",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "797ff78b-b48f-4e14-bb62-8cd0ebf9501a",
+        "outputId": "cedf213c-95e4-439a-a871-641aec307517",
+        "tags": []
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              ""
+            ]
+          },
+          "execution_count": 12,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "char_to_num(['n','i','c','k'])"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 13,
+      "id": "8cd7f4f4-ae77-4509-a4f4-c723787ebad1",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "8cd7f4f4-ae77-4509-a4f4-c723787ebad1",
+        "outputId": "cb5428be-73ea-47b2-ea8e-4d2c4e3dcb00"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              ""
+            ]
+          },
+          "execution_count": 13,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "num_to_char([14,  9,  3, 11])"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 14,
+      "id": "9491bab5-6a3c-4f79-879a-8f9fbe73ae2e",
+      "metadata": {
+        "id": "9491bab5-6a3c-4f79-879a-8f9fbe73ae2e",
+        "tags": []
+      },
+      "outputs": [],
+      "source": [
+        "def load_alignments(path:str) -> List[str]:\n",
+        "    with open(path, 'r') as f:\n",
+        "        lines = f.readlines()\n",
+        "    tokens = []\n",
+        "    for line in lines:\n",
+        "        line = line.split()\n",
+        "        if line[2] != 'sil':\n",
+        "            tokens = [*tokens,' ',line[2]]\n",
+        "    return char_to_num(tf.reshape(tf.strings.unicode_split(tokens, input_encoding='UTF-8'), (-1)))[1:]"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 15,
+      "id": "dd01ca9f-77fb-4643-a2aa-47dd82c5d66b",
+      "metadata": {
+        "id": "dd01ca9f-77fb-4643-a2aa-47dd82c5d66b",
+        "tags": []
+      },
+      "outputs": [],
+      "source": [
+        "def load_data(path: str):\n",
+        "    path = bytes.decode(path.numpy())\n",
+        "    # file_name = path.split('/')[-1].split('.')[0]\n",
+        "    # File name splitting for windows\n",
+        "    file_name = path.split('\\\\')[-1].split('.')[0]\n",
+        "    video_path = os.path.join('data','s1',f'{file_name}.mpg')\n",
+        "    alignment_path = os.path.join('data','alignments','s1',f'{file_name}.align')\n",
+        "    frames = load_video(video_path)\n",
+        "    alignments = load_alignments(alignment_path)\n",
+        "\n",
+        "    return frames, alignments"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 16,
+      "id": "8cb7cc58-31ae-4904-a805-1177a82717d2",
+      "metadata": {
+        "id": "8cb7cc58-31ae-4904-a805-1177a82717d2",
+        "tags": []
+      },
+      "outputs": [],
+      "source": [
+        "test_path = '.\\\\data\\\\s1\\\\bbal6n.mpg'\n",
+        "# test_path = './data/s1/bbal6n.mpg'"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 17,
+      "id": "76aa964f-0c84-490d-897a-d00e3966e2c9",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 35
+        },
+        "id": "76aa964f-0c84-490d-897a-d00e3966e2c9",
+        "outputId": "d04d49ad-4eeb-4be1-ffa7-f2655aa86a7a"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "'bbal6n'"
+            ]
+          },
+          "execution_count": 17,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "tf.convert_to_tensor(test_path).numpy().decode('utf-8').split('\\\\')[-1].split('.')[0]\n",
+        "# tf.convert_to_tensor(test_path).numpy().decode('utf-8').split('/')[-1].split('.')[0]"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 18,
+      "id": "eb602c71-8560-4f9e-b26b-08202febb937",
+      "metadata": {
+        "id": "eb602c71-8560-4f9e-b26b-08202febb937",
+        "scrolled": true,
+        "tags": []
+      },
+      "outputs": [],
+      "source": [
+        "frames, alignments = load_data(tf.convert_to_tensor(test_path))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 19,
+      "id": "0e3184a1-6b02-4b4f-84a8-a0a65f951ea2",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 244
+        },
+        "id": "0e3184a1-6b02-4b4f-84a8-a0a65f951ea2",
+        "outputId": "ca65c601-b300-4a41-d8fe-7915a8e5a50b"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              ""
+            ]
+          },
+          "execution_count": 19,
+          "metadata": {},
+          "output_type": "execute_result"
+        },
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "
"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "plt.imshow(frames[40])"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 20,
+      "id": "d7ec0833-d54b-4073-84cf-92d011c60ec1",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "d7ec0833-d54b-4073-84cf-92d011c60ec1",
+        "outputId": "02abd7a1-113b-42b1-a6ba-9a458373b112"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              ""
+            ]
+          },
+          "execution_count": 20,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "alignments"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 21,
+      "id": "fe1ad370-b287-4b46-85a2-7c45b0bd9b10",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "fe1ad370-b287-4b46-85a2-7c45b0bd9b10",
+        "outputId": "8910d788-545b-4e76-b154-4a90d8e63927"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              ""
+            ]
+          },
+          "execution_count": 21,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "tf.strings.reduce_join([bytes.decode(x) for x in num_to_char(alignments.numpy()).numpy()])"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 22,
+      "id": "6871031a-b0ba-4c76-a852-f6329b0f2606",
+      "metadata": {
+        "id": "6871031a-b0ba-4c76-a852-f6329b0f2606",
+        "tags": []
+      },
+      "outputs": [],
+      "source": [
+        "def mappable_function(path:str) ->List[str]:\n",
+        "    result = tf.py_function(load_data, [path], (tf.float32, tf.int64))\n",
+        "    return result"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "c40a7eb4-0c3e-4eab-9291-5611cb68ce08",
+      "metadata": {
+        "id": "c40a7eb4-0c3e-4eab-9291-5611cb68ce08",
+        "tags": []
+      },
+      "source": [
+        "# 2. Create Data Pipeline"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 23,
+      "id": "7686355d-45aa-4c85-ad9c-053e6a9b4d81",
+      "metadata": {
+        "id": "7686355d-45aa-4c85-ad9c-053e6a9b4d81",
+        "tags": []
+      },
+      "outputs": [],
+      "source": [
+        "from matplotlib import pyplot as plt"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 24,
+      "id": "f066fea2-91b1-42ed-a67d-00566a1a53ff",
+      "metadata": {
+        "id": "f066fea2-91b1-42ed-a67d-00566a1a53ff",
+        "tags": []
+      },
+      "outputs": [],
+      "source": [
+        "data = tf.data.Dataset.list_files('.\\\\data\\\\s1\\\\*.mpg')\n",
+        "data = data.shuffle(500, reshuffle_each_iteration=False)\n",
+        "data = data.map(mappable_function)\n",
+        "data = data.padded_batch(2, padded_shapes=([75,None,None,None],[40]))\n",
+        "data = data.prefetch(tf.data.AUTOTUNE)\n",
+        "# Added for split\n",
+        "train = data.take(450)\n",
+        "test = data.skip(450)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 25,
+      "id": "4de3b276",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "(450, 50)"
+            ]
+          },
+          "execution_count": 25,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "len(train), len(test)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 26,
+      "id": "5281bde8-fdc8-4da1-bd55-5a7929a9e80c",
+      "metadata": {
+        "id": "5281bde8-fdc8-4da1-bd55-5a7929a9e80c"
+      },
+      "outputs": [],
+      "source": [
+        "frames, alignments = data.as_numpy_iterator().next()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 27,
+      "id": "cbebe683-6afd-47fd-bba4-c83b4b13bb32",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "cbebe683-6afd-47fd-bba4-c83b4b13bb32",
+        "outputId": "8924019e-28cd-4bfa-9e1e-bb836d71e6f8"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "2"
+            ]
+          },
+          "execution_count": 27,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "len(frames)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 28,
+      "id": "5cf2d676-93a9-434c-b3c7-bdcc2577b2e7",
+      "metadata": {
+        "id": "5cf2d676-93a9-434c-b3c7-bdcc2577b2e7",
+        "tags": []
+      },
+      "outputs": [],
+      "source": [
+        "sample = data.as_numpy_iterator()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 29,
+      "id": "efa6cd46-7079-46c0-b45b-832f339f6cb0",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "efa6cd46-7079-46c0-b45b-832f339f6cb0",
+        "outputId": "c24ecd06-8d12-4531-df65-27a9f77acada",
+        "scrolled": true,
+        "tags": []
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "array([[[[[ 1.2631532 ],\n",
+              "          [ 1.3815738 ],\n",
+              "          [ 1.1842061 ],\n",
+              "          ...,\n",
+              "          [ 0.3157883 ],\n",
+              "          [ 0.27631477],\n",
+              "          [ 0.27631477]],\n",
+              "\n",
+              "         [[ 1.3421003 ],\n",
+              "          [ 1.4210473 ],\n",
+              "          [ 1.1842061 ],\n",
+              "          ...,\n",
+              "          [ 0.3157883 ],\n",
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+            ]
+          },
+          "execution_count": 29,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "val = sample.next(); val[0]"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 30,
+      "id": "acf5eb4f-a0da-4a9a-bf24-af13e9cc2fbe",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "acf5eb4f-a0da-4a9a-bf24-af13e9cc2fbe",
+        "outputId": "2b7ce709-6400-402c-ab9d-0e4c5739cafc",
+        "tags": []
+      },
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n"
+          ]
+        }
+      ],
+      "source": [
+        "imageio.mimsave('.\\\\animation.gif', val[0][0], fps=10)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 31,
+      "id": "c33a87a2-d5e0-4ec9-b174-73ebf41bf03a",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 244
+        },
+        "id": "c33a87a2-d5e0-4ec9-b174-73ebf41bf03a",
+        "outputId": "1b7cc028-1990-4714-8d7e-fda85218c0d5",
+        "tags": []
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              ""
+            ]
+          },
+          "execution_count": 31,
+          "metadata": {},
+          "output_type": "execute_result"
+        },
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "
"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# 0:videos, 0: 1st video out of the batch,  0: return the first frame in the video\n",
+        "plt.imshow(val[0][0][35])"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 32,
+      "id": "84593332-133c-4205-b7a6-8e235d5e2b3b",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "84593332-133c-4205-b7a6-8e235d5e2b3b",
+        "outputId": "69f24afc-2cb9-4d07-9d93-3b4e1fde5046",
+        "tags": []
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              ""
+            ]
+          },
+          "execution_count": 32,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "tf.strings.reduce_join([num_to_char(word) for word in val[1][0]])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "0f47733c-83bc-465c-b118-b198b492ad37",
+      "metadata": {
+        "id": "0f47733c-83bc-465c-b118-b198b492ad37",
+        "tags": []
+      },
+      "source": [
+        "# 3. Design the Deep Neural Network"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 33,
+      "id": "d8e9a497-191b-4842-afbd-26f5e13c43ba",
+      "metadata": {
+        "id": "d8e9a497-191b-4842-afbd-26f5e13c43ba",
+        "tags": []
+      },
+      "outputs": [],
+      "source": [
+        "import tensorflow\n",
+        "from tensorflow import keras\n",
+        "\n",
+        "from keras.models import Sequential\n",
+        "from keras.layers import Conv3D, LSTM, Dense, Dropout, Bidirectional, MaxPool3D, Activation, Reshape, SpatialDropout3D, BatchNormalization, TimeDistributed, Flatten\n",
+        "from keras.optimizers import Adam\n",
+        "from keras.callbacks import ModelCheckpoint, LearningRateScheduler\n",
+        "from keras.layers import LSTM, Bidirectional\n",
+        "from keras.initializers import Orthogonal\n",
+        "from keras.layers import Lambda\n",
+        "from keras.layers import Reshape\n",
+        "import tensorflow as tf"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 34,
+      "id": "3f753ed2-70b9-4236-8c1c-08ca065dc8bf",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "3f753ed2-70b9-4236-8c1c-08ca065dc8bf",
+        "outputId": "b06442c3-cc68-4c88-d5e8-c9a915fc8b96"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "(75, 46, 140, 1)"
+            ]
+          },
+          "execution_count": 34,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "data.as_numpy_iterator().next()[0][0].shape"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 35,
+      "id": "f9171056-a352-491a-9ed9-92b28ced268e",
+      "metadata": {
+        "id": "f9171056-a352-491a-9ed9-92b28ced268e",
+        "tags": []
+      },
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\layers\\convolutional\\base_conv.py:99: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
+            "  super().__init__(\n"
+          ]
+        }
+      ],
+      "source": [
+        "model = Sequential()\n",
+        "model.add(Conv3D(128, 3, input_shape=(75,46,140,1), padding='same'))\n",
+        "model.add(Activation('relu'))\n",
+        "model.add(MaxPool3D((1,2,2)))\n",
+        "\n",
+        "model.add(Conv3D(256, 3, padding='same'))\n",
+        "model.add(Activation('relu'))\n",
+        "model.add(MaxPool3D((1,2,2)))\n",
+        "\n",
+        "model.add(Conv3D(75, 3, padding='same'))\n",
+        "model.add(Activation('relu'))\n",
+        "model.add(MaxPool3D((1,2,2)))\n",
+        "\n",
+        "# model.add(Lambda(lambda x: tf.print(\"Shape before TimeDistributed:\", tf.shape(x))))\n",
+        "\n",
+        "model.add(TimeDistributed(Flatten()))\n",
+        "\n",
+        "# model.add(Reshape((-1, 75*5*17)))\n",
+        "\n",
+        "# model.add(Bidirectional(LSTM(128, return_sequences=True)))\n",
+        "model.add(Bidirectional(LSTM(128, return_sequences=True)))\n",
+        "\n",
+        "# # model.add(Bidirectional(LSTM(128, kernel_initializer='Orthogonal', return_sequences=True)))\n",
+        "model.add(Bidirectional(LSTM(128, kernel_initializer=Orthogonal(), return_sequences=True)))\n",
+        "model.add(Dropout(.5))\n",
+        "\n",
+        "# # model.add(Bidirectional(LSTM(128, kernel_initializer='Orthogonal', return_sequences=True)))\n",
+        "model.add(Bidirectional(LSTM(128, kernel_initializer=Orthogonal(), return_sequences=True)))\n",
+        "model.add(Dropout(.5))\n",
+        "\n",
+        "model.add(Dense(char_to_num.vocabulary_size()+1, kernel_initializer='he_normal', activation='softmax'))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 36,
+      "id": "ab905c18",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "
Model: \"sequential\"\n",
+              "
\n"
+            ],
+            "text/plain": [
+              "\u001b[1mModel: \"sequential\"\u001b[0m\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+              "┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃\n",
+              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+              "│ conv3d (Conv3D)                 │ (None, 75, 46, 140,    │         3,584 │\n",
+              "│                                 │ 128)                   │               │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ activation (Activation)         │ (None, 75, 46, 140,    │             0 │\n",
+              "│                                 │ 128)                   │               │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ max_pooling3d (MaxPooling3D)    │ (None, 75, 23, 70,     │             0 │\n",
+              "│                                 │ 128)                   │               │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ conv3d_1 (Conv3D)               │ (None, 75, 23, 70,     │       884,992 │\n",
+              "│                                 │ 256)                   │               │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ activation_1 (Activation)       │ (None, 75, 23, 70,     │             0 │\n",
+              "│                                 │ 256)                   │               │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ max_pooling3d_1 (MaxPooling3D)  │ (None, 75, 11, 35,     │             0 │\n",
+              "│                                 │ 256)                   │               │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ conv3d_2 (Conv3D)               │ (None, 75, 11, 35, 75) │       518,475 │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ activation_2 (Activation)       │ (None, 75, 11, 35, 75) │             0 │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ max_pooling3d_2 (MaxPooling3D)  │ (None, 75, 5, 17, 75)  │             0 │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ time_distributed                │ (None, 75, 6375)       │             0 │\n",
+              "│ (TimeDistributed)               │                        │               │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ bidirectional (Bidirectional)   │ (None, 75, 256)        │     6,660,096 │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ bidirectional_1 (Bidirectional) │ (None, 75, 256)        │       394,240 │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ dropout (Dropout)               │ (None, 75, 256)        │             0 │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ bidirectional_2 (Bidirectional) │ (None, 75, 256)        │       394,240 │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ dropout_1 (Dropout)             │ (None, 75, 256)        │             0 │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ dense (Dense)                   │ (None, 75, 41)         │        10,537 │\n",
+              "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
+              "
\n"
+            ],
+            "text/plain": [
+              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
+              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+              "│ conv3d (\u001b[38;5;33mConv3D\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m46\u001b[0m, \u001b[38;5;34m140\u001b[0m,    │         \u001b[38;5;34m3,584\u001b[0m │\n",
+              "│                                 │ \u001b[38;5;34m128\u001b[0m)                   │               │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ activation (\u001b[38;5;33mActivation\u001b[0m)         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m46\u001b[0m, \u001b[38;5;34m140\u001b[0m,    │             \u001b[38;5;34m0\u001b[0m │\n",
+              "│                                 │ \u001b[38;5;34m128\u001b[0m)                   │               │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ max_pooling3d (\u001b[38;5;33mMaxPooling3D\u001b[0m)    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m23\u001b[0m, \u001b[38;5;34m70\u001b[0m,     │             \u001b[38;5;34m0\u001b[0m │\n",
+              "│                                 │ \u001b[38;5;34m128\u001b[0m)                   │               │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ conv3d_1 (\u001b[38;5;33mConv3D\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m23\u001b[0m, \u001b[38;5;34m70\u001b[0m,     │       \u001b[38;5;34m884,992\u001b[0m │\n",
+              "│                                 │ \u001b[38;5;34m256\u001b[0m)                   │               │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ activation_1 (\u001b[38;5;33mActivation\u001b[0m)       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m23\u001b[0m, \u001b[38;5;34m70\u001b[0m,     │             \u001b[38;5;34m0\u001b[0m │\n",
+              "│                                 │ \u001b[38;5;34m256\u001b[0m)                   │               │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ max_pooling3d_1 (\u001b[38;5;33mMaxPooling3D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m11\u001b[0m, \u001b[38;5;34m35\u001b[0m,     │             \u001b[38;5;34m0\u001b[0m │\n",
+              "│                                 │ \u001b[38;5;34m256\u001b[0m)                   │               │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ conv3d_2 (\u001b[38;5;33mConv3D\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m11\u001b[0m, \u001b[38;5;34m35\u001b[0m, \u001b[38;5;34m75\u001b[0m) │       \u001b[38;5;34m518,475\u001b[0m │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ activation_2 (\u001b[38;5;33mActivation\u001b[0m)       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m11\u001b[0m, \u001b[38;5;34m35\u001b[0m, \u001b[38;5;34m75\u001b[0m) │             \u001b[38;5;34m0\u001b[0m │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ max_pooling3d_2 (\u001b[38;5;33mMaxPooling3D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m17\u001b[0m, \u001b[38;5;34m75\u001b[0m)  │             \u001b[38;5;34m0\u001b[0m │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ time_distributed                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m6375\u001b[0m)       │             \u001b[38;5;34m0\u001b[0m │\n",
+              "│ (\u001b[38;5;33mTimeDistributed\u001b[0m)               │                        │               │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ bidirectional (\u001b[38;5;33mBidirectional\u001b[0m)   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m256\u001b[0m)        │     \u001b[38;5;34m6,660,096\u001b[0m │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ bidirectional_1 (\u001b[38;5;33mBidirectional\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m256\u001b[0m)        │       \u001b[38;5;34m394,240\u001b[0m │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ dropout (\u001b[38;5;33mDropout\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m256\u001b[0m)        │             \u001b[38;5;34m0\u001b[0m │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ bidirectional_2 (\u001b[38;5;33mBidirectional\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m256\u001b[0m)        │       \u001b[38;5;34m394,240\u001b[0m │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ dropout_1 (\u001b[38;5;33mDropout\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m256\u001b[0m)        │             \u001b[38;5;34m0\u001b[0m │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ dense (\u001b[38;5;33mDense\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m41\u001b[0m)         │        \u001b[38;5;34m10,537\u001b[0m │\n",
+              "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
 Total params: 8,866,164 (33.82 MB)\n",
+              "
\n"
+            ],
+            "text/plain": [
+              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m8,866,164\u001b[0m (33.82 MB)\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
 Trainable params: 8,866,164 (33.82 MB)\n",
+              "
\n"
+            ],
+            "text/plain": [
+              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m8,866,164\u001b[0m (33.82 MB)\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
 Non-trainable params: 0 (0.00 B)\n",
+              "
\n"
+            ],
+            "text/plain": [
+              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "model.summary()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 37,
+      "id": "fae71bed",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "6375"
+            ]
+          },
+          "execution_count": 37,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "5*17*75"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 38,
+      "id": "7a5815ab",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 3s/step\n"
+          ]
+        }
+      ],
+      "source": [
+        "yhat = model.predict(val[0])"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 39,
+      "id": "65eccd93",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              ""
+            ]
+          },
+          "execution_count": 39,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "tf.strings.reduce_join([num_to_char(x) for x in tf.argmax(yhat[0],axis=1)])"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 40,
+      "id": "6ed47531-8317-4255-9a12-b757642258e6",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "6ed47531-8317-4255-9a12-b757642258e6",
+        "outputId": "5b755432-4136-4834-fd69-78aace01b57b",
+        "tags": []
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              ""
+            ]
+          },
+          "execution_count": 40,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "tf.strings.reduce_join([num_to_char(tf.argmax(x)) for x in yhat[0]])"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 41,
+      "id": "7c37b9b9-5298-4038-9c33-5031d1b457f0",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "7c37b9b9-5298-4038-9c33-5031d1b457f0",
+        "outputId": "fcdf9c94-35db-4aa0-fcf7-3029f32eac97",
+        "tags": []
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "(None, 75, 46, 140, 1)"
+            ]
+          },
+          "execution_count": 41,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "model.input_shape"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 42,
+      "id": "98b316a4-5322-4782-8e36-4b3c1a696d85",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "98b316a4-5322-4782-8e36-4b3c1a696d85",
+        "outputId": "5f9ebad7-b4cc-4ac8-c5e6-f5471120f50f",
+        "tags": []
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "(None, 75, 41)"
+            ]
+          },
+          "execution_count": 42,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "model.output_shape"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "2ec02176-5c26-46c3-aff7-8352e6563c7d",
+      "metadata": {
+        "id": "2ec02176-5c26-46c3-aff7-8352e6563c7d",
+        "tags": []
+      },
+      "source": [
+        "# 4. Setup Training Options and Train"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 43,
+      "id": "ab015fd0-7fb4-4d5d-9fa2-30a05dbd515a",
+      "metadata": {
+        "id": "ab015fd0-7fb4-4d5d-9fa2-30a05dbd515a",
+        "tags": []
+      },
+      "outputs": [],
+      "source": [
+        "def scheduler(epoch, lr):\n",
+        "    if epoch < 30:\n",
+        "        return lr\n",
+        "    else:\n",
+        "        return lr * tf.math.exp(-0.1)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 44,
+      "id": "c564d5c9-db54-4e88-b311-9aeab7fb3e69",
+      "metadata": {
+        "id": "c564d5c9-db54-4e88-b311-9aeab7fb3e69",
+        "tags": []
+      },
+      "outputs": [],
+      "source": [
+        "def CTCLoss(y_true, y_pred):\n",
+        "    batch_len = tf.cast(tf.shape(y_true)[0], dtype=\"int64\")\n",
+        "    input_length = tf.cast(tf.shape(y_pred)[1], dtype=\"int64\")\n",
+        "    label_length = tf.cast(tf.shape(y_true)[1], dtype=\"int64\")\n",
+        "\n",
+        "    input_length = input_length * tf.ones(shape=(batch_len, 1), dtype=\"int64\")\n",
+        "    label_length = label_length * tf.ones(shape=(batch_len, 1), dtype=\"int64\")\n",
+        "\n",
+        "    loss = tf.keras.backend.ctc_batch_cost(y_true, y_pred, input_length, label_length)\n",
+        "    return loss"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 45,
+      "id": "a26dc3fc-a19c-4378-bd8c-e2b597a1d15c",
+      "metadata": {
+        "id": "a26dc3fc-a19c-4378-bd8c-e2b597a1d15c",
+        "tags": []
+      },
+      "outputs": [],
+      "source": [
+        "class ProduceExample(tf.keras.callbacks.Callback):\n",
+        "    def __init__(self, dataset) -> None:\n",
+        "        self.dataset = dataset.as_numpy_iterator()\n",
+        "\n",
+        "    def on_epoch_end(self, epoch, logs=None) -> None:\n",
+        "        data = self.dataset.next()\n",
+        "        yhat = self.model.predict(data[0])\n",
+        "        decoded = tf.keras.backend.ctc_decode(yhat, [75,75], greedy=False)[0][0].numpy()\n",
+        "        for x in range(len(yhat)):\n",
+        "            print('Original:', tf.strings.reduce_join(num_to_char(data[1][x])).numpy().decode('utf-8'))\n",
+        "            print('Prediction:', tf.strings.reduce_join(num_to_char(decoded[x])).numpy().decode('utf-8'))\n",
+        "            print('~'*100)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 46,
+      "id": "04be90d8-2482-46f9-b513-d5f4f8001c7e",
+      "metadata": {
+        "id": "04be90d8-2482-46f9-b513-d5f4f8001c7e",
+        "tags": []
+      },
+      "outputs": [],
+      "source": [
+        "model.compile(optimizer=Adam(learning_rate=0.0001), loss=CTCLoss)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 47,
+      "id": "eab49367-3f1e-4464-ae76-dbd07549d97e",
+      "metadata": {
+        "id": "eab49367-3f1e-4464-ae76-dbd07549d97e",
+        "tags": []
+      },
+      "outputs": [],
+      "source": [
+        "# Adjust the file path to end with '.weights.h5'\n",
+        "checkpoint_path = os.path.join('models', 'overlapped-weights368.weights.h5')\n",
+        "\n",
+        "# Create the ModelCheckpoint callback\n",
+        "checkpoint_callback = ModelCheckpoint(\n",
+        "    filepath=checkpoint_path,\n",
+        "    monitor='loss',\n",
+        "    save_weights_only=True\n",
+        ")\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 48,
+      "id": "e085a632-d464-46ef-8777-959cad4adb2c",
+      "metadata": {
+        "id": "e085a632-d464-46ef-8777-959cad4adb2c",
+        "tags": []
+      },
+      "outputs": [],
+      "source": [
+        "schedule_callback = LearningRateScheduler(scheduler)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 49,
+      "id": "48eca991-90ab-4592-8a79-b50e9ca015b6",
+      "metadata": {
+        "id": "48eca991-90ab-4592-8a79-b50e9ca015b6",
+        "tags": []
+      },
+      "outputs": [],
+      "source": [
+        "example_callback = ProduceExample(test)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 50,
+      "id": "8ffba483-aa61-4bbe-a15f-a73e1ddf097c",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "8ffba483-aa61-4bbe-a15f-a73e1ddf097c",
+        "outputId": "5ac8771e-cd3b-4d4d-eaf5-15add3c1e8ae",
+        "tags": []
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Epoch 1/100\n",
+            "WARNING:tensorflow:From c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\legacy\\backend.py:666: The name tf.nn.ctc_loss is deprecated. Please use tf.compat.v1.nn.ctc_loss instead.\n",
+            "\n"
+          ]
+        },
+        {
+          "ename": "InvalidArgumentError",
+          "evalue": "Graph execution error:\n\nDetected at node sequential_1/time_distributed_1/Reshape_17 defined at (most recent call last):\n  File \"\", line 198, in _run_module_as_main\n\n  File \"\", line 88, in _run_code\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel_launcher.py\", line 18, in \n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\traitlets\\config\\application.py\", line 1075, in launch_instance\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel\\kernelapp.py\", line 739, in start\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\tornado\\platform\\asyncio.py\", line 205, in start\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\asyncio\\base_events.py\", line 641, in run_forever\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\asyncio\\base_events.py\", line 1987, in _run_once\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\asyncio\\events.py\", line 88, in _run\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 545, in dispatch_queue\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 534, in process_one\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 437, in dispatch_shell\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel\\ipkernel.py\", line 362, in execute_request\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 778, in execute_request\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel\\ipkernel.py\", line 449, in do_execute\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel\\zmqshell.py\", line 549, in run_cell\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3075, in run_cell\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3130, in _run_cell\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\async_helpers.py\", line 129, in _pseudo_sync_runner\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3334, in run_cell_async\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3517, in run_ast_nodes\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3577, in run_code\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Temp\\ipykernel_11284\\3536403359.py\", line 1, in \n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 117, in error_handler\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\backend\\tensorflow\\trainer.py\", line 329, in fit\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\backend\\tensorflow\\trainer.py\", line 122, in one_step_on_iterator\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\backend\\tensorflow\\trainer.py\", line 110, in one_step_on_data\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\backend\\tensorflow\\trainer.py\", line 57, in train_step\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 117, in error_handler\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\layers\\layer.py\", line 826, in __call__\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 117, in error_handler\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\ops\\operation.py\", line 48, in __call__\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 156, in error_handler\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\models\\sequential.py\", line 206, in call\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\models\\functional.py\", line 199, in call\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\ops\\function.py\", line 151, in _run_through_graph\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\models\\functional.py\", line 583, in call\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 117, in error_handler\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\layers\\layer.py\", line 826, in __call__\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 117, in error_handler\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\ops\\operation.py\", line 48, in __call__\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 156, in error_handler\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\layers\\rnn\\time_distributed.py\", line 108, in call\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\layers\\rnn\\time_distributed.py\", line 102, in step_function\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\layers\\reshaping\\flatten.py\", line 54, in call\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\ops\\numpy.py\", line 4527, in reshape\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\backend\\tensorflow\\numpy.py\", line 1618, in reshape\n\nOnly one input size may be -1, not both 0 and 1\n\t [[{{node sequential_1/time_distributed_1/Reshape_17}}]] [Op:__inference_one_step_on_iterator_34683]",
+          "output_type": "error",
+          "traceback": [
+            "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+            "\u001b[1;31mInvalidArgumentError\u001b[0m                      Traceback (most recent call last)",
+            "Cell \u001b[1;32mIn[50], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtrain\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalidation_data\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtest\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mepochs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m100\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcallbacks\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[43mcheckpoint_callback\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mschedule_callback\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mexample_callback\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n",
+            "File \u001b[1;32mc:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\utils\\traceback_utils.py:122\u001b[0m, in \u001b[0;36mfilter_traceback..error_handler\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    119\u001b[0m     filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n\u001b[0;32m    120\u001b[0m     \u001b[38;5;66;03m# To get the full stack trace, call:\u001b[39;00m\n\u001b[0;32m    121\u001b[0m     \u001b[38;5;66;03m# `keras.config.disable_traceback_filtering()`\u001b[39;00m\n\u001b[1;32m--> 122\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m e\u001b[38;5;241m.\u001b[39mwith_traceback(filtered_tb) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m    123\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[0;32m    124\u001b[0m     \u001b[38;5;28;01mdel\u001b[39;00m filtered_tb\n",
+            "File \u001b[1;32mc:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\tensorflow\\python\\eager\\execute.py:53\u001b[0m, in \u001b[0;36mquick_execute\u001b[1;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[0;32m     51\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m     52\u001b[0m   ctx\u001b[38;5;241m.\u001b[39mensure_initialized()\n\u001b[1;32m---> 53\u001b[0m   tensors \u001b[38;5;241m=\u001b[39m pywrap_tfe\u001b[38;5;241m.\u001b[39mTFE_Py_Execute(ctx\u001b[38;5;241m.\u001b[39m_handle, device_name, op_name,\n\u001b[0;32m     54\u001b[0m                                       inputs, attrs, num_outputs)\n\u001b[0;32m     55\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m core\u001b[38;5;241m.\u001b[39m_NotOkStatusException \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m     56\u001b[0m   \u001b[38;5;28;01mif\u001b[39;00m name \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
+            "\u001b[1;31mInvalidArgumentError\u001b[0m: Graph execution error:\n\nDetected at node sequential_1/time_distributed_1/Reshape_17 defined at (most recent call last):\n  File \"\", line 198, in _run_module_as_main\n\n  File \"\", line 88, in _run_code\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel_launcher.py\", line 18, in \n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\traitlets\\config\\application.py\", line 1075, in launch_instance\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel\\kernelapp.py\", line 739, in start\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\tornado\\platform\\asyncio.py\", line 205, in start\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\asyncio\\base_events.py\", line 641, in run_forever\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\asyncio\\base_events.py\", line 1987, in _run_once\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\asyncio\\events.py\", line 88, in _run\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 545, in dispatch_queue\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 534, in process_one\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 437, in dispatch_shell\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel\\ipkernel.py\", line 362, in execute_request\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 778, in execute_request\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel\\ipkernel.py\", line 449, in do_execute\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel\\zmqshell.py\", line 549, in run_cell\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3075, in run_cell\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3130, in _run_cell\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\async_helpers.py\", line 129, in _pseudo_sync_runner\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3334, in run_cell_async\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3517, in run_ast_nodes\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3577, in run_code\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Temp\\ipykernel_11284\\3536403359.py\", line 1, in \n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 117, in error_handler\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\backend\\tensorflow\\trainer.py\", line 329, in fit\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\backend\\tensorflow\\trainer.py\", line 122, in one_step_on_iterator\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\backend\\tensorflow\\trainer.py\", line 110, in one_step_on_data\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\backend\\tensorflow\\trainer.py\", line 57, in train_step\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 117, in error_handler\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\layers\\layer.py\", line 826, in __call__\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 117, in error_handler\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\ops\\operation.py\", line 48, in __call__\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 156, in error_handler\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\models\\sequential.py\", line 206, in call\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\models\\functional.py\", line 199, in call\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\ops\\function.py\", line 151, in _run_through_graph\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\models\\functional.py\", line 583, in call\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 117, in error_handler\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\layers\\layer.py\", line 826, in __call__\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 117, in error_handler\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\ops\\operation.py\", line 48, in __call__\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 156, in error_handler\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\layers\\rnn\\time_distributed.py\", line 108, in call\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\layers\\rnn\\time_distributed.py\", line 102, in step_function\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\layers\\reshaping\\flatten.py\", line 54, in call\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\ops\\numpy.py\", line 4527, in reshape\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\backend\\tensorflow\\numpy.py\", line 1618, in reshape\n\nOnly one input size may be -1, not both 0 and 1\n\t [[{{node sequential_1/time_distributed_1/Reshape_17}}]] [Op:__inference_one_step_on_iterator_34683]"
+          ]
+        }
+      ],
+      "source": [
+        "model.fit(train, validation_data=test, epochs=100, callbacks=[checkpoint_callback, schedule_callback, example_callback])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "fa8ee94b-89f7-4733-8a0c-a86f86ff590a",
+      "metadata": {
+        "id": "fa8ee94b-89f7-4733-8a0c-a86f86ff590a",
+        "tags": []
+      },
+      "source": [
+        "# 5. Make a Prediction"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "01fa7204-ce0e-49a8-8dbd-14fe5dfead40",
+      "metadata": {
+        "id": "01fa7204-ce0e-49a8-8dbd-14fe5dfead40",
+        "tags": []
+      },
+      "outputs": [],
+      "source": [
+        "# url = 'https://drive.google.com/uc?id=1vWscXs4Vt0a_1IH1-ct2TCgXAZT-N3_Y'\n",
+        "# output = 'checkpoints.zip'\n",
+        "# gdown.download(url, output, quiet=False)\n",
+        "# gdown.extractall('checkpoints.zip', 'models')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 51,
+      "id": "247f664d-3c87-4e96-946e-930dad0e1c2c",
+      "metadata": {
+        "id": "247f664d-3c87-4e96-946e-930dad0e1c2c",
+        "tags": []
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "models\\unseen-weights178.h5\n"
+          ]
+        },
+        {
+          "ename": "ValueError",
+          "evalue": "Layer count mismatch when loading weights from file. Model expected 7 layers, found 9 saved layers.",
+          "output_type": "error",
+          "traceback": [
+            "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+            "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
+            "Cell \u001b[1;32mIn[51], line 3\u001b[0m\n\u001b[0;32m      1\u001b[0m checkpoint_path \u001b[38;5;241m=\u001b[39m os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mjoin(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmodels\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124munseen-weights178.h5\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m      2\u001b[0m \u001b[38;5;28mprint\u001b[39m(checkpoint_path)\n\u001b[1;32m----> 3\u001b[0m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mload_weights\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcheckpoint_path\u001b[49m\u001b[43m)\u001b[49m\n",
+            "File \u001b[1;32mc:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\utils\\traceback_utils.py:122\u001b[0m, in \u001b[0;36mfilter_traceback..error_handler\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    119\u001b[0m     filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n\u001b[0;32m    120\u001b[0m     \u001b[38;5;66;03m# To get the full stack trace, call:\u001b[39;00m\n\u001b[0;32m    121\u001b[0m     \u001b[38;5;66;03m# `keras.config.disable_traceback_filtering()`\u001b[39;00m\n\u001b[1;32m--> 122\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m e\u001b[38;5;241m.\u001b[39mwith_traceback(filtered_tb) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m    123\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[0;32m    124\u001b[0m     \u001b[38;5;28;01mdel\u001b[39;00m filtered_tb\n",
+            "File \u001b[1;32mc:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\legacy\\saving\\legacy_h5_format.py:357\u001b[0m, in \u001b[0;36mload_weights_from_hdf5_group\u001b[1;34m(f, model)\u001b[0m\n\u001b[0;32m    355\u001b[0m layer_names \u001b[38;5;241m=\u001b[39m filtered_layer_names\n\u001b[0;32m    356\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(layer_names) \u001b[38;5;241m!=\u001b[39m \u001b[38;5;28mlen\u001b[39m(filtered_layers):\n\u001b[1;32m--> 357\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m    358\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mLayer count mismatch when loading weights from file. \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    359\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mModel expected \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlen\u001b[39m(filtered_layers)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m layers, found \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    360\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlen\u001b[39m(layer_names)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m saved layers.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    361\u001b[0m     )\n\u001b[0;32m    363\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m k, name \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(layer_names):\n\u001b[0;32m    364\u001b[0m     g \u001b[38;5;241m=\u001b[39m f[name]\n",
+            "\u001b[1;31mValueError\u001b[0m: Layer count mismatch when loading weights from file. Model expected 7 layers, found 9 saved layers."
+          ]
+        }
+      ],
+      "source": [
+        "checkpoint_path = os.path.join('models', 'unseen-weights178.h5')\n",
+        "print(checkpoint_path)\n",
+        "model.load_weights(checkpoint_path)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "7f8d689f-b7bb-443c-9b88-e40c1d800828",
+      "metadata": {
+        "id": "7f8d689f-b7bb-443c-9b88-e40c1d800828",
+        "tags": []
+      },
+      "outputs": [],
+      "source": [
+        "test_data = test.as_numpy_iterator()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "38546dc2-bee9-4837-864b-8a884df40ad7",
+      "metadata": {
+        "id": "38546dc2-bee9-4837-864b-8a884df40ad7",
+        "tags": []
+      },
+      "outputs": [],
+      "source": [
+        "sample = test_data.next()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "a43621f0-229d-4c0d-9554-9c3a3da9c61a",
+      "metadata": {
+        "id": "a43621f0-229d-4c0d-9554-9c3a3da9c61a",
+        "tags": []
+      },
+      "outputs": [],
+      "source": [
+        "yhat = model.predict(sample[0])"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "ea462999-f87e-4a7e-a057-5be7b6d8f7d5",
+      "metadata": {
+        "id": "ea462999-f87e-4a7e-a057-5be7b6d8f7d5",
+        "tags": []
+      },
+      "outputs": [],
+      "source": [
+        "print('~'*100, 'REAL TEXT')\n",
+        "[tf.strings.reduce_join([num_to_char(word) for word in sentence]) for sentence in sample[1]]"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "82bd4c10-dd6e-411e-834b-2a3b43fd12c5",
+      "metadata": {
+        "id": "82bd4c10-dd6e-411e-834b-2a3b43fd12c5",
+        "tags": []
+      },
+      "outputs": [],
+      "source": [
+        "decoded = tf.keras.backend.ctc_decode(yhat, input_length=[75,75], greedy=True)[0][0].numpy()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "5d68ac46-c90b-4eab-a709-f19aee569ff5",
+      "metadata": {
+        "id": "5d68ac46-c90b-4eab-a709-f19aee569ff5",
+        "tags": []
+      },
+      "outputs": [],
+      "source": [
+        "print('~'*100, 'PREDICTIONS')\n",
+        "[tf.strings.reduce_join([num_to_char(word) for word in sentence]) for sentence in decoded]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "64622f98-e99b-4fed-a2cc-f0da82eb5431",
+      "metadata": {
+        "id": "64622f98-e99b-4fed-a2cc-f0da82eb5431"
+      },
+      "source": [
+        "# Test on a Video"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "a8b0c4d0-2031-4331-b91d-d87b1ae6f6e2",
+      "metadata": {
+        "id": "a8b0c4d0-2031-4331-b91d-d87b1ae6f6e2"
+      },
+      "outputs": [],
+      "source": [
+        "sample = load_data(tf.convert_to_tensor('.\\\\data\\\\s1\\\\bras9a.mpg'))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "0cca60e4-47a9-4683-8a75-48f4684f723d",
+      "metadata": {
+        "id": "0cca60e4-47a9-4683-8a75-48f4684f723d"
+      },
+      "outputs": [],
+      "source": [
+        "print('~'*100, 'REAL TEXT')\n",
+        "[tf.strings.reduce_join([num_to_char(word) for word in sentence]) for sentence in [sample[1]]]"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "8cc5037c-1e32-435c-b0cc-01e1fb3b863c",
+      "metadata": {
+        "id": "8cc5037c-1e32-435c-b0cc-01e1fb3b863c"
+      },
+      "outputs": [],
+      "source": [
+        "yhat = model.predict(tf.expand_dims(sample[0], axis=0))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "22c4f77d-715d-409f-bc5e-3ebe48704e8f",
+      "metadata": {
+        "id": "22c4f77d-715d-409f-bc5e-3ebe48704e8f"
+      },
+      "outputs": [],
+      "source": [
+        "# decoded = tf.keras.backend.ctc_decode(yhat, input_length=[75], greedy=True)[0][0].numpy()\n",
+        "decoded = keras.backend.ctc_decode(yhat, input_length=[75], greedy=True)[0][0].numpy()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "e4d12ecc-b634-499e-a4bc-db9f010835fb",
+      "metadata": {
+        "id": "e4d12ecc-b634-499e-a4bc-db9f010835fb"
+      },
+      "outputs": [],
+      "source": [
+        "print('~'*100, 'PREDICTIONS')\n",
+        "[tf.strings.reduce_join([num_to_char(word) for word in sentence]) for sentence in decoded]"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "551dfea2-de6b-4400-b71a-a17631529e3f",
+      "metadata": {
+        "id": "551dfea2-de6b-4400-b71a-a17631529e3f"
+      },
+      "outputs": [],
+      "source": []
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "fa95863d-3832-47bf-8a77-ebaa38054ace",
+      "metadata": {
+        "id": "fa95863d-3832-47bf-8a77-ebaa38054ace"
+      },
+      "outputs": [],
+      "source": []
+    }
+  ],
+  "metadata": {
+    "accelerator": "GPU",
+    "colab": {
+      "gpuType": "T4",
+      "include_colab_link": true,
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.12.3"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 5
+}
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       ]
@@ -1585,7 +1585,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 85,
+   "execution_count": 189,
    "id": "84593332-133c-4205-b7a6-8e235d5e2b3b",
    "metadata": {
     "tags": []
@@ -1594,10 +1594,10 @@
     {
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+       ""
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      },
-     "execution_count": 85,
+     "execution_count": 189,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1618,7 +1618,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 92,
+   "execution_count": 190,
    "id": "d8e9a497-191b-4842-afbd-26f5e13c43ba",
    "metadata": {
     "tags": []
@@ -1635,7 +1635,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 93,
+   "execution_count": 191,
    "id": "3f753ed2-70b9-4236-8c1c-08ca065dc8bf",
    "metadata": {},
    "outputs": [
@@ -1645,7 +1645,7 @@
        "(75, 46, 140, 1)"
       ]
      },
-     "execution_count": 93,
+     "execution_count": 191,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1656,7 +1656,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 94,
+   "execution_count": 192,
    "id": "f9171056-a352-491a-9ed9-92b28ced268e",
    "metadata": {
     "tags": []
@@ -1690,7 +1690,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 95,
+   "execution_count": 193,
    "id": "78851825-2bcd-42a9-b7f2-28bb5a6bf43a",
    "metadata": {
     "collapsed": true,
@@ -1703,11 +1703,11 @@
     {
      "data": {
       "text/html": [
-       "
Model: \"sequential_3\"\n",
+       "
Model: \"sequential_5\"\n",
        "
\n"
       ],
       "text/plain": [
-       "\u001b[1mModel: \"sequential_3\"\u001b[0m\n"
+       "\u001b[1mModel: \"sequential_5\"\u001b[0m\n"
       ]
      },
      "metadata": {},
@@ -1719,42 +1719,42 @@
        "
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
        "┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃\n",
        "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
-       "│ conv3d_9 (Conv3D)               │ (None, 75, 46, 140,    │         3,584 │\n",
+       "│ conv3d_15 (Conv3D)              │ (None, 75, 46, 140,    │         3,584 │\n",
        "│                                 │ 128)                   │               │\n",
        "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ activation_9 (Activation)       │ (None, 75, 46, 140,    │             0 │\n",
+       "│ activation_15 (Activation)      │ (None, 75, 46, 140,    │             0 │\n",
        "│                                 │ 128)                   │               │\n",
        "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ max_pooling3d_9 (MaxPooling3D)  │ (None, 75, 23, 70,     │             0 │\n",
+       "│ max_pooling3d_15 (MaxPooling3D) │ (None, 75, 23, 70,     │             0 │\n",
        "│                                 │ 128)                   │               │\n",
        "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ conv3d_10 (Conv3D)              │ (None, 75, 23, 70,     │       884,992 │\n",
+       "│ conv3d_16 (Conv3D)              │ (None, 75, 23, 70,     │       884,992 │\n",
        "│                                 │ 256)                   │               │\n",
        "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ activation_10 (Activation)      │ (None, 75, 23, 70,     │             0 │\n",
+       "│ activation_16 (Activation)      │ (None, 75, 23, 70,     │             0 │\n",
        "│                                 │ 256)                   │               │\n",
        "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ max_pooling3d_10 (MaxPooling3D) │ (None, 75, 11, 35,     │             0 │\n",
+       "│ max_pooling3d_16 (MaxPooling3D) │ (None, 75, 11, 35,     │             0 │\n",
        "│                                 │ 256)                   │               │\n",
        "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ conv3d_11 (Conv3D)              │ (None, 75, 11, 35, 75) │       518,475 │\n",
+       "│ conv3d_17 (Conv3D)              │ (None, 75, 11, 35, 75) │       518,475 │\n",
        "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ activation_11 (Activation)      │ (None, 75, 11, 35, 75) │             0 │\n",
+       "│ activation_17 (Activation)      │ (None, 75, 11, 35, 75) │             0 │\n",
        "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ max_pooling3d_11 (MaxPooling3D) │ (None, 75, 5, 17, 75)  │             0 │\n",
+       "│ max_pooling3d_17 (MaxPooling3D) │ (None, 75, 5, 17, 75)  │             0 │\n",
        "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ time_distributed_3              │ (None, 75, 6375)       │             0 │\n",
+       "│ time_distributed_5              │ (None, 75, 6375)       │             0 │\n",
        "│ (TimeDistributed)               │                        │               │\n",
        "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ bidirectional_3 (Bidirectional) │ (None, 75, 256)        │     6,660,096 │\n",
+       "│ bidirectional_7 (Bidirectional) │ (None, 75, 256)        │     6,660,096 │\n",
        "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dropout_2 (Dropout)             │ (None, 75, 256)        │             0 │\n",
+       "│ dropout_6 (Dropout)             │ (None, 75, 256)        │             0 │\n",
        "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ bidirectional_4 (Bidirectional) │ (None, 75, 256)        │       394,240 │\n",
+       "│ bidirectional_8 (Bidirectional) │ (None, 75, 256)        │       394,240 │\n",
        "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dropout_3 (Dropout)             │ (None, 75, 256)        │             0 │\n",
+       "│ dropout_7 (Dropout)             │ (None, 75, 256)        │             0 │\n",
        "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dense_1 (Dense)                 │ (None, 75, 41)         │        10,537 │\n",
+       "│ dense_3 (Dense)                 │ (None, 75, 41)         │        10,537 │\n",
        "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
        "
\n"
       ],
@@ -1762,42 +1762,42 @@
        "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
        "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
        "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
-       "│ conv3d_9 (\u001b[38;5;33mConv3D\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m46\u001b[0m, \u001b[38;5;34m140\u001b[0m,    │         \u001b[38;5;34m3,584\u001b[0m │\n",
+       "│ conv3d_15 (\u001b[38;5;33mConv3D\u001b[0m)              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m46\u001b[0m, \u001b[38;5;34m140\u001b[0m,    │         \u001b[38;5;34m3,584\u001b[0m │\n",
        "│                                 │ \u001b[38;5;34m128\u001b[0m)                   │               │\n",
        "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ activation_9 (\u001b[38;5;33mActivation\u001b[0m)       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m46\u001b[0m, \u001b[38;5;34m140\u001b[0m,    │             \u001b[38;5;34m0\u001b[0m │\n",
+       "│ activation_15 (\u001b[38;5;33mActivation\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m46\u001b[0m, \u001b[38;5;34m140\u001b[0m,    │             \u001b[38;5;34m0\u001b[0m │\n",
        "│                                 │ \u001b[38;5;34m128\u001b[0m)                   │               │\n",
        "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ max_pooling3d_9 (\u001b[38;5;33mMaxPooling3D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m23\u001b[0m, \u001b[38;5;34m70\u001b[0m,     │             \u001b[38;5;34m0\u001b[0m │\n",
+       "│ max_pooling3d_15 (\u001b[38;5;33mMaxPooling3D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m23\u001b[0m, \u001b[38;5;34m70\u001b[0m,     │             \u001b[38;5;34m0\u001b[0m │\n",
        "│                                 │ \u001b[38;5;34m128\u001b[0m)                   │               │\n",
        "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ conv3d_10 (\u001b[38;5;33mConv3D\u001b[0m)              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m23\u001b[0m, \u001b[38;5;34m70\u001b[0m,     │       \u001b[38;5;34m884,992\u001b[0m │\n",
+       "│ conv3d_16 (\u001b[38;5;33mConv3D\u001b[0m)              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m23\u001b[0m, \u001b[38;5;34m70\u001b[0m,     │       \u001b[38;5;34m884,992\u001b[0m │\n",
        "│                                 │ \u001b[38;5;34m256\u001b[0m)                   │               │\n",
        "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ activation_10 (\u001b[38;5;33mActivation\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m23\u001b[0m, \u001b[38;5;34m70\u001b[0m,     │             \u001b[38;5;34m0\u001b[0m │\n",
+       "│ activation_16 (\u001b[38;5;33mActivation\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m23\u001b[0m, \u001b[38;5;34m70\u001b[0m,     │             \u001b[38;5;34m0\u001b[0m │\n",
        "│                                 │ \u001b[38;5;34m256\u001b[0m)                   │               │\n",
        "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ max_pooling3d_10 (\u001b[38;5;33mMaxPooling3D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m11\u001b[0m, \u001b[38;5;34m35\u001b[0m,     │             \u001b[38;5;34m0\u001b[0m │\n",
+       "│ max_pooling3d_16 (\u001b[38;5;33mMaxPooling3D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m11\u001b[0m, \u001b[38;5;34m35\u001b[0m,     │             \u001b[38;5;34m0\u001b[0m │\n",
        "│                                 │ \u001b[38;5;34m256\u001b[0m)                   │               │\n",
        "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ conv3d_11 (\u001b[38;5;33mConv3D\u001b[0m)              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m11\u001b[0m, \u001b[38;5;34m35\u001b[0m, \u001b[38;5;34m75\u001b[0m) │       \u001b[38;5;34m518,475\u001b[0m │\n",
+       "│ conv3d_17 (\u001b[38;5;33mConv3D\u001b[0m)              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m11\u001b[0m, \u001b[38;5;34m35\u001b[0m, \u001b[38;5;34m75\u001b[0m) │       \u001b[38;5;34m518,475\u001b[0m │\n",
        "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ activation_11 (\u001b[38;5;33mActivation\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m11\u001b[0m, \u001b[38;5;34m35\u001b[0m, \u001b[38;5;34m75\u001b[0m) │             \u001b[38;5;34m0\u001b[0m │\n",
+       "│ activation_17 (\u001b[38;5;33mActivation\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m11\u001b[0m, \u001b[38;5;34m35\u001b[0m, \u001b[38;5;34m75\u001b[0m) │             \u001b[38;5;34m0\u001b[0m │\n",
        "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ max_pooling3d_11 (\u001b[38;5;33mMaxPooling3D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m17\u001b[0m, \u001b[38;5;34m75\u001b[0m)  │             \u001b[38;5;34m0\u001b[0m │\n",
+       "│ max_pooling3d_17 (\u001b[38;5;33mMaxPooling3D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m17\u001b[0m, \u001b[38;5;34m75\u001b[0m)  │             \u001b[38;5;34m0\u001b[0m │\n",
        "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ time_distributed_3              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m6375\u001b[0m)       │             \u001b[38;5;34m0\u001b[0m │\n",
+       "│ time_distributed_5              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m6375\u001b[0m)       │             \u001b[38;5;34m0\u001b[0m │\n",
        "│ (\u001b[38;5;33mTimeDistributed\u001b[0m)               │                        │               │\n",
        "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ bidirectional_3 (\u001b[38;5;33mBidirectional\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m256\u001b[0m)        │     \u001b[38;5;34m6,660,096\u001b[0m │\n",
+       "│ bidirectional_7 (\u001b[38;5;33mBidirectional\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m256\u001b[0m)        │     \u001b[38;5;34m6,660,096\u001b[0m │\n",
        "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dropout_2 (\u001b[38;5;33mDropout\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m256\u001b[0m)        │             \u001b[38;5;34m0\u001b[0m │\n",
+       "│ dropout_6 (\u001b[38;5;33mDropout\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m256\u001b[0m)        │             \u001b[38;5;34m0\u001b[0m │\n",
        "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ bidirectional_4 (\u001b[38;5;33mBidirectional\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m256\u001b[0m)        │       \u001b[38;5;34m394,240\u001b[0m │\n",
+       "│ bidirectional_8 (\u001b[38;5;33mBidirectional\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m256\u001b[0m)        │       \u001b[38;5;34m394,240\u001b[0m │\n",
        "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dropout_3 (\u001b[38;5;33mDropout\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m256\u001b[0m)        │             \u001b[38;5;34m0\u001b[0m │\n",
+       "│ dropout_7 (\u001b[38;5;33mDropout\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m256\u001b[0m)        │             \u001b[38;5;34m0\u001b[0m │\n",
        "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dense_1 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m41\u001b[0m)         │        \u001b[38;5;34m10,537\u001b[0m │\n",
+       "│ dense_3 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m41\u001b[0m)         │        \u001b[38;5;34m10,537\u001b[0m │\n",
        "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
       ]
      },
@@ -1850,7 +1850,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 96,
+   "execution_count": 194,
    "id": "f4b4798c-a65a-4c47-9e2a-3b09dc98d320",
    "metadata": {},
    "outputs": [
@@ -1860,7 +1860,7 @@
        "6375"
       ]
      },
-     "execution_count": 96,
+     "execution_count": 194,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1871,7 +1871,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 97,
+   "execution_count": 195,
    "id": "e5c2eae0-c359-41a4-97a0-75c44dccb7d1",
    "metadata": {
     "tags": []
@@ -1891,17 +1891,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 98,
+   "execution_count": 196,
    "id": "ffdc7319-0d69-4f7e-a6d4-ce72deb81c0b",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       ""
+       ""
       ]
      },
-     "execution_count": 98,
+     "execution_count": 196,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1912,7 +1912,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 99,
+   "execution_count": 197,
    "id": "6ed47531-8317-4255-9a12-b757642258e6",
    "metadata": {
     "tags": []
@@ -1921,10 +1921,10 @@
     {
      "data": {
       "text/plain": [
-       ""
+       ""
       ]
      },
-     "execution_count": 99,
+     "execution_count": 197,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1935,7 +1935,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 100,
+   "execution_count": 198,
    "id": "7c37b9b9-5298-4038-9c33-5031d1b457f0",
    "metadata": {
     "tags": []
@@ -1947,7 +1947,7 @@
        "(None, 75, 46, 140, 1)"
       ]
      },
-     "execution_count": 100,
+     "execution_count": 198,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1958,7 +1958,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 101,
+   "execution_count": 199,
    "id": "98b316a4-5322-4782-8e36-4b3c1a696d85",
    "metadata": {
     "tags": []
@@ -1970,7 +1970,7 @@
        "(None, 75, 41)"
       ]
      },
-     "execution_count": 101,
+     "execution_count": 199,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1991,7 +1991,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 102,
+   "execution_count": 200,
    "id": "ab015fd0-7fb4-4d5d-9fa2-30a05dbd515a",
    "metadata": {
     "tags": []
@@ -2007,7 +2007,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 103,
+   "execution_count": 201,
    "id": "c564d5c9-db54-4e88-b311-9aeab7fb3e69",
    "metadata": {
     "tags": []
@@ -2028,7 +2028,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 104,
+   "execution_count": 202,
    "id": "a26dc3fc-a19c-4378-bd8c-e2b597a1d15c",
    "metadata": {
     "tags": []
@@ -2051,7 +2051,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 105,
+   "execution_count": 203,
    "id": "04be90d8-2482-46f9-b513-d5f4f8001c7e",
    "metadata": {
     "tags": []
@@ -2063,7 +2063,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 109,
+   "execution_count": 204,
    "id": "eab49367-3f1e-4464-ae76-dbd07549d97e",
    "metadata": {
     "tags": []
@@ -2076,7 +2076,7 @@
      "traceback": [
       "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "Cell \u001b[1;32mIn[109], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m checkpoint_callback \u001b[38;5;241m=\u001b[39m \u001b[43mModelCheckpoint\u001b[49m\u001b[43m(\u001b[49m\u001b[43mos\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpath\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mjoin\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mmodels\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mcheckpoint\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmonitor\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mloss\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msave_weights_only\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n",
+      "Cell \u001b[1;32mIn[204], line 2\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;66;03m# checkpoint_callback = ModelCheckpoint(os.path.join('models','checkpoint'), monitor='loss', save_weights_only=True)\u001b[39;00m\n\u001b[1;32m----> 2\u001b[0m checkpoint_callback \u001b[38;5;241m=\u001b[39m \u001b[43mModelCheckpoint\u001b[49m\u001b[43m(\u001b[49m\u001b[43mos\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpath\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mjoin\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mmodels\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mcheckpoint\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmonitor\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mloss\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msave_weights_only\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n",
       "File \u001b[1;32mc:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\callbacks\\model_checkpoint.py:183\u001b[0m, in \u001b[0;36mModelCheckpoint.__init__\u001b[1;34m(self, filepath, monitor, verbose, save_best_only, save_weights_only, mode, save_freq, initial_value_threshold)\u001b[0m\n\u001b[0;32m    181\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m save_weights_only:\n\u001b[0;32m    182\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfilepath\u001b[38;5;241m.\u001b[39mendswith(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m.weights.h5\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[1;32m--> 183\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m    184\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mWhen using `save_weights_only=True` in `ModelCheckpoint`\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    185\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m, the filepath provided must end in `.weights.h5` \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    186\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m(Keras weights format). Received: \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    187\u001b[0m             \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfilepath=\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfilepath\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    188\u001b[0m         )\n\u001b[0;32m    189\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    190\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfilepath\u001b[38;5;241m.\u001b[39mendswith(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m.keras\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n",
       "\u001b[1;31mValueError\u001b[0m: When using `save_weights_only=True` in `ModelCheckpoint`, the filepath provided must end in `.weights.h5` (Keras weights format). Received: filepath=models\\checkpoint"
      ]
diff --git a/app/utils.py b/app/utils.py
index b73bfef1..29785c5b 100644
--- a/app/utils.py
+++ b/app/utils.py
@@ -1,7 +1,8 @@
 import tensorflow as tf
 from typing import List
+import numpy as np
 import cv2
-import os 
+import os
 
 vocab = [x for x in "abcdefghijklmnopqrstuvwxyz'?!123456789 "]
 char_to_num = tf.keras.layers.StringLookup(vocabulary=vocab, oov_token="")
@@ -10,39 +11,40 @@
     vocabulary=char_to_num.get_vocabulary(), oov_token="", invert=True
 )
 
-def load_video(path:str) -> List[float]: 
+def load_video(path:str) -> List[float]:
     #print(path)
     cap = cv2.VideoCapture(path)
     frames = []
-    for _ in range(int(cap.get(cv2.CAP_PROP_FRAME_COUNT))): 
+    for _ in range(int(cap.get(cv2.CAP_PROP_FRAME_COUNT))):
         ret, frame = cap.read()
         frame = tf.image.rgb_to_grayscale(frame)
         frames.append(frame[190:236,80:220,:])
     cap.release()
-    
+
     mean = tf.math.reduce_mean(frames)
     std = tf.math.reduce_std(tf.cast(frames, tf.float32))
     return tf.cast((frames - mean), tf.float32) / std
-    
-def load_alignments(path:str) -> List[str]: 
+
+def load_alignments(path:str) -> List[str]:
     #print(path)
-    with open(path, 'r') as f: 
-        lines = f.readlines() 
+    with open(path, 'r') as f:
+        lines = f.readlines()
     tokens = []
     for line in lines:
         line = line.split()
-        if line[2] != 'sil': 
+        if line[2] != 'sil':
             tokens = [*tokens,' ',line[2]]
     return char_to_num(tf.reshape(tf.strings.unicode_split(tokens, input_encoding='UTF-8'), (-1)))[1:]
 
-def load_data(path: str): 
+def load_data(path: str):
     path = bytes.decode(path.numpy())
     file_name = path.split('/')[-1].split('.')[0]
     # File name splitting for windows
     file_name = path.split('\\')[-1].split('.')[0]
     video_path = os.path.join('..','data','s1',f'{file_name}.mpg')
     alignment_path = os.path.join('..','data','alignments','s1',f'{file_name}.align')
-    frames = load_video(video_path) 
+    frames = load_video(video_path)
+    print(frames.shape)
     alignments = load_alignments(alignment_path)
-    
+
     return frames, alignments
\ No newline at end of file

From 3b58183bd1fb2e28f61407557d7005209ecd1557 Mon Sep 17 00:00:00 2001
From: "haidun.li" 
Date: Tue, 16 Apr 2024 01:06:24 -0300
Subject: [PATCH 08/17] Update kernelspec display name and language version in
 LipNet.ipynb

---
 LipNet.ipynb    |    6 +-
 LipNet_my.ipynb | 2590 -----------------------------------------------
 2 files changed, 3 insertions(+), 2593 deletions(-)
 delete mode 100644 LipNet_my.ipynb

diff --git a/LipNet.ipynb b/LipNet.ipynb
index cedd532f..628862ec 100644
--- a/LipNet.ipynb
+++ b/LipNet.ipynb
@@ -2431,9 +2431,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "lips",
+   "display_name": "Python 3",
    "language": "python",
-   "name": "lips"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -2445,7 +2445,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.12"
+   "version": "3.12.3"
   }
  },
  "nbformat": 4,
diff --git a/LipNet_my.ipynb b/LipNet_my.ipynb
deleted file mode 100644
index 0141a560..00000000
--- a/LipNet_my.ipynb
+++ /dev/null
@@ -1,2590 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "colab_type": "text",
-        "id": "view-in-github"
-      },
-      "source": [
-        "\"Open"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "a3573a47-3689-4668-b62f-5c8451b2b4e9",
-      "metadata": {
-        "id": "a3573a47-3689-4668-b62f-5c8451b2b4e9",
-        "tags": []
-      },
-      "source": [
-        "# 0. Install and Import Dependencies"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 1,
-      "id": "ddfbccbe-41ae-4c23-98b1-a13868e2b499",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "ddfbccbe-41ae-4c23-98b1-a13868e2b499",
-        "outputId": "964dae10-91f7-4769-b15c-4a00ca9db312",
-        "scrolled": true,
-        "tags": []
-      },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Package                   Version\n",
-            "------------------------- --------------\n",
-            "absl-py                   2.1.0\n",
-            "altair                    5.3.0\n",
-            "anyio                     4.3.0\n",
-            "argon2-cffi               23.1.0\n",
-            "argon2-cffi-bindings      21.2.0\n",
-            "arrow                     1.3.0\n",
-            "asttokens                 2.4.1\n",
-            "astunparse                1.6.3\n",
-            "async-lru                 2.0.4\n",
-            "attrs                     23.2.0\n",
-            "av                        12.0.0\n",
-            "Babel                     2.14.0\n",
-            "beautifulsoup4            4.12.3\n",
-            "bleach                    6.1.0\n",
-            "blinker                   1.7.0\n",
-            "cachetools                5.3.3\n",
-            "certifi                   2024.2.2\n",
-            "cffi                      1.16.0\n",
-            "charset-normalizer        3.3.2\n",
-            "click                     8.1.7\n",
-            "colorama                  0.4.6\n",
-            "comm                      0.2.2\n",
-            "contourpy                 1.2.1\n",
-            "cycler                    0.12.1\n",
-            "debugpy                   1.8.1\n",
-            "decorator                 5.1.1\n",
-            "defusedxml                0.7.1\n",
-            "executing                 2.0.1\n",
-            "fastjsonschema            2.19.1\n",
-            "filelock                  3.13.4\n",
-            "flatbuffers               24.3.25\n",
-            "fonttools                 4.51.0\n",
-            "fqdn                      1.5.1\n",
-            "fsspec                    2024.3.1\n",
-            "gast                      0.5.4\n",
-            "gdown                     5.1.0\n",
-            "gitdb                     4.0.11\n",
-            "GitPython                 3.1.43\n",
-            "google-pasta              0.2.0\n",
-            "grpcio                    1.62.1\n",
-            "h11                       0.14.0\n",
-            "h5py                      3.11.0\n",
-            "httpcore                  1.0.5\n",
-            "httpx                     0.27.0\n",
-            "idna                      3.7\n",
-            "imageio                   2.23.0\n",
-            "ipykernel                 6.29.4\n",
-            "ipynb-py-convert          0.4.6\n",
-            "ipython                   8.23.0\n",
-            "ipywidgets                8.1.2\n",
-            "isoduration               20.11.0\n",
-            "jedi                      0.19.1\n",
-            "Jinja2                    3.1.3\n",
-            "json5                     0.9.24\n",
-            "jsonpointer               2.4\n",
-            "jsonschema                4.21.1\n",
-            "jsonschema-specifications 2023.12.1\n",
-            "jupyter                   1.0.0\n",
-            "jupyter_client            8.6.1\n",
-            "jupyter-console           6.6.3\n",
-            "jupyter_core              5.7.2\n",
-            "jupyter-events            0.10.0\n",
-            "jupyter-lsp               2.2.5\n",
-            "jupyter_server            2.13.0\n",
-            "jupyter_server_terminals  0.5.3\n",
-            "jupyterlab                4.1.6\n",
-            "jupyterlab_pygments       0.3.0\n",
-            "jupyterlab_server         2.26.0\n",
-            "jupyterlab_widgets        3.0.10\n",
-            "kiwisolver                1.4.5\n",
-            "libclang                  18.1.1\n",
-            "Markdown                  3.6\n",
-            "markdown-it-py            3.0.0\n",
-            "MarkupSafe                2.1.5\n",
-            "matplotlib                3.8.4\n",
-            "matplotlib-inline         0.1.6\n",
-            "mdurl                     0.1.2\n",
-            "mistune                   3.0.2\n",
-            "ml-dtypes                 0.3.2\n",
-            "mpmath                    1.3.0\n",
-            "namex                     0.0.7\n",
-            "nbclient                  0.10.0\n",
-            "nbconvert                 7.16.3\n",
-            "nbformat                  5.10.4\n",
-            "nest-asyncio              1.6.0\n",
-            "networkx                  3.3\n",
-            "notebook                  7.1.2\n",
-            "notebook_shim             0.2.4\n",
-            "numpy                     1.26.4\n",
-            "opencv-python             4.6.0.66\n",
-            "opt-einsum                3.3.0\n",
-            "optree                    0.11.0\n",
-            "overrides                 7.7.0\n",
-            "packaging                 24.0\n",
-            "pandas                    2.2.1\n",
-            "pandocfilters             1.5.1\n",
-            "parso                     0.8.4\n",
-            "pillow                    10.3.0\n",
-            "pip                       24.0\n",
-            "platformdirs              4.2.0\n",
-            "prometheus_client         0.20.0\n",
-            "prompt-toolkit            3.0.43\n",
-            "protobuf                  4.25.3\n",
-            "psutil                    5.9.8\n",
-            "pure-eval                 0.2.2\n",
-            "pyarrow                   15.0.2\n",
-            "pycparser                 2.22\n",
-            "pydeck                    0.8.1b0\n",
-            "Pygments                  2.17.2\n",
-            "pyparsing                 3.1.2\n",
-            "PySocks                   1.7.1\n",
-            "python-dateutil           2.9.0.post0\n",
-            "python-json-logger        2.0.7\n",
-            "pytz                      2024.1\n",
-            "pywin32                   306\n",
-            "pywinpty                  2.0.13\n",
-            "PyYAML                    6.0.1\n",
-            "pyzmq                     25.1.2\n",
-            "qtconsole                 5.5.1\n",
-            "QtPy                      2.4.1\n",
-            "referencing               0.34.0\n",
-            "requests                  2.31.0\n",
-            "rfc3339-validator         0.1.4\n",
-            "rfc3986-validator         0.1.1\n",
-            "rich                      13.7.1\n",
-            "rpds-py                   0.18.0\n",
-            "Send2Trash                1.8.3\n",
-            "setuptools                69.2.0\n",
-            "six                       1.16.0\n",
-            "smmap                     5.0.1\n",
-            "sniffio                   1.3.1\n",
-            "soupsieve                 2.5\n",
-            "stack-data                0.6.3\n",
-            "streamlit                 1.33.0\n",
-            "sympy                     1.12\n",
-            "tenacity                  8.2.3\n",
-            "tensorboard               2.16.2\n",
-            "tensorboard-data-server   0.7.2\n",
-            "tensorflow                2.16.1\n",
-            "tensorflow-intel          2.16.1\n",
-            "termcolor                 2.4.0\n",
-            "terminado                 0.18.1\n",
-            "tinycss2                  1.2.1\n",
-            "toml                      0.10.2\n",
-            "toolz                     0.12.1\n",
-            "torch                     2.2.2\n",
-            "torchvision               0.17.2\n",
-            "tornado                   6.4\n",
-            "tqdm                      4.66.2\n",
-            "traitlets                 5.14.2\n",
-            "types-python-dateutil     2.9.0.20240316\n",
-            "typing_extensions         4.11.0\n",
-            "tzdata                    2024.1\n",
-            "uri-template              1.3.0\n",
-            "urllib3                   2.2.1\n",
-            "watchdog                  4.0.0\n",
-            "wcwidth                   0.2.13\n",
-            "webcolors                 1.13\n",
-            "webencodings              0.5.1\n",
-            "websocket-client          1.7.0\n",
-            "Werkzeug                  3.0.2\n",
-            "wheel                     0.43.0\n",
-            "widgetsnbextension        4.0.10\n",
-            "wrapt                     1.16.0\n"
-          ]
-        }
-      ],
-      "source": [
-        "!pip list"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 2,
-      "id": "02f907ea-f669-46c7-adcf-7f257e663448",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "02f907ea-f669-46c7-adcf-7f257e663448",
-        "outputId": "e444b2f9-189a-40bd-a2f9-925f13917c35",
-        "tags": []
-      },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Requirement already satisfied: opencv-python in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (4.6.0.66)\n",
-            "Requirement already satisfied: matplotlib in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (3.8.4)\n",
-            "Requirement already satisfied: imageio in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (2.23.0)\n",
-            "Requirement already satisfied: gdown in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (5.1.0)\n",
-            "Requirement already satisfied: tensorflow in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (2.16.1)\n",
-            "Requirement already satisfied: numpy>=1.21.2 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from opencv-python) (1.26.4)\n",
-            "Requirement already satisfied: contourpy>=1.0.1 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from matplotlib) (1.2.1)\n",
-            "Requirement already satisfied: cycler>=0.10 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from matplotlib) (0.12.1)\n",
-            "Requirement already satisfied: fonttools>=4.22.0 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from matplotlib) (4.51.0)\n",
-            "Requirement already satisfied: kiwisolver>=1.3.1 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from matplotlib) (1.4.5)\n",
-            "Requirement already satisfied: packaging>=20.0 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from matplotlib) (24.0)\n",
-            "Requirement already satisfied: pillow>=8 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from matplotlib) (10.3.0)\n",
-            "Requirement already satisfied: pyparsing>=2.3.1 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from matplotlib) (3.1.2)\n",
-            "Requirement already satisfied: python-dateutil>=2.7 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from matplotlib) (2.9.0.post0)\n",
-            "Requirement already satisfied: beautifulsoup4 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from gdown) (4.12.3)\n",
-            "Requirement already satisfied: filelock in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from gdown) (3.13.4)\n",
-            "Requirement already satisfied: requests[socks] in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from gdown) (2.31.0)\n",
-            "Requirement already satisfied: tqdm in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from gdown) (4.66.2)\n",
-            "Requirement already satisfied: tensorflow-intel==2.16.1 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from tensorflow) (2.16.1)\n",
-            "Requirement already satisfied: absl-py>=1.0.0 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from tensorflow-intel==2.16.1->tensorflow) (2.1.0)\n",
-            "Requirement already satisfied: astunparse>=1.6.0 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from tensorflow-intel==2.16.1->tensorflow) (1.6.3)\n",
-            "Requirement already satisfied: flatbuffers>=23.5.26 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from tensorflow-intel==2.16.1->tensorflow) (24.3.25)\n",
-            "Requirement already satisfied: gast!=0.5.0,!=0.5.1,!=0.5.2,>=0.2.1 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from tensorflow-intel==2.16.1->tensorflow) (0.5.4)\n",
-            "Requirement already satisfied: google-pasta>=0.1.1 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from tensorflow-intel==2.16.1->tensorflow) (0.2.0)\n",
-            "Requirement already satisfied: h5py>=3.10.0 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from tensorflow-intel==2.16.1->tensorflow) (3.11.0)\n",
-            "Requirement already satisfied: libclang>=13.0.0 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from tensorflow-intel==2.16.1->tensorflow) (18.1.1)\n",
-            "Requirement already satisfied: ml-dtypes~=0.3.1 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from tensorflow-intel==2.16.1->tensorflow) (0.3.2)\n",
-            "Requirement already satisfied: opt-einsum>=2.3.2 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from tensorflow-intel==2.16.1->tensorflow) (3.3.0)\n",
-            "Requirement already satisfied: protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.20.3 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from tensorflow-intel==2.16.1->tensorflow) (4.25.3)\n",
-            "Requirement already satisfied: setuptools in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from tensorflow-intel==2.16.1->tensorflow) (69.2.0)\n",
-            "Requirement already satisfied: six>=1.12.0 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from tensorflow-intel==2.16.1->tensorflow) (1.16.0)\n",
-            "Requirement already satisfied: termcolor>=1.1.0 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from tensorflow-intel==2.16.1->tensorflow) (2.4.0)\n",
-            "Requirement already satisfied: typing-extensions>=3.6.6 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from tensorflow-intel==2.16.1->tensorflow) (4.11.0)\n",
-            "Requirement already satisfied: wrapt>=1.11.0 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from tensorflow-intel==2.16.1->tensorflow) (1.16.0)\n",
-            "Requirement already satisfied: grpcio<2.0,>=1.24.3 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from tensorflow-intel==2.16.1->tensorflow) (1.62.1)\n",
-            "Requirement already satisfied: tensorboard<2.17,>=2.16 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from tensorflow-intel==2.16.1->tensorflow) (2.16.2)\n",
-            "Collecting keras>=3.0.0 (from tensorflow-intel==2.16.1->tensorflow)\n",
-            "  Using cached keras-3.2.1-py3-none-any.whl.metadata (5.6 kB)\n",
-            "Requirement already satisfied: soupsieve>1.2 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from beautifulsoup4->gdown) (2.5)\n",
-            "Requirement already satisfied: charset-normalizer<4,>=2 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from requests[socks]->gdown) (3.3.2)\n",
-            "Requirement already satisfied: idna<4,>=2.5 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from requests[socks]->gdown) (3.7)\n",
-            "Requirement already satisfied: urllib3<3,>=1.21.1 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from requests[socks]->gdown) (2.2.1)\n",
-            "Requirement already satisfied: certifi>=2017.4.17 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from requests[socks]->gdown) (2024.2.2)\n",
-            "Requirement already satisfied: PySocks!=1.5.7,>=1.5.6 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from requests[socks]->gdown) (1.7.1)\n",
-            "Requirement already satisfied: colorama in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from tqdm->gdown) (0.4.6)\n",
-            "Requirement already satisfied: wheel<1.0,>=0.23.0 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from astunparse>=1.6.0->tensorflow-intel==2.16.1->tensorflow) (0.43.0)\n",
-            "Requirement already satisfied: rich in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from keras>=3.0.0->tensorflow-intel==2.16.1->tensorflow) (13.7.1)\n",
-            "Requirement already satisfied: namex in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from keras>=3.0.0->tensorflow-intel==2.16.1->tensorflow) (0.0.7)\n",
-            "Requirement already satisfied: optree in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from keras>=3.0.0->tensorflow-intel==2.16.1->tensorflow) (0.11.0)\n",
-            "Requirement already satisfied: markdown>=2.6.8 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from tensorboard<2.17,>=2.16->tensorflow-intel==2.16.1->tensorflow) (3.6)\n",
-            "Requirement already satisfied: tensorboard-data-server<0.8.0,>=0.7.0 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from tensorboard<2.17,>=2.16->tensorflow-intel==2.16.1->tensorflow) (0.7.2)\n",
-            "Requirement already satisfied: werkzeug>=1.0.1 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from tensorboard<2.17,>=2.16->tensorflow-intel==2.16.1->tensorflow) (3.0.2)\n",
-            "Requirement already satisfied: MarkupSafe>=2.1.1 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from werkzeug>=1.0.1->tensorboard<2.17,>=2.16->tensorflow-intel==2.16.1->tensorflow) (2.1.5)\n",
-            "Requirement already satisfied: markdown-it-py>=2.2.0 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from rich->keras>=3.0.0->tensorflow-intel==2.16.1->tensorflow) (3.0.0)\n",
-            "Requirement already satisfied: pygments<3.0.0,>=2.13.0 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from rich->keras>=3.0.0->tensorflow-intel==2.16.1->tensorflow) (2.17.2)\n",
-            "Requirement already satisfied: mdurl~=0.1 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from markdown-it-py>=2.2.0->rich->keras>=3.0.0->tensorflow-intel==2.16.1->tensorflow) (0.1.2)\n",
-            "Using cached keras-3.2.1-py3-none-any.whl (1.1 MB)\n",
-            "Installing collected packages: keras\n",
-            "Successfully installed keras-3.2.1\n"
-          ]
-        }
-      ],
-      "source": [
-        "!pip install opencv-python matplotlib imageio gdown tensorflow"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 3,
-      "id": "b24af50c-20b8-409d-ad78-30a933fdd669",
-      "metadata": {
-        "id": "b24af50c-20b8-409d-ad78-30a933fdd669",
-        "tags": []
-      },
-      "outputs": [],
-      "source": [
-        "import os\n",
-        "import cv2\n",
-        "import tensorflow as tf\n",
-        "import numpy as np\n",
-        "from typing import List\n",
-        "from matplotlib import pyplot as plt\n",
-        "import imageio"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 52,
-      "id": "1e3db0b0-e559-4ad6-91fd-e7414b7d75e6",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "1e3db0b0-e559-4ad6-91fd-e7414b7d75e6",
-        "outputId": "c1e41844-c8fd-4efc-eb22-11f3817b0c9f"
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              "[]"
-            ]
-          },
-          "execution_count": 52,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "tf.config.list_physical_devices('GPU')"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 5,
-      "id": "378d045a-3003-4f93-b7d2-a25a97774a68",
-      "metadata": {
-        "id": "378d045a-3003-4f93-b7d2-a25a97774a68",
-        "tags": []
-      },
-      "outputs": [],
-      "source": [
-        "physical_devices = tf.config.list_physical_devices('GPU')\n",
-        "try:\n",
-        "    tf.config.experimental.set_memory_growth(physical_devices[0], True)\n",
-        "except:\n",
-        "    pass"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "7a19e88e-c7b9-45c1-ae1e-f2109329c71b",
-      "metadata": {
-        "id": "7a19e88e-c7b9-45c1-ae1e-f2109329c71b",
-        "tags": []
-      },
-      "source": [
-        "# 1. Build Data Loading Functions"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 6,
-      "id": "8fb99c90-e05a-437f-839d-6e772f8c1dd5",
-      "metadata": {
-        "id": "8fb99c90-e05a-437f-839d-6e772f8c1dd5",
-        "tags": []
-      },
-      "outputs": [],
-      "source": [
-        "import gdown"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 7,
-      "id": "c019e4c6-2af3-4160-99ea-5c8cb009f1a7",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "c019e4c6-2af3-4160-99ea-5c8cb009f1a7",
-        "outputId": "49a7c7ff-ab7d-480e-bcfd-ee652ad89af1",
-        "tags": []
-      },
-      "outputs": [],
-      "source": [
-        "# url = 'https://drive.google.com/uc?id=1YlvpDLix3S-U8fd-gqRwPcWXAXm8JwjL'\n",
-        "# output = 'data.zip'\n",
-        "# gdown.download(url, output, quiet=False)\n",
-        "# gdown.extractall('data.zip')"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 8,
-      "id": "8548cc59-6dfc-4acc-abc3-3e65212db02e",
-      "metadata": {
-        "id": "8548cc59-6dfc-4acc-abc3-3e65212db02e",
-        "tags": []
-      },
-      "outputs": [],
-      "source": [
-        "def load_video(path:str) -> List[float]:\n",
-        "\n",
-        "    cap = cv2.VideoCapture(path)\n",
-        "    frames = []\n",
-        "    for _ in range(int(cap.get(cv2.CAP_PROP_FRAME_COUNT))):\n",
-        "        ret, frame = cap.read()\n",
-        "        frame = tf.image.rgb_to_grayscale(frame)\n",
-        "        frames.append(frame[190:236,80:220,:])\n",
-        "    cap.release()\n",
-        "\n",
-        "    mean = tf.math.reduce_mean(frames)\n",
-        "    std = tf.math.reduce_std(tf.cast(frames, tf.float32))\n",
-        "    return tf.cast((frames - mean), tf.float32) / std"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 9,
-      "id": "ec735e0b-ec98-4eb0-8f49-c35527d6670a",
-      "metadata": {
-        "id": "ec735e0b-ec98-4eb0-8f49-c35527d6670a",
-        "tags": []
-      },
-      "outputs": [],
-      "source": [
-        "vocab = [x for x in \"abcdefghijklmnopqrstuvwxyz'?!123456789 \"]"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 10,
-      "id": "be04e972-d7a5-4a72-82d8-a6bdde1f3ce6",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "be04e972-d7a5-4a72-82d8-a6bdde1f3ce6",
-        "outputId": "01f490ee-dabf-4e94-c6ed-181e8c1ae2c4",
-        "tags": []
-      },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "The vocabulary is: ['', 'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z', \"'\", '?', '!', '1', '2', '3', '4', '5', '6', '7', '8', '9', ' '] (size =40)\n"
-          ]
-        }
-      ],
-      "source": [
-        "char_to_num = tf.keras.layers.StringLookup(vocabulary=vocab, oov_token=\"\")\n",
-        "num_to_char = tf.keras.layers.StringLookup(\n",
-        "    vocabulary=char_to_num.get_vocabulary(), oov_token=\"\", invert=True\n",
-        ")\n",
-        "\n",
-        "print(\n",
-        "    f\"The vocabulary is: {char_to_num.get_vocabulary()} \"\n",
-        "    f\"(size ={char_to_num.vocabulary_size()})\"\n",
-        ")"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 11,
-      "id": "559f7420-6802-45fa-9ca0-b1ff209b461c",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "559f7420-6802-45fa-9ca0-b1ff209b461c",
-        "outputId": "90f0547b-3308-478e-be4a-c89cef1d58e6",
-        "tags": []
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              "['',\n",
-              " 'a',\n",
-              " 'b',\n",
-              " 'c',\n",
-              " 'd',\n",
-              " 'e',\n",
-              " 'f',\n",
-              " 'g',\n",
-              " 'h',\n",
-              " 'i',\n",
-              " 'j',\n",
-              " 'k',\n",
-              " 'l',\n",
-              " 'm',\n",
-              " 'n',\n",
-              " 'o',\n",
-              " 'p',\n",
-              " 'q',\n",
-              " 'r',\n",
-              " 's',\n",
-              " 't',\n",
-              " 'u',\n",
-              " 'v',\n",
-              " 'w',\n",
-              " 'x',\n",
-              " 'y',\n",
-              " 'z',\n",
-              " \"'\",\n",
-              " '?',\n",
-              " '!',\n",
-              " '1',\n",
-              " '2',\n",
-              " '3',\n",
-              " '4',\n",
-              " '5',\n",
-              " '6',\n",
-              " '7',\n",
-              " '8',\n",
-              " '9',\n",
-              " ' ']"
-            ]
-          },
-          "execution_count": 11,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "char_to_num.get_vocabulary()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 12,
-      "id": "797ff78b-b48f-4e14-bb62-8cd0ebf9501a",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "797ff78b-b48f-4e14-bb62-8cd0ebf9501a",
-        "outputId": "cedf213c-95e4-439a-a871-641aec307517",
-        "tags": []
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              ""
-            ]
-          },
-          "execution_count": 12,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "char_to_num(['n','i','c','k'])"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 13,
-      "id": "8cd7f4f4-ae77-4509-a4f4-c723787ebad1",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "8cd7f4f4-ae77-4509-a4f4-c723787ebad1",
-        "outputId": "cb5428be-73ea-47b2-ea8e-4d2c4e3dcb00"
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              ""
-            ]
-          },
-          "execution_count": 13,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "num_to_char([14,  9,  3, 11])"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 14,
-      "id": "9491bab5-6a3c-4f79-879a-8f9fbe73ae2e",
-      "metadata": {
-        "id": "9491bab5-6a3c-4f79-879a-8f9fbe73ae2e",
-        "tags": []
-      },
-      "outputs": [],
-      "source": [
-        "def load_alignments(path:str) -> List[str]:\n",
-        "    with open(path, 'r') as f:\n",
-        "        lines = f.readlines()\n",
-        "    tokens = []\n",
-        "    for line in lines:\n",
-        "        line = line.split()\n",
-        "        if line[2] != 'sil':\n",
-        "            tokens = [*tokens,' ',line[2]]\n",
-        "    return char_to_num(tf.reshape(tf.strings.unicode_split(tokens, input_encoding='UTF-8'), (-1)))[1:]"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 15,
-      "id": "dd01ca9f-77fb-4643-a2aa-47dd82c5d66b",
-      "metadata": {
-        "id": "dd01ca9f-77fb-4643-a2aa-47dd82c5d66b",
-        "tags": []
-      },
-      "outputs": [],
-      "source": [
-        "def load_data(path: str):\n",
-        "    path = bytes.decode(path.numpy())\n",
-        "    # file_name = path.split('/')[-1].split('.')[0]\n",
-        "    # File name splitting for windows\n",
-        "    file_name = path.split('\\\\')[-1].split('.')[0]\n",
-        "    video_path = os.path.join('data','s1',f'{file_name}.mpg')\n",
-        "    alignment_path = os.path.join('data','alignments','s1',f'{file_name}.align')\n",
-        "    frames = load_video(video_path)\n",
-        "    alignments = load_alignments(alignment_path)\n",
-        "\n",
-        "    return frames, alignments"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 16,
-      "id": "8cb7cc58-31ae-4904-a805-1177a82717d2",
-      "metadata": {
-        "id": "8cb7cc58-31ae-4904-a805-1177a82717d2",
-        "tags": []
-      },
-      "outputs": [],
-      "source": [
-        "test_path = '.\\\\data\\\\s1\\\\bbal6n.mpg'\n",
-        "# test_path = './data/s1/bbal6n.mpg'"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 17,
-      "id": "76aa964f-0c84-490d-897a-d00e3966e2c9",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 35
-        },
-        "id": "76aa964f-0c84-490d-897a-d00e3966e2c9",
-        "outputId": "d04d49ad-4eeb-4be1-ffa7-f2655aa86a7a"
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              "'bbal6n'"
-            ]
-          },
-          "execution_count": 17,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "tf.convert_to_tensor(test_path).numpy().decode('utf-8').split('\\\\')[-1].split('.')[0]\n",
-        "# tf.convert_to_tensor(test_path).numpy().decode('utf-8').split('/')[-1].split('.')[0]"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 18,
-      "id": "eb602c71-8560-4f9e-b26b-08202febb937",
-      "metadata": {
-        "id": "eb602c71-8560-4f9e-b26b-08202febb937",
-        "scrolled": true,
-        "tags": []
-      },
-      "outputs": [],
-      "source": [
-        "frames, alignments = load_data(tf.convert_to_tensor(test_path))"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 19,
-      "id": "0e3184a1-6b02-4b4f-84a8-a0a65f951ea2",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 244
-        },
-        "id": "0e3184a1-6b02-4b4f-84a8-a0a65f951ea2",
-        "outputId": "ca65c601-b300-4a41-d8fe-7915a8e5a50b"
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              ""
-            ]
-          },
-          "execution_count": 19,
-          "metadata": {},
-          "output_type": "execute_result"
-        },
-        {
-          "data": {
-            "image/png": 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",
-            "text/plain": [
-              "
"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        }
-      ],
-      "source": [
-        "plt.imshow(frames[40])"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 20,
-      "id": "d7ec0833-d54b-4073-84cf-92d011c60ec1",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "d7ec0833-d54b-4073-84cf-92d011c60ec1",
-        "outputId": "02abd7a1-113b-42b1-a6ba-9a458373b112"
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              ""
-            ]
-          },
-          "execution_count": 20,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "alignments"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 21,
-      "id": "fe1ad370-b287-4b46-85a2-7c45b0bd9b10",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "fe1ad370-b287-4b46-85a2-7c45b0bd9b10",
-        "outputId": "8910d788-545b-4e76-b154-4a90d8e63927"
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              ""
-            ]
-          },
-          "execution_count": 21,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "tf.strings.reduce_join([bytes.decode(x) for x in num_to_char(alignments.numpy()).numpy()])"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 22,
-      "id": "6871031a-b0ba-4c76-a852-f6329b0f2606",
-      "metadata": {
-        "id": "6871031a-b0ba-4c76-a852-f6329b0f2606",
-        "tags": []
-      },
-      "outputs": [],
-      "source": [
-        "def mappable_function(path:str) ->List[str]:\n",
-        "    result = tf.py_function(load_data, [path], (tf.float32, tf.int64))\n",
-        "    return result"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "c40a7eb4-0c3e-4eab-9291-5611cb68ce08",
-      "metadata": {
-        "id": "c40a7eb4-0c3e-4eab-9291-5611cb68ce08",
-        "tags": []
-      },
-      "source": [
-        "# 2. Create Data Pipeline"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 23,
-      "id": "7686355d-45aa-4c85-ad9c-053e6a9b4d81",
-      "metadata": {
-        "id": "7686355d-45aa-4c85-ad9c-053e6a9b4d81",
-        "tags": []
-      },
-      "outputs": [],
-      "source": [
-        "from matplotlib import pyplot as plt"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 24,
-      "id": "f066fea2-91b1-42ed-a67d-00566a1a53ff",
-      "metadata": {
-        "id": "f066fea2-91b1-42ed-a67d-00566a1a53ff",
-        "tags": []
-      },
-      "outputs": [],
-      "source": [
-        "data = tf.data.Dataset.list_files('.\\\\data\\\\s1\\\\*.mpg')\n",
-        "data = data.shuffle(500, reshuffle_each_iteration=False)\n",
-        "data = data.map(mappable_function)\n",
-        "data = data.padded_batch(2, padded_shapes=([75,None,None,None],[40]))\n",
-        "data = data.prefetch(tf.data.AUTOTUNE)\n",
-        "# Added for split\n",
-        "train = data.take(450)\n",
-        "test = data.skip(450)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 25,
-      "id": "4de3b276",
-      "metadata": {},
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              "(450, 50)"
-            ]
-          },
-          "execution_count": 25,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "len(train), len(test)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 26,
-      "id": "5281bde8-fdc8-4da1-bd55-5a7929a9e80c",
-      "metadata": {
-        "id": "5281bde8-fdc8-4da1-bd55-5a7929a9e80c"
-      },
-      "outputs": [],
-      "source": [
-        "frames, alignments = data.as_numpy_iterator().next()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 27,
-      "id": "cbebe683-6afd-47fd-bba4-c83b4b13bb32",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "cbebe683-6afd-47fd-bba4-c83b4b13bb32",
-        "outputId": "8924019e-28cd-4bfa-9e1e-bb836d71e6f8"
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              "2"
-            ]
-          },
-          "execution_count": 27,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "len(frames)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 28,
-      "id": "5cf2d676-93a9-434c-b3c7-bdcc2577b2e7",
-      "metadata": {
-        "id": "5cf2d676-93a9-434c-b3c7-bdcc2577b2e7",
-        "tags": []
-      },
-      "outputs": [],
-      "source": [
-        "sample = data.as_numpy_iterator()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 29,
-      "id": "efa6cd46-7079-46c0-b45b-832f339f6cb0",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "efa6cd46-7079-46c0-b45b-832f339f6cb0",
-        "outputId": "c24ecd06-8d12-4531-df65-27a9f77acada",
-        "scrolled": true,
-        "tags": []
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              "array([[[[[ 1.2631532 ],\n",
-              "          [ 1.3815738 ],\n",
-              "          [ 1.1842061 ],\n",
-              "          ...,\n",
-              "          [ 0.3157883 ],\n",
-              "          [ 0.27631477],\n",
-              "          [ 0.27631477]],\n",
-              "\n",
-              "         [[ 1.3421003 ],\n",
-              "          [ 1.4210473 ],\n",
-              "          [ 1.1842061 ],\n",
-              "          ...,\n",
-              "          [ 0.3157883 ],\n",
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-            ]
-          },
-          "execution_count": 29,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "val = sample.next(); val[0]"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 30,
-      "id": "acf5eb4f-a0da-4a9a-bf24-af13e9cc2fbe",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "acf5eb4f-a0da-4a9a-bf24-af13e9cc2fbe",
-        "outputId": "2b7ce709-6400-402c-ab9d-0e4c5739cafc",
-        "tags": []
-      },
-      "outputs": [
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
-            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n"
-          ]
-        }
-      ],
-      "source": [
-        "imageio.mimsave('.\\\\animation.gif', val[0][0], fps=10)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 31,
-      "id": "c33a87a2-d5e0-4ec9-b174-73ebf41bf03a",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 244
-        },
-        "id": "c33a87a2-d5e0-4ec9-b174-73ebf41bf03a",
-        "outputId": "1b7cc028-1990-4714-8d7e-fda85218c0d5",
-        "tags": []
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              ""
-            ]
-          },
-          "execution_count": 31,
-          "metadata": {},
-          "output_type": "execute_result"
-        },
-        {
-          "data": {
-            "image/png": 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",
-            "text/plain": [
-              "
"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        }
-      ],
-      "source": [
-        "# 0:videos, 0: 1st video out of the batch,  0: return the first frame in the video\n",
-        "plt.imshow(val[0][0][35])"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 32,
-      "id": "84593332-133c-4205-b7a6-8e235d5e2b3b",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "84593332-133c-4205-b7a6-8e235d5e2b3b",
-        "outputId": "69f24afc-2cb9-4d07-9d93-3b4e1fde5046",
-        "tags": []
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              ""
-            ]
-          },
-          "execution_count": 32,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "tf.strings.reduce_join([num_to_char(word) for word in val[1][0]])"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "0f47733c-83bc-465c-b118-b198b492ad37",
-      "metadata": {
-        "id": "0f47733c-83bc-465c-b118-b198b492ad37",
-        "tags": []
-      },
-      "source": [
-        "# 3. Design the Deep Neural Network"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 33,
-      "id": "d8e9a497-191b-4842-afbd-26f5e13c43ba",
-      "metadata": {
-        "id": "d8e9a497-191b-4842-afbd-26f5e13c43ba",
-        "tags": []
-      },
-      "outputs": [],
-      "source": [
-        "import tensorflow\n",
-        "from tensorflow import keras\n",
-        "\n",
-        "from keras.models import Sequential\n",
-        "from keras.layers import Conv3D, LSTM, Dense, Dropout, Bidirectional, MaxPool3D, Activation, Reshape, SpatialDropout3D, BatchNormalization, TimeDistributed, Flatten\n",
-        "from keras.optimizers import Adam\n",
-        "from keras.callbacks import ModelCheckpoint, LearningRateScheduler\n",
-        "from keras.layers import LSTM, Bidirectional\n",
-        "from keras.initializers import Orthogonal\n",
-        "from keras.layers import Lambda\n",
-        "from keras.layers import Reshape\n",
-        "import tensorflow as tf"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 34,
-      "id": "3f753ed2-70b9-4236-8c1c-08ca065dc8bf",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "3f753ed2-70b9-4236-8c1c-08ca065dc8bf",
-        "outputId": "b06442c3-cc68-4c88-d5e8-c9a915fc8b96"
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              "(75, 46, 140, 1)"
-            ]
-          },
-          "execution_count": 34,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "data.as_numpy_iterator().next()[0][0].shape"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 35,
-      "id": "f9171056-a352-491a-9ed9-92b28ced268e",
-      "metadata": {
-        "id": "f9171056-a352-491a-9ed9-92b28ced268e",
-        "tags": []
-      },
-      "outputs": [
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            "c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\layers\\convolutional\\base_conv.py:99: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
-            "  super().__init__(\n"
-          ]
-        }
-      ],
-      "source": [
-        "model = Sequential()\n",
-        "model.add(Conv3D(128, 3, input_shape=(75,46,140,1), padding='same'))\n",
-        "model.add(Activation('relu'))\n",
-        "model.add(MaxPool3D((1,2,2)))\n",
-        "\n",
-        "model.add(Conv3D(256, 3, padding='same'))\n",
-        "model.add(Activation('relu'))\n",
-        "model.add(MaxPool3D((1,2,2)))\n",
-        "\n",
-        "model.add(Conv3D(75, 3, padding='same'))\n",
-        "model.add(Activation('relu'))\n",
-        "model.add(MaxPool3D((1,2,2)))\n",
-        "\n",
-        "# model.add(Lambda(lambda x: tf.print(\"Shape before TimeDistributed:\", tf.shape(x))))\n",
-        "\n",
-        "model.add(TimeDistributed(Flatten()))\n",
-        "\n",
-        "# model.add(Reshape((-1, 75*5*17)))\n",
-        "\n",
-        "# model.add(Bidirectional(LSTM(128, return_sequences=True)))\n",
-        "model.add(Bidirectional(LSTM(128, return_sequences=True)))\n",
-        "\n",
-        "# # model.add(Bidirectional(LSTM(128, kernel_initializer='Orthogonal', return_sequences=True)))\n",
-        "model.add(Bidirectional(LSTM(128, kernel_initializer=Orthogonal(), return_sequences=True)))\n",
-        "model.add(Dropout(.5))\n",
-        "\n",
-        "# # model.add(Bidirectional(LSTM(128, kernel_initializer='Orthogonal', return_sequences=True)))\n",
-        "model.add(Bidirectional(LSTM(128, kernel_initializer=Orthogonal(), return_sequences=True)))\n",
-        "model.add(Dropout(.5))\n",
-        "\n",
-        "model.add(Dense(char_to_num.vocabulary_size()+1, kernel_initializer='he_normal', activation='softmax'))"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 36,
-      "id": "ab905c18",
-      "metadata": {},
-      "outputs": [
-        {
-          "data": {
-            "text/html": [
-              "
Model: \"sequential\"\n",
-              "
\n"
-            ],
-            "text/plain": [
-              "\u001b[1mModel: \"sequential\"\u001b[0m\n"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        },
-        {
-          "data": {
-            "text/html": [
-              "
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
-              "┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃\n",
-              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
-              "│ conv3d (Conv3D)                 │ (None, 75, 46, 140,    │         3,584 │\n",
-              "│                                 │ 128)                   │               │\n",
-              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-              "│ activation (Activation)         │ (None, 75, 46, 140,    │             0 │\n",
-              "│                                 │ 128)                   │               │\n",
-              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-              "│ max_pooling3d (MaxPooling3D)    │ (None, 75, 23, 70,     │             0 │\n",
-              "│                                 │ 128)                   │               │\n",
-              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-              "│ conv3d_1 (Conv3D)               │ (None, 75, 23, 70,     │       884,992 │\n",
-              "│                                 │ 256)                   │               │\n",
-              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-              "│ activation_1 (Activation)       │ (None, 75, 23, 70,     │             0 │\n",
-              "│                                 │ 256)                   │               │\n",
-              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-              "│ max_pooling3d_1 (MaxPooling3D)  │ (None, 75, 11, 35,     │             0 │\n",
-              "│                                 │ 256)                   │               │\n",
-              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-              "│ conv3d_2 (Conv3D)               │ (None, 75, 11, 35, 75) │       518,475 │\n",
-              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-              "│ activation_2 (Activation)       │ (None, 75, 11, 35, 75) │             0 │\n",
-              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-              "│ max_pooling3d_2 (MaxPooling3D)  │ (None, 75, 5, 17, 75)  │             0 │\n",
-              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-              "│ time_distributed                │ (None, 75, 6375)       │             0 │\n",
-              "│ (TimeDistributed)               │                        │               │\n",
-              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-              "│ bidirectional (Bidirectional)   │ (None, 75, 256)        │     6,660,096 │\n",
-              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-              "│ bidirectional_1 (Bidirectional) │ (None, 75, 256)        │       394,240 │\n",
-              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-              "│ dropout (Dropout)               │ (None, 75, 256)        │             0 │\n",
-              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-              "│ bidirectional_2 (Bidirectional) │ (None, 75, 256)        │       394,240 │\n",
-              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-              "│ dropout_1 (Dropout)             │ (None, 75, 256)        │             0 │\n",
-              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-              "│ dense (Dense)                   │ (None, 75, 41)         │        10,537 │\n",
-              "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
-              "
\n"
-            ],
-            "text/plain": [
-              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
-              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
-              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
-              "│ conv3d (\u001b[38;5;33mConv3D\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m46\u001b[0m, \u001b[38;5;34m140\u001b[0m,    │         \u001b[38;5;34m3,584\u001b[0m │\n",
-              "│                                 │ \u001b[38;5;34m128\u001b[0m)                   │               │\n",
-              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-              "│ activation (\u001b[38;5;33mActivation\u001b[0m)         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m46\u001b[0m, \u001b[38;5;34m140\u001b[0m,    │             \u001b[38;5;34m0\u001b[0m │\n",
-              "│                                 │ \u001b[38;5;34m128\u001b[0m)                   │               │\n",
-              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-              "│ max_pooling3d (\u001b[38;5;33mMaxPooling3D\u001b[0m)    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m23\u001b[0m, \u001b[38;5;34m70\u001b[0m,     │             \u001b[38;5;34m0\u001b[0m │\n",
-              "│                                 │ \u001b[38;5;34m128\u001b[0m)                   │               │\n",
-              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-              "│ conv3d_1 (\u001b[38;5;33mConv3D\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m23\u001b[0m, \u001b[38;5;34m70\u001b[0m,     │       \u001b[38;5;34m884,992\u001b[0m │\n",
-              "│                                 │ \u001b[38;5;34m256\u001b[0m)                   │               │\n",
-              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-              "│ activation_1 (\u001b[38;5;33mActivation\u001b[0m)       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m23\u001b[0m, \u001b[38;5;34m70\u001b[0m,     │             \u001b[38;5;34m0\u001b[0m │\n",
-              "│                                 │ \u001b[38;5;34m256\u001b[0m)                   │               │\n",
-              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-              "│ max_pooling3d_1 (\u001b[38;5;33mMaxPooling3D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m11\u001b[0m, \u001b[38;5;34m35\u001b[0m,     │             \u001b[38;5;34m0\u001b[0m │\n",
-              "│                                 │ \u001b[38;5;34m256\u001b[0m)                   │               │\n",
-              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-              "│ conv3d_2 (\u001b[38;5;33mConv3D\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m11\u001b[0m, \u001b[38;5;34m35\u001b[0m, \u001b[38;5;34m75\u001b[0m) │       \u001b[38;5;34m518,475\u001b[0m │\n",
-              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-              "│ activation_2 (\u001b[38;5;33mActivation\u001b[0m)       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m11\u001b[0m, \u001b[38;5;34m35\u001b[0m, \u001b[38;5;34m75\u001b[0m) │             \u001b[38;5;34m0\u001b[0m │\n",
-              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-              "│ max_pooling3d_2 (\u001b[38;5;33mMaxPooling3D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m17\u001b[0m, \u001b[38;5;34m75\u001b[0m)  │             \u001b[38;5;34m0\u001b[0m │\n",
-              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-              "│ time_distributed                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m6375\u001b[0m)       │             \u001b[38;5;34m0\u001b[0m │\n",
-              "│ (\u001b[38;5;33mTimeDistributed\u001b[0m)               │                        │               │\n",
-              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-              "│ bidirectional (\u001b[38;5;33mBidirectional\u001b[0m)   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m256\u001b[0m)        │     \u001b[38;5;34m6,660,096\u001b[0m │\n",
-              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-              "│ bidirectional_1 (\u001b[38;5;33mBidirectional\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m256\u001b[0m)        │       \u001b[38;5;34m394,240\u001b[0m │\n",
-              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-              "│ dropout (\u001b[38;5;33mDropout\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m256\u001b[0m)        │             \u001b[38;5;34m0\u001b[0m │\n",
-              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-              "│ bidirectional_2 (\u001b[38;5;33mBidirectional\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m256\u001b[0m)        │       \u001b[38;5;34m394,240\u001b[0m │\n",
-              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-              "│ dropout_1 (\u001b[38;5;33mDropout\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m256\u001b[0m)        │             \u001b[38;5;34m0\u001b[0m │\n",
-              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-              "│ dense (\u001b[38;5;33mDense\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m41\u001b[0m)         │        \u001b[38;5;34m10,537\u001b[0m │\n",
-              "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        },
-        {
-          "data": {
-            "text/html": [
-              "
 Total params: 8,866,164 (33.82 MB)\n",
-              "
\n"
-            ],
-            "text/plain": [
-              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m8,866,164\u001b[0m (33.82 MB)\n"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        },
-        {
-          "data": {
-            "text/html": [
-              "
 Trainable params: 8,866,164 (33.82 MB)\n",
-              "
\n"
-            ],
-            "text/plain": [
-              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m8,866,164\u001b[0m (33.82 MB)\n"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        },
-        {
-          "data": {
-            "text/html": [
-              "
 Non-trainable params: 0 (0.00 B)\n",
-              "
\n"
-            ],
-            "text/plain": [
-              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        }
-      ],
-      "source": [
-        "model.summary()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 37,
-      "id": "fae71bed",
-      "metadata": {},
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              "6375"
-            ]
-          },
-          "execution_count": 37,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "5*17*75"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 38,
-      "id": "7a5815ab",
-      "metadata": {},
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 3s/step\n"
-          ]
-        }
-      ],
-      "source": [
-        "yhat = model.predict(val[0])"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 39,
-      "id": "65eccd93",
-      "metadata": {},
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              ""
-            ]
-          },
-          "execution_count": 39,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "tf.strings.reduce_join([num_to_char(x) for x in tf.argmax(yhat[0],axis=1)])"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 40,
-      "id": "6ed47531-8317-4255-9a12-b757642258e6",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "6ed47531-8317-4255-9a12-b757642258e6",
-        "outputId": "5b755432-4136-4834-fd69-78aace01b57b",
-        "tags": []
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              ""
-            ]
-          },
-          "execution_count": 40,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "tf.strings.reduce_join([num_to_char(tf.argmax(x)) for x in yhat[0]])"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 41,
-      "id": "7c37b9b9-5298-4038-9c33-5031d1b457f0",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "7c37b9b9-5298-4038-9c33-5031d1b457f0",
-        "outputId": "fcdf9c94-35db-4aa0-fcf7-3029f32eac97",
-        "tags": []
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              "(None, 75, 46, 140, 1)"
-            ]
-          },
-          "execution_count": 41,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "model.input_shape"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 42,
-      "id": "98b316a4-5322-4782-8e36-4b3c1a696d85",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "98b316a4-5322-4782-8e36-4b3c1a696d85",
-        "outputId": "5f9ebad7-b4cc-4ac8-c5e6-f5471120f50f",
-        "tags": []
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              "(None, 75, 41)"
-            ]
-          },
-          "execution_count": 42,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "model.output_shape"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "2ec02176-5c26-46c3-aff7-8352e6563c7d",
-      "metadata": {
-        "id": "2ec02176-5c26-46c3-aff7-8352e6563c7d",
-        "tags": []
-      },
-      "source": [
-        "# 4. Setup Training Options and Train"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 43,
-      "id": "ab015fd0-7fb4-4d5d-9fa2-30a05dbd515a",
-      "metadata": {
-        "id": "ab015fd0-7fb4-4d5d-9fa2-30a05dbd515a",
-        "tags": []
-      },
-      "outputs": [],
-      "source": [
-        "def scheduler(epoch, lr):\n",
-        "    if epoch < 30:\n",
-        "        return lr\n",
-        "    else:\n",
-        "        return lr * tf.math.exp(-0.1)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 44,
-      "id": "c564d5c9-db54-4e88-b311-9aeab7fb3e69",
-      "metadata": {
-        "id": "c564d5c9-db54-4e88-b311-9aeab7fb3e69",
-        "tags": []
-      },
-      "outputs": [],
-      "source": [
-        "def CTCLoss(y_true, y_pred):\n",
-        "    batch_len = tf.cast(tf.shape(y_true)[0], dtype=\"int64\")\n",
-        "    input_length = tf.cast(tf.shape(y_pred)[1], dtype=\"int64\")\n",
-        "    label_length = tf.cast(tf.shape(y_true)[1], dtype=\"int64\")\n",
-        "\n",
-        "    input_length = input_length * tf.ones(shape=(batch_len, 1), dtype=\"int64\")\n",
-        "    label_length = label_length * tf.ones(shape=(batch_len, 1), dtype=\"int64\")\n",
-        "\n",
-        "    loss = tf.keras.backend.ctc_batch_cost(y_true, y_pred, input_length, label_length)\n",
-        "    return loss"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 45,
-      "id": "a26dc3fc-a19c-4378-bd8c-e2b597a1d15c",
-      "metadata": {
-        "id": "a26dc3fc-a19c-4378-bd8c-e2b597a1d15c",
-        "tags": []
-      },
-      "outputs": [],
-      "source": [
-        "class ProduceExample(tf.keras.callbacks.Callback):\n",
-        "    def __init__(self, dataset) -> None:\n",
-        "        self.dataset = dataset.as_numpy_iterator()\n",
-        "\n",
-        "    def on_epoch_end(self, epoch, logs=None) -> None:\n",
-        "        data = self.dataset.next()\n",
-        "        yhat = self.model.predict(data[0])\n",
-        "        decoded = tf.keras.backend.ctc_decode(yhat, [75,75], greedy=False)[0][0].numpy()\n",
-        "        for x in range(len(yhat)):\n",
-        "            print('Original:', tf.strings.reduce_join(num_to_char(data[1][x])).numpy().decode('utf-8'))\n",
-        "            print('Prediction:', tf.strings.reduce_join(num_to_char(decoded[x])).numpy().decode('utf-8'))\n",
-        "            print('~'*100)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 46,
-      "id": "04be90d8-2482-46f9-b513-d5f4f8001c7e",
-      "metadata": {
-        "id": "04be90d8-2482-46f9-b513-d5f4f8001c7e",
-        "tags": []
-      },
-      "outputs": [],
-      "source": [
-        "model.compile(optimizer=Adam(learning_rate=0.0001), loss=CTCLoss)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 47,
-      "id": "eab49367-3f1e-4464-ae76-dbd07549d97e",
-      "metadata": {
-        "id": "eab49367-3f1e-4464-ae76-dbd07549d97e",
-        "tags": []
-      },
-      "outputs": [],
-      "source": [
-        "# Adjust the file path to end with '.weights.h5'\n",
-        "checkpoint_path = os.path.join('models', 'overlapped-weights368.weights.h5')\n",
-        "\n",
-        "# Create the ModelCheckpoint callback\n",
-        "checkpoint_callback = ModelCheckpoint(\n",
-        "    filepath=checkpoint_path,\n",
-        "    monitor='loss',\n",
-        "    save_weights_only=True\n",
-        ")\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 48,
-      "id": "e085a632-d464-46ef-8777-959cad4adb2c",
-      "metadata": {
-        "id": "e085a632-d464-46ef-8777-959cad4adb2c",
-        "tags": []
-      },
-      "outputs": [],
-      "source": [
-        "schedule_callback = LearningRateScheduler(scheduler)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 49,
-      "id": "48eca991-90ab-4592-8a79-b50e9ca015b6",
-      "metadata": {
-        "id": "48eca991-90ab-4592-8a79-b50e9ca015b6",
-        "tags": []
-      },
-      "outputs": [],
-      "source": [
-        "example_callback = ProduceExample(test)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 50,
-      "id": "8ffba483-aa61-4bbe-a15f-a73e1ddf097c",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "8ffba483-aa61-4bbe-a15f-a73e1ddf097c",
-        "outputId": "5ac8771e-cd3b-4d4d-eaf5-15add3c1e8ae",
-        "tags": []
-      },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Epoch 1/100\n",
-            "WARNING:tensorflow:From c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\legacy\\backend.py:666: The name tf.nn.ctc_loss is deprecated. Please use tf.compat.v1.nn.ctc_loss instead.\n",
-            "\n"
-          ]
-        },
-        {
-          "ename": "InvalidArgumentError",
-          "evalue": "Graph execution error:\n\nDetected at node sequential_1/time_distributed_1/Reshape_17 defined at (most recent call last):\n  File \"\", line 198, in _run_module_as_main\n\n  File \"\", line 88, in _run_code\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel_launcher.py\", line 18, in \n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\traitlets\\config\\application.py\", line 1075, in launch_instance\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel\\kernelapp.py\", line 739, in start\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\tornado\\platform\\asyncio.py\", line 205, in start\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\asyncio\\base_events.py\", line 641, in run_forever\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\asyncio\\base_events.py\", line 1987, in _run_once\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\asyncio\\events.py\", line 88, in _run\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 545, in dispatch_queue\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 534, in process_one\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 437, in dispatch_shell\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel\\ipkernel.py\", line 362, in execute_request\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 778, in execute_request\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel\\ipkernel.py\", line 449, in do_execute\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel\\zmqshell.py\", line 549, in run_cell\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3075, in run_cell\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3130, in _run_cell\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\async_helpers.py\", line 129, in _pseudo_sync_runner\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3334, in run_cell_async\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3517, in run_ast_nodes\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3577, in run_code\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Temp\\ipykernel_11284\\3536403359.py\", line 1, in \n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 117, in error_handler\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\backend\\tensorflow\\trainer.py\", line 329, in fit\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\backend\\tensorflow\\trainer.py\", line 122, in one_step_on_iterator\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\backend\\tensorflow\\trainer.py\", line 110, in one_step_on_data\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\backend\\tensorflow\\trainer.py\", line 57, in train_step\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 117, in error_handler\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\layers\\layer.py\", line 826, in __call__\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 117, in error_handler\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\ops\\operation.py\", line 48, in __call__\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 156, in error_handler\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\models\\sequential.py\", line 206, in call\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\models\\functional.py\", line 199, in call\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\ops\\function.py\", line 151, in _run_through_graph\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\models\\functional.py\", line 583, in call\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 117, in error_handler\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\layers\\layer.py\", line 826, in __call__\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 117, in error_handler\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\ops\\operation.py\", line 48, in __call__\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 156, in error_handler\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\layers\\rnn\\time_distributed.py\", line 108, in call\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\layers\\rnn\\time_distributed.py\", line 102, in step_function\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\layers\\reshaping\\flatten.py\", line 54, in call\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\ops\\numpy.py\", line 4527, in reshape\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\backend\\tensorflow\\numpy.py\", line 1618, in reshape\n\nOnly one input size may be -1, not both 0 and 1\n\t [[{{node sequential_1/time_distributed_1/Reshape_17}}]] [Op:__inference_one_step_on_iterator_34683]",
-          "output_type": "error",
-          "traceback": [
-            "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-            "\u001b[1;31mInvalidArgumentError\u001b[0m                      Traceback (most recent call last)",
-            "Cell \u001b[1;32mIn[50], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtrain\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalidation_data\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtest\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mepochs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m100\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcallbacks\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[43mcheckpoint_callback\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mschedule_callback\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mexample_callback\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n",
-            "File \u001b[1;32mc:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\utils\\traceback_utils.py:122\u001b[0m, in \u001b[0;36mfilter_traceback..error_handler\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    119\u001b[0m     filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n\u001b[0;32m    120\u001b[0m     \u001b[38;5;66;03m# To get the full stack trace, call:\u001b[39;00m\n\u001b[0;32m    121\u001b[0m     \u001b[38;5;66;03m# `keras.config.disable_traceback_filtering()`\u001b[39;00m\n\u001b[1;32m--> 122\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m e\u001b[38;5;241m.\u001b[39mwith_traceback(filtered_tb) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m    123\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[0;32m    124\u001b[0m     \u001b[38;5;28;01mdel\u001b[39;00m filtered_tb\n",
-            "File \u001b[1;32mc:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\tensorflow\\python\\eager\\execute.py:53\u001b[0m, in \u001b[0;36mquick_execute\u001b[1;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[0;32m     51\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m     52\u001b[0m   ctx\u001b[38;5;241m.\u001b[39mensure_initialized()\n\u001b[1;32m---> 53\u001b[0m   tensors \u001b[38;5;241m=\u001b[39m pywrap_tfe\u001b[38;5;241m.\u001b[39mTFE_Py_Execute(ctx\u001b[38;5;241m.\u001b[39m_handle, device_name, op_name,\n\u001b[0;32m     54\u001b[0m                                       inputs, attrs, num_outputs)\n\u001b[0;32m     55\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m core\u001b[38;5;241m.\u001b[39m_NotOkStatusException \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m     56\u001b[0m   \u001b[38;5;28;01mif\u001b[39;00m name \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
-            "\u001b[1;31mInvalidArgumentError\u001b[0m: Graph execution error:\n\nDetected at node sequential_1/time_distributed_1/Reshape_17 defined at (most recent call last):\n  File \"\", line 198, in _run_module_as_main\n\n  File \"\", line 88, in _run_code\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel_launcher.py\", line 18, in \n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\traitlets\\config\\application.py\", line 1075, in launch_instance\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel\\kernelapp.py\", line 739, in start\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\tornado\\platform\\asyncio.py\", line 205, in start\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\asyncio\\base_events.py\", line 641, in run_forever\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\asyncio\\base_events.py\", line 1987, in _run_once\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\asyncio\\events.py\", line 88, in _run\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 545, in dispatch_queue\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 534, in process_one\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 437, in dispatch_shell\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel\\ipkernel.py\", line 362, in execute_request\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 778, in execute_request\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel\\ipkernel.py\", line 449, in do_execute\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel\\zmqshell.py\", line 549, in run_cell\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3075, in run_cell\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3130, in _run_cell\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\async_helpers.py\", line 129, in _pseudo_sync_runner\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3334, in run_cell_async\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3517, in run_ast_nodes\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3577, in run_code\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Temp\\ipykernel_11284\\3536403359.py\", line 1, in \n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 117, in error_handler\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\backend\\tensorflow\\trainer.py\", line 329, in fit\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\backend\\tensorflow\\trainer.py\", line 122, in one_step_on_iterator\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\backend\\tensorflow\\trainer.py\", line 110, in one_step_on_data\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\backend\\tensorflow\\trainer.py\", line 57, in train_step\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 117, in error_handler\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\layers\\layer.py\", line 826, in __call__\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 117, in error_handler\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\ops\\operation.py\", line 48, in __call__\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 156, in error_handler\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\models\\sequential.py\", line 206, in call\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\models\\functional.py\", line 199, in call\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\ops\\function.py\", line 151, in _run_through_graph\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\models\\functional.py\", line 583, in call\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 117, in error_handler\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\layers\\layer.py\", line 826, in __call__\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 117, in error_handler\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\ops\\operation.py\", line 48, in __call__\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 156, in error_handler\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\layers\\rnn\\time_distributed.py\", line 108, in call\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\layers\\rnn\\time_distributed.py\", line 102, in step_function\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\layers\\reshaping\\flatten.py\", line 54, in call\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\ops\\numpy.py\", line 4527, in reshape\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\backend\\tensorflow\\numpy.py\", line 1618, in reshape\n\nOnly one input size may be -1, not both 0 and 1\n\t [[{{node sequential_1/time_distributed_1/Reshape_17}}]] [Op:__inference_one_step_on_iterator_34683]"
-          ]
-        }
-      ],
-      "source": [
-        "model.fit(train, validation_data=test, epochs=100, callbacks=[checkpoint_callback, schedule_callback, example_callback])"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "fa8ee94b-89f7-4733-8a0c-a86f86ff590a",
-      "metadata": {
-        "id": "fa8ee94b-89f7-4733-8a0c-a86f86ff590a",
-        "tags": []
-      },
-      "source": [
-        "# 5. Make a Prediction"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "id": "01fa7204-ce0e-49a8-8dbd-14fe5dfead40",
-      "metadata": {
-        "id": "01fa7204-ce0e-49a8-8dbd-14fe5dfead40",
-        "tags": []
-      },
-      "outputs": [],
-      "source": [
-        "# url = 'https://drive.google.com/uc?id=1vWscXs4Vt0a_1IH1-ct2TCgXAZT-N3_Y'\n",
-        "# output = 'checkpoints.zip'\n",
-        "# gdown.download(url, output, quiet=False)\n",
-        "# gdown.extractall('checkpoints.zip', 'models')"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 51,
-      "id": "247f664d-3c87-4e96-946e-930dad0e1c2c",
-      "metadata": {
-        "id": "247f664d-3c87-4e96-946e-930dad0e1c2c",
-        "tags": []
-      },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "models\\unseen-weights178.h5\n"
-          ]
-        },
-        {
-          "ename": "ValueError",
-          "evalue": "Layer count mismatch when loading weights from file. Model expected 7 layers, found 9 saved layers.",
-          "output_type": "error",
-          "traceback": [
-            "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-            "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
-            "Cell \u001b[1;32mIn[51], line 3\u001b[0m\n\u001b[0;32m      1\u001b[0m checkpoint_path \u001b[38;5;241m=\u001b[39m os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mjoin(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmodels\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124munseen-weights178.h5\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m      2\u001b[0m \u001b[38;5;28mprint\u001b[39m(checkpoint_path)\n\u001b[1;32m----> 3\u001b[0m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mload_weights\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcheckpoint_path\u001b[49m\u001b[43m)\u001b[49m\n",
-            "File \u001b[1;32mc:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\utils\\traceback_utils.py:122\u001b[0m, in \u001b[0;36mfilter_traceback..error_handler\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    119\u001b[0m     filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n\u001b[0;32m    120\u001b[0m     \u001b[38;5;66;03m# To get the full stack trace, call:\u001b[39;00m\n\u001b[0;32m    121\u001b[0m     \u001b[38;5;66;03m# `keras.config.disable_traceback_filtering()`\u001b[39;00m\n\u001b[1;32m--> 122\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m e\u001b[38;5;241m.\u001b[39mwith_traceback(filtered_tb) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m    123\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[0;32m    124\u001b[0m     \u001b[38;5;28;01mdel\u001b[39;00m filtered_tb\n",
-            "File \u001b[1;32mc:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\legacy\\saving\\legacy_h5_format.py:357\u001b[0m, in \u001b[0;36mload_weights_from_hdf5_group\u001b[1;34m(f, model)\u001b[0m\n\u001b[0;32m    355\u001b[0m layer_names \u001b[38;5;241m=\u001b[39m filtered_layer_names\n\u001b[0;32m    356\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(layer_names) \u001b[38;5;241m!=\u001b[39m \u001b[38;5;28mlen\u001b[39m(filtered_layers):\n\u001b[1;32m--> 357\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m    358\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mLayer count mismatch when loading weights from file. \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    359\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mModel expected \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlen\u001b[39m(filtered_layers)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m layers, found \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    360\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlen\u001b[39m(layer_names)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m saved layers.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    361\u001b[0m     )\n\u001b[0;32m    363\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m k, name \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(layer_names):\n\u001b[0;32m    364\u001b[0m     g \u001b[38;5;241m=\u001b[39m f[name]\n",
-            "\u001b[1;31mValueError\u001b[0m: Layer count mismatch when loading weights from file. Model expected 7 layers, found 9 saved layers."
-          ]
-        }
-      ],
-      "source": [
-        "checkpoint_path = os.path.join('models', 'unseen-weights178.h5')\n",
-        "print(checkpoint_path)\n",
-        "model.load_weights(checkpoint_path)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "id": "7f8d689f-b7bb-443c-9b88-e40c1d800828",
-      "metadata": {
-        "id": "7f8d689f-b7bb-443c-9b88-e40c1d800828",
-        "tags": []
-      },
-      "outputs": [],
-      "source": [
-        "test_data = test.as_numpy_iterator()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "id": "38546dc2-bee9-4837-864b-8a884df40ad7",
-      "metadata": {
-        "id": "38546dc2-bee9-4837-864b-8a884df40ad7",
-        "tags": []
-      },
-      "outputs": [],
-      "source": [
-        "sample = test_data.next()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "id": "a43621f0-229d-4c0d-9554-9c3a3da9c61a",
-      "metadata": {
-        "id": "a43621f0-229d-4c0d-9554-9c3a3da9c61a",
-        "tags": []
-      },
-      "outputs": [],
-      "source": [
-        "yhat = model.predict(sample[0])"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "id": "ea462999-f87e-4a7e-a057-5be7b6d8f7d5",
-      "metadata": {
-        "id": "ea462999-f87e-4a7e-a057-5be7b6d8f7d5",
-        "tags": []
-      },
-      "outputs": [],
-      "source": [
-        "print('~'*100, 'REAL TEXT')\n",
-        "[tf.strings.reduce_join([num_to_char(word) for word in sentence]) for sentence in sample[1]]"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "id": "82bd4c10-dd6e-411e-834b-2a3b43fd12c5",
-      "metadata": {
-        "id": "82bd4c10-dd6e-411e-834b-2a3b43fd12c5",
-        "tags": []
-      },
-      "outputs": [],
-      "source": [
-        "decoded = tf.keras.backend.ctc_decode(yhat, input_length=[75,75], greedy=True)[0][0].numpy()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "id": "5d68ac46-c90b-4eab-a709-f19aee569ff5",
-      "metadata": {
-        "id": "5d68ac46-c90b-4eab-a709-f19aee569ff5",
-        "tags": []
-      },
-      "outputs": [],
-      "source": [
-        "print('~'*100, 'PREDICTIONS')\n",
-        "[tf.strings.reduce_join([num_to_char(word) for word in sentence]) for sentence in decoded]"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "64622f98-e99b-4fed-a2cc-f0da82eb5431",
-      "metadata": {
-        "id": "64622f98-e99b-4fed-a2cc-f0da82eb5431"
-      },
-      "source": [
-        "# Test on a Video"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "id": "a8b0c4d0-2031-4331-b91d-d87b1ae6f6e2",
-      "metadata": {
-        "id": "a8b0c4d0-2031-4331-b91d-d87b1ae6f6e2"
-      },
-      "outputs": [],
-      "source": [
-        "sample = load_data(tf.convert_to_tensor('.\\\\data\\\\s1\\\\bras9a.mpg'))"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "id": "0cca60e4-47a9-4683-8a75-48f4684f723d",
-      "metadata": {
-        "id": "0cca60e4-47a9-4683-8a75-48f4684f723d"
-      },
-      "outputs": [],
-      "source": [
-        "print('~'*100, 'REAL TEXT')\n",
-        "[tf.strings.reduce_join([num_to_char(word) for word in sentence]) for sentence in [sample[1]]]"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "id": "8cc5037c-1e32-435c-b0cc-01e1fb3b863c",
-      "metadata": {
-        "id": "8cc5037c-1e32-435c-b0cc-01e1fb3b863c"
-      },
-      "outputs": [],
-      "source": [
-        "yhat = model.predict(tf.expand_dims(sample[0], axis=0))"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "id": "22c4f77d-715d-409f-bc5e-3ebe48704e8f",
-      "metadata": {
-        "id": "22c4f77d-715d-409f-bc5e-3ebe48704e8f"
-      },
-      "outputs": [],
-      "source": [
-        "# decoded = tf.keras.backend.ctc_decode(yhat, input_length=[75], greedy=True)[0][0].numpy()\n",
-        "decoded = keras.backend.ctc_decode(yhat, input_length=[75], greedy=True)[0][0].numpy()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "id": "e4d12ecc-b634-499e-a4bc-db9f010835fb",
-      "metadata": {
-        "id": "e4d12ecc-b634-499e-a4bc-db9f010835fb"
-      },
-      "outputs": [],
-      "source": [
-        "print('~'*100, 'PREDICTIONS')\n",
-        "[tf.strings.reduce_join([num_to_char(word) for word in sentence]) for sentence in decoded]"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "id": "551dfea2-de6b-4400-b71a-a17631529e3f",
-      "metadata": {
-        "id": "551dfea2-de6b-4400-b71a-a17631529e3f"
-      },
-      "outputs": [],
-      "source": []
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "id": "fa95863d-3832-47bf-8a77-ebaa38054ace",
-      "metadata": {
-        "id": "fa95863d-3832-47bf-8a77-ebaa38054ace"
-      },
-      "outputs": [],
-      "source": []
-    }
-  ],
-  "metadata": {
-    "accelerator": "GPU",
-    "colab": {
-      "gpuType": "T4",
-      "include_colab_link": true,
-      "provenance": []
-    },
-    "kernelspec": {
-      "display_name": "Python 3",
-      "name": "python3"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.12.3"
-    }
-  },
-  "nbformat": 4,
-  "nbformat_minor": 5
-}

From a1509efd5af010c839f5230ee076613fb7ac6277 Mon Sep 17 00:00:00 2001
From: "haidun.li" 
Date: Tue, 16 Apr 2024 01:12:14 -0300
Subject: [PATCH 09/17] Refactor .gitignore: Remove unnecessary files and
 directories

---
 .gitignore    | 176 +-------------------------------------------------
 animation.gif | Bin 443531 -> 0 bytes
 2 files changed, 2 insertions(+), 174 deletions(-)
 delete mode 100644 animation.gif

diff --git a/.gitignore b/.gitignore
index 7a957989..1a6ba95f 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,177 +1,5 @@
-.vscode
-node_modules
-dist
-yarn-error.log
-yarn.lock
-package-lock.json
-**/__pycache__/
-**/*.pyc
-**/*.pyo
-**/*.pyd
-.DS_Store
-
 # LipNet
 data/
 models/
-
-
-# Byte-compiled / optimized / DLL files
-__pycache__/
-*.py[cod]
-*$py.class
-
-# C extensions
-*.so
-
-# Distribution / packaging
-.Python
-build/
-develop-eggs/
-dist/
-downloads/
-eggs/
-.eggs/
-lib/
-lib64/
-parts/
-sdist/
-var/
-wheels/
-share/python-wheels/
-*.egg-info/
-.installed.cfg
-*.egg
-MANIFEST
-
-# PyInstaller
-#  Usually these files are written by a python script from a template
-#  before PyInstaller builds the exe, so as to inject date/other infos into it.
-*.manifest
-*.spec
-
-# Installer logs
-pip-log.txt
-pip-delete-this-directory.txt
-
-# Unit test / coverage reports
-htmlcov/
-.tox/
-.nox/
-.coverage
-.coverage.*
-.cache
-nosetests.xml
-coverage.xml
-*.cover
-*.py,cover
-.hypothesis/
-.pytest_cache/
-cover/
-
-# Translations
-*.mo
-*.pot
-
-# Django stuff:
-*.log
-local_settings.py
-db.sqlite3
-db.sqlite3-journal
-
-# Flask stuff:
-instance/
-.webassets-cache
-
-# Scrapy stuff:
-.scrapy
-
-# Sphinx documentation
-docs/_build/
-
-# PyBuilder
-.pybuilder/
-target/
-
-# Jupyter Notebook
-.ipynb_checkpoints
-
-# IPython
-profile_default/
-ipython_config.py
-
-# pyenv
-#   For a library or package, you might want to ignore these files since the code is
-#   intended to run in multiple environments; otherwise, check them in:
-# .python-version
-
-# pipenv
-#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
-#   However, in case of collaboration, if having platform-specific dependencies or dependencies
-#   having no cross-platform support, pipenv may install dependencies that don't work, or not
-#   install all needed dependencies.
-#Pipfile.lock
-
-# poetry
-#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
-#   This is especially recommended for binary packages to ensure reproducibility, and is more
-#   commonly ignored for libraries.
-#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
-#poetry.lock
-
-# pdm
-#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
-#pdm.lock
-#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
-#   in version control.
-#   https://pdm.fming.dev/#use-with-ide
-.pdm.toml
-
-# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
-__pypackages__/
-
-# Celery stuff
-celerybeat-schedule
-celerybeat.pid
-
-# SageMath parsed files
-*.sage.py
-
-# Environments
-.env
-.venv
-env/
-venv/
-ENV/
-env.bak/
-venv.bak/
-
-# Spyder project settings
-.spyderproject
-.spyproject
-
-# Rope project settings
-.ropeproject
-
-# mkdocs documentation
-/site
-
-# mypy
-.mypy_cache/
-.dmypy.json
-dmypy.json
-
-# Pyre type checker
-.pyre/
-
-# pytype static type analyzer
-.pytype/
-
-# Cython debug symbols
-cython_debug/
-
-# PyCharm
-#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
-#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
-#  and can be added to the global gitignore or merged into this file.  For a more nuclear
-#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
-#.idea/
+results/
+__pycache__/
\ No newline at end of file
diff --git a/animation.gif b/animation.gif
deleted file mode 100644
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GIT binary patch
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From ac4ede4dba2c216590c51ea4b962458a94800cb8 Mon Sep 17 00:00:00 2001
From: "haidun.li" 
Date: Tue, 16 Apr 2024 01:13:38 -0300
Subject: [PATCH 10/17] Add alignment generator script to extract audio from
 video and generate alignment file

---
 aligment_generator/align_gen.py | 49 +++++++++++++++++++++++++++++++++
 1 file changed, 49 insertions(+)
 create mode 100644 aligment_generator/align_gen.py

diff --git a/aligment_generator/align_gen.py b/aligment_generator/align_gen.py
new file mode 100644
index 00000000..5b5aeadc
--- /dev/null
+++ b/aligment_generator/align_gen.py
@@ -0,0 +1,49 @@
+from moviepy.editor import VideoFileClip
+import speech_recognition as sr
+import os
+video_path = os.getcwd() + "/data_reprocess/input.mp4"
+print('video_path:', video_path)
+audio_path = os.getcwd() + "/data_reprocess/input.wav"
+print('audio_path:', audio_path)
+align_file_path = os.getcwd() + "/data_reprocess/input.txt"
+print('align_file_path:', align_file_path)
+
+def extract_audio_from_video(video_path, audio_path):
+    video_clip = VideoFileClip(video_path)
+    audio_clip = video_clip.audio
+    audio_clip.write_audiofile(audio_path)
+
+def recognize_speech_from_file(file_path):
+    recognizer = sr.Recognizer()
+
+    with sr.AudioFile(file_path) as source:
+        audio = recognizer.record(source)
+
+    return recognizer.recognize_google(audio)
+
+def format_align_file(transcription):
+    # For simplicity, let's assume that the words in the transcription are separated by spaces
+    words = transcription.split()
+
+    # Set the total duration of the video (in milliseconds)
+    total_duration = 74500
+
+    # Calculate the approximate duration of each word
+    word_duration = total_duration / len(words)
+
+    align_file_content = ""
+
+    start_time = 0
+    for i, word in enumerate(words):
+        end_time = start_time + word_duration
+        align_file_content += f"{start_time} {end_time} {word}\n"
+        start_time = end_time
+
+    return align_file_content
+
+extract_audio_from_video(video_path, audio_path)
+transcription = recognize_speech_from_file(audio_path)
+align_file_content = format_align_file(transcription)
+
+with open(align_file_path, "w") as align_file:
+    align_file.write(align_file_content)

From 111f8030fe3106f8f3d4afc44ec8b4ea113012dc Mon Sep 17 00:00:00 2001
From: Haidun Li 
Date: Fri, 21 Jun 2024 13:51:18 -0300
Subject: [PATCH 11/17] Refactor modelutil.py: Import Orthogonal initializer
 and update LSTM layers

---
 LipNet_my.ipynb | 2590 +++++++++++++++++++++++++++++++++++++++++++++++
 animation.gif   |  Bin 0 -> 443531 bytes
 2 files changed, 2590 insertions(+)
 create mode 100755 LipNet_my.ipynb
 create mode 100755 animation.gif

diff --git a/LipNet_my.ipynb b/LipNet_my.ipynb
new file mode 100755
index 00000000..0141a560
--- /dev/null
+++ b/LipNet_my.ipynb
@@ -0,0 +1,2590 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "colab_type": "text",
+        "id": "view-in-github"
+      },
+      "source": [
+        "\"Open"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "a3573a47-3689-4668-b62f-5c8451b2b4e9",
+      "metadata": {
+        "id": "a3573a47-3689-4668-b62f-5c8451b2b4e9",
+        "tags": []
+      },
+      "source": [
+        "# 0. Install and Import Dependencies"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "id": "ddfbccbe-41ae-4c23-98b1-a13868e2b499",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "ddfbccbe-41ae-4c23-98b1-a13868e2b499",
+        "outputId": "964dae10-91f7-4769-b15c-4a00ca9db312",
+        "scrolled": true,
+        "tags": []
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Package                   Version\n",
+            "------------------------- --------------\n",
+            "absl-py                   2.1.0\n",
+            "altair                    5.3.0\n",
+            "anyio                     4.3.0\n",
+            "argon2-cffi               23.1.0\n",
+            "argon2-cffi-bindings      21.2.0\n",
+            "arrow                     1.3.0\n",
+            "asttokens                 2.4.1\n",
+            "astunparse                1.6.3\n",
+            "async-lru                 2.0.4\n",
+            "attrs                     23.2.0\n",
+            "av                        12.0.0\n",
+            "Babel                     2.14.0\n",
+            "beautifulsoup4            4.12.3\n",
+            "bleach                    6.1.0\n",
+            "blinker                   1.7.0\n",
+            "cachetools                5.3.3\n",
+            "certifi                   2024.2.2\n",
+            "cffi                      1.16.0\n",
+            "charset-normalizer        3.3.2\n",
+            "click                     8.1.7\n",
+            "colorama                  0.4.6\n",
+            "comm                      0.2.2\n",
+            "contourpy                 1.2.1\n",
+            "cycler                    0.12.1\n",
+            "debugpy                   1.8.1\n",
+            "decorator                 5.1.1\n",
+            "defusedxml                0.7.1\n",
+            "executing                 2.0.1\n",
+            "fastjsonschema            2.19.1\n",
+            "filelock                  3.13.4\n",
+            "flatbuffers               24.3.25\n",
+            "fonttools                 4.51.0\n",
+            "fqdn                      1.5.1\n",
+            "fsspec                    2024.3.1\n",
+            "gast                      0.5.4\n",
+            "gdown                     5.1.0\n",
+            "gitdb                     4.0.11\n",
+            "GitPython                 3.1.43\n",
+            "google-pasta              0.2.0\n",
+            "grpcio                    1.62.1\n",
+            "h11                       0.14.0\n",
+            "h5py                      3.11.0\n",
+            "httpcore                  1.0.5\n",
+            "httpx                     0.27.0\n",
+            "idna                      3.7\n",
+            "imageio                   2.23.0\n",
+            "ipykernel                 6.29.4\n",
+            "ipynb-py-convert          0.4.6\n",
+            "ipython                   8.23.0\n",
+            "ipywidgets                8.1.2\n",
+            "isoduration               20.11.0\n",
+            "jedi                      0.19.1\n",
+            "Jinja2                    3.1.3\n",
+            "json5                     0.9.24\n",
+            "jsonpointer               2.4\n",
+            "jsonschema                4.21.1\n",
+            "jsonschema-specifications 2023.12.1\n",
+            "jupyter                   1.0.0\n",
+            "jupyter_client            8.6.1\n",
+            "jupyter-console           6.6.3\n",
+            "jupyter_core              5.7.2\n",
+            "jupyter-events            0.10.0\n",
+            "jupyter-lsp               2.2.5\n",
+            "jupyter_server            2.13.0\n",
+            "jupyter_server_terminals  0.5.3\n",
+            "jupyterlab                4.1.6\n",
+            "jupyterlab_pygments       0.3.0\n",
+            "jupyterlab_server         2.26.0\n",
+            "jupyterlab_widgets        3.0.10\n",
+            "kiwisolver                1.4.5\n",
+            "libclang                  18.1.1\n",
+            "Markdown                  3.6\n",
+            "markdown-it-py            3.0.0\n",
+            "MarkupSafe                2.1.5\n",
+            "matplotlib                3.8.4\n",
+            "matplotlib-inline         0.1.6\n",
+            "mdurl                     0.1.2\n",
+            "mistune                   3.0.2\n",
+            "ml-dtypes                 0.3.2\n",
+            "mpmath                    1.3.0\n",
+            "namex                     0.0.7\n",
+            "nbclient                  0.10.0\n",
+            "nbconvert                 7.16.3\n",
+            "nbformat                  5.10.4\n",
+            "nest-asyncio              1.6.0\n",
+            "networkx                  3.3\n",
+            "notebook                  7.1.2\n",
+            "notebook_shim             0.2.4\n",
+            "numpy                     1.26.4\n",
+            "opencv-python             4.6.0.66\n",
+            "opt-einsum                3.3.0\n",
+            "optree                    0.11.0\n",
+            "overrides                 7.7.0\n",
+            "packaging                 24.0\n",
+            "pandas                    2.2.1\n",
+            "pandocfilters             1.5.1\n",
+            "parso                     0.8.4\n",
+            "pillow                    10.3.0\n",
+            "pip                       24.0\n",
+            "platformdirs              4.2.0\n",
+            "prometheus_client         0.20.0\n",
+            "prompt-toolkit            3.0.43\n",
+            "protobuf                  4.25.3\n",
+            "psutil                    5.9.8\n",
+            "pure-eval                 0.2.2\n",
+            "pyarrow                   15.0.2\n",
+            "pycparser                 2.22\n",
+            "pydeck                    0.8.1b0\n",
+            "Pygments                  2.17.2\n",
+            "pyparsing                 3.1.2\n",
+            "PySocks                   1.7.1\n",
+            "python-dateutil           2.9.0.post0\n",
+            "python-json-logger        2.0.7\n",
+            "pytz                      2024.1\n",
+            "pywin32                   306\n",
+            "pywinpty                  2.0.13\n",
+            "PyYAML                    6.0.1\n",
+            "pyzmq                     25.1.2\n",
+            "qtconsole                 5.5.1\n",
+            "QtPy                      2.4.1\n",
+            "referencing               0.34.0\n",
+            "requests                  2.31.0\n",
+            "rfc3339-validator         0.1.4\n",
+            "rfc3986-validator         0.1.1\n",
+            "rich                      13.7.1\n",
+            "rpds-py                   0.18.0\n",
+            "Send2Trash                1.8.3\n",
+            "setuptools                69.2.0\n",
+            "six                       1.16.0\n",
+            "smmap                     5.0.1\n",
+            "sniffio                   1.3.1\n",
+            "soupsieve                 2.5\n",
+            "stack-data                0.6.3\n",
+            "streamlit                 1.33.0\n",
+            "sympy                     1.12\n",
+            "tenacity                  8.2.3\n",
+            "tensorboard               2.16.2\n",
+            "tensorboard-data-server   0.7.2\n",
+            "tensorflow                2.16.1\n",
+            "tensorflow-intel          2.16.1\n",
+            "termcolor                 2.4.0\n",
+            "terminado                 0.18.1\n",
+            "tinycss2                  1.2.1\n",
+            "toml                      0.10.2\n",
+            "toolz                     0.12.1\n",
+            "torch                     2.2.2\n",
+            "torchvision               0.17.2\n",
+            "tornado                   6.4\n",
+            "tqdm                      4.66.2\n",
+            "traitlets                 5.14.2\n",
+            "types-python-dateutil     2.9.0.20240316\n",
+            "typing_extensions         4.11.0\n",
+            "tzdata                    2024.1\n",
+            "uri-template              1.3.0\n",
+            "urllib3                   2.2.1\n",
+            "watchdog                  4.0.0\n",
+            "wcwidth                   0.2.13\n",
+            "webcolors                 1.13\n",
+            "webencodings              0.5.1\n",
+            "websocket-client          1.7.0\n",
+            "Werkzeug                  3.0.2\n",
+            "wheel                     0.43.0\n",
+            "widgetsnbextension        4.0.10\n",
+            "wrapt                     1.16.0\n"
+          ]
+        }
+      ],
+      "source": [
+        "!pip list"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "id": "02f907ea-f669-46c7-adcf-7f257e663448",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "02f907ea-f669-46c7-adcf-7f257e663448",
+        "outputId": "e444b2f9-189a-40bd-a2f9-925f13917c35",
+        "tags": []
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Requirement already satisfied: opencv-python in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (4.6.0.66)\n",
+            "Requirement already satisfied: matplotlib in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (3.8.4)\n",
+            "Requirement already satisfied: imageio in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (2.23.0)\n",
+            "Requirement already satisfied: gdown in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (5.1.0)\n",
+            "Requirement already satisfied: tensorflow in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (2.16.1)\n",
+            "Requirement already satisfied: numpy>=1.21.2 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from opencv-python) (1.26.4)\n",
+            "Requirement already satisfied: contourpy>=1.0.1 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from matplotlib) (1.2.1)\n",
+            "Requirement already satisfied: cycler>=0.10 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from matplotlib) (0.12.1)\n",
+            "Requirement already satisfied: fonttools>=4.22.0 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from matplotlib) (4.51.0)\n",
+            "Requirement already satisfied: kiwisolver>=1.3.1 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from matplotlib) (1.4.5)\n",
+            "Requirement already satisfied: packaging>=20.0 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from matplotlib) (24.0)\n",
+            "Requirement already satisfied: pillow>=8 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from matplotlib) (10.3.0)\n",
+            "Requirement already satisfied: pyparsing>=2.3.1 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from matplotlib) (3.1.2)\n",
+            "Requirement already satisfied: python-dateutil>=2.7 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from matplotlib) (2.9.0.post0)\n",
+            "Requirement already satisfied: beautifulsoup4 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from gdown) (4.12.3)\n",
+            "Requirement already satisfied: filelock in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from gdown) (3.13.4)\n",
+            "Requirement already satisfied: requests[socks] in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from gdown) (2.31.0)\n",
+            "Requirement already satisfied: tqdm in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from gdown) (4.66.2)\n",
+            "Requirement already satisfied: tensorflow-intel==2.16.1 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from tensorflow) (2.16.1)\n",
+            "Requirement already satisfied: absl-py>=1.0.0 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from tensorflow-intel==2.16.1->tensorflow) (2.1.0)\n",
+            "Requirement already satisfied: astunparse>=1.6.0 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from tensorflow-intel==2.16.1->tensorflow) (1.6.3)\n",
+            "Requirement already satisfied: flatbuffers>=23.5.26 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from tensorflow-intel==2.16.1->tensorflow) (24.3.25)\n",
+            "Requirement already satisfied: gast!=0.5.0,!=0.5.1,!=0.5.2,>=0.2.1 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from tensorflow-intel==2.16.1->tensorflow) (0.5.4)\n",
+            "Requirement already satisfied: google-pasta>=0.1.1 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from tensorflow-intel==2.16.1->tensorflow) (0.2.0)\n",
+            "Requirement already satisfied: h5py>=3.10.0 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from tensorflow-intel==2.16.1->tensorflow) (3.11.0)\n",
+            "Requirement already satisfied: libclang>=13.0.0 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from tensorflow-intel==2.16.1->tensorflow) (18.1.1)\n",
+            "Requirement already satisfied: ml-dtypes~=0.3.1 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from tensorflow-intel==2.16.1->tensorflow) (0.3.2)\n",
+            "Requirement already satisfied: opt-einsum>=2.3.2 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from tensorflow-intel==2.16.1->tensorflow) (3.3.0)\n",
+            "Requirement already satisfied: protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.20.3 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from tensorflow-intel==2.16.1->tensorflow) (4.25.3)\n",
+            "Requirement already satisfied: setuptools in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from tensorflow-intel==2.16.1->tensorflow) (69.2.0)\n",
+            "Requirement already satisfied: six>=1.12.0 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from tensorflow-intel==2.16.1->tensorflow) (1.16.0)\n",
+            "Requirement already satisfied: termcolor>=1.1.0 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from tensorflow-intel==2.16.1->tensorflow) (2.4.0)\n",
+            "Requirement already satisfied: typing-extensions>=3.6.6 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from tensorflow-intel==2.16.1->tensorflow) (4.11.0)\n",
+            "Requirement already satisfied: wrapt>=1.11.0 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from tensorflow-intel==2.16.1->tensorflow) (1.16.0)\n",
+            "Requirement already satisfied: grpcio<2.0,>=1.24.3 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from tensorflow-intel==2.16.1->tensorflow) (1.62.1)\n",
+            "Requirement already satisfied: tensorboard<2.17,>=2.16 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from tensorflow-intel==2.16.1->tensorflow) (2.16.2)\n",
+            "Collecting keras>=3.0.0 (from tensorflow-intel==2.16.1->tensorflow)\n",
+            "  Using cached keras-3.2.1-py3-none-any.whl.metadata (5.6 kB)\n",
+            "Requirement already satisfied: soupsieve>1.2 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from beautifulsoup4->gdown) (2.5)\n",
+            "Requirement already satisfied: charset-normalizer<4,>=2 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from requests[socks]->gdown) (3.3.2)\n",
+            "Requirement already satisfied: idna<4,>=2.5 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from requests[socks]->gdown) (3.7)\n",
+            "Requirement already satisfied: urllib3<3,>=1.21.1 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from requests[socks]->gdown) (2.2.1)\n",
+            "Requirement already satisfied: certifi>=2017.4.17 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from requests[socks]->gdown) (2024.2.2)\n",
+            "Requirement already satisfied: PySocks!=1.5.7,>=1.5.6 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from requests[socks]->gdown) (1.7.1)\n",
+            "Requirement already satisfied: colorama in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from tqdm->gdown) (0.4.6)\n",
+            "Requirement already satisfied: wheel<1.0,>=0.23.0 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from astunparse>=1.6.0->tensorflow-intel==2.16.1->tensorflow) (0.43.0)\n",
+            "Requirement already satisfied: rich in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from keras>=3.0.0->tensorflow-intel==2.16.1->tensorflow) (13.7.1)\n",
+            "Requirement already satisfied: namex in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from keras>=3.0.0->tensorflow-intel==2.16.1->tensorflow) (0.0.7)\n",
+            "Requirement already satisfied: optree in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from keras>=3.0.0->tensorflow-intel==2.16.1->tensorflow) (0.11.0)\n",
+            "Requirement already satisfied: markdown>=2.6.8 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from tensorboard<2.17,>=2.16->tensorflow-intel==2.16.1->tensorflow) (3.6)\n",
+            "Requirement already satisfied: tensorboard-data-server<0.8.0,>=0.7.0 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from tensorboard<2.17,>=2.16->tensorflow-intel==2.16.1->tensorflow) (0.7.2)\n",
+            "Requirement already satisfied: werkzeug>=1.0.1 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from tensorboard<2.17,>=2.16->tensorflow-intel==2.16.1->tensorflow) (3.0.2)\n",
+            "Requirement already satisfied: MarkupSafe>=2.1.1 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from werkzeug>=1.0.1->tensorboard<2.17,>=2.16->tensorflow-intel==2.16.1->tensorflow) (2.1.5)\n",
+            "Requirement already satisfied: markdown-it-py>=2.2.0 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from rich->keras>=3.0.0->tensorflow-intel==2.16.1->tensorflow) (3.0.0)\n",
+            "Requirement already satisfied: pygments<3.0.0,>=2.13.0 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from rich->keras>=3.0.0->tensorflow-intel==2.16.1->tensorflow) (2.17.2)\n",
+            "Requirement already satisfied: mdurl~=0.1 in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from markdown-it-py>=2.2.0->rich->keras>=3.0.0->tensorflow-intel==2.16.1->tensorflow) (0.1.2)\n",
+            "Using cached keras-3.2.1-py3-none-any.whl (1.1 MB)\n",
+            "Installing collected packages: keras\n",
+            "Successfully installed keras-3.2.1\n"
+          ]
+        }
+      ],
+      "source": [
+        "!pip install opencv-python matplotlib imageio gdown tensorflow"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "id": "b24af50c-20b8-409d-ad78-30a933fdd669",
+      "metadata": {
+        "id": "b24af50c-20b8-409d-ad78-30a933fdd669",
+        "tags": []
+      },
+      "outputs": [],
+      "source": [
+        "import os\n",
+        "import cv2\n",
+        "import tensorflow as tf\n",
+        "import numpy as np\n",
+        "from typing import List\n",
+        "from matplotlib import pyplot as plt\n",
+        "import imageio"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 52,
+      "id": "1e3db0b0-e559-4ad6-91fd-e7414b7d75e6",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "1e3db0b0-e559-4ad6-91fd-e7414b7d75e6",
+        "outputId": "c1e41844-c8fd-4efc-eb22-11f3817b0c9f"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "[]"
+            ]
+          },
+          "execution_count": 52,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "tf.config.list_physical_devices('GPU')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "id": "378d045a-3003-4f93-b7d2-a25a97774a68",
+      "metadata": {
+        "id": "378d045a-3003-4f93-b7d2-a25a97774a68",
+        "tags": []
+      },
+      "outputs": [],
+      "source": [
+        "physical_devices = tf.config.list_physical_devices('GPU')\n",
+        "try:\n",
+        "    tf.config.experimental.set_memory_growth(physical_devices[0], True)\n",
+        "except:\n",
+        "    pass"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "7a19e88e-c7b9-45c1-ae1e-f2109329c71b",
+      "metadata": {
+        "id": "7a19e88e-c7b9-45c1-ae1e-f2109329c71b",
+        "tags": []
+      },
+      "source": [
+        "# 1. Build Data Loading Functions"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "id": "8fb99c90-e05a-437f-839d-6e772f8c1dd5",
+      "metadata": {
+        "id": "8fb99c90-e05a-437f-839d-6e772f8c1dd5",
+        "tags": []
+      },
+      "outputs": [],
+      "source": [
+        "import gdown"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "id": "c019e4c6-2af3-4160-99ea-5c8cb009f1a7",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "c019e4c6-2af3-4160-99ea-5c8cb009f1a7",
+        "outputId": "49a7c7ff-ab7d-480e-bcfd-ee652ad89af1",
+        "tags": []
+      },
+      "outputs": [],
+      "source": [
+        "# url = 'https://drive.google.com/uc?id=1YlvpDLix3S-U8fd-gqRwPcWXAXm8JwjL'\n",
+        "# output = 'data.zip'\n",
+        "# gdown.download(url, output, quiet=False)\n",
+        "# gdown.extractall('data.zip')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "id": "8548cc59-6dfc-4acc-abc3-3e65212db02e",
+      "metadata": {
+        "id": "8548cc59-6dfc-4acc-abc3-3e65212db02e",
+        "tags": []
+      },
+      "outputs": [],
+      "source": [
+        "def load_video(path:str) -> List[float]:\n",
+        "\n",
+        "    cap = cv2.VideoCapture(path)\n",
+        "    frames = []\n",
+        "    for _ in range(int(cap.get(cv2.CAP_PROP_FRAME_COUNT))):\n",
+        "        ret, frame = cap.read()\n",
+        "        frame = tf.image.rgb_to_grayscale(frame)\n",
+        "        frames.append(frame[190:236,80:220,:])\n",
+        "    cap.release()\n",
+        "\n",
+        "    mean = tf.math.reduce_mean(frames)\n",
+        "    std = tf.math.reduce_std(tf.cast(frames, tf.float32))\n",
+        "    return tf.cast((frames - mean), tf.float32) / std"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 9,
+      "id": "ec735e0b-ec98-4eb0-8f49-c35527d6670a",
+      "metadata": {
+        "id": "ec735e0b-ec98-4eb0-8f49-c35527d6670a",
+        "tags": []
+      },
+      "outputs": [],
+      "source": [
+        "vocab = [x for x in \"abcdefghijklmnopqrstuvwxyz'?!123456789 \"]"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 10,
+      "id": "be04e972-d7a5-4a72-82d8-a6bdde1f3ce6",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "be04e972-d7a5-4a72-82d8-a6bdde1f3ce6",
+        "outputId": "01f490ee-dabf-4e94-c6ed-181e8c1ae2c4",
+        "tags": []
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "The vocabulary is: ['', 'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z', \"'\", '?', '!', '1', '2', '3', '4', '5', '6', '7', '8', '9', ' '] (size =40)\n"
+          ]
+        }
+      ],
+      "source": [
+        "char_to_num = tf.keras.layers.StringLookup(vocabulary=vocab, oov_token=\"\")\n",
+        "num_to_char = tf.keras.layers.StringLookup(\n",
+        "    vocabulary=char_to_num.get_vocabulary(), oov_token=\"\", invert=True\n",
+        ")\n",
+        "\n",
+        "print(\n",
+        "    f\"The vocabulary is: {char_to_num.get_vocabulary()} \"\n",
+        "    f\"(size ={char_to_num.vocabulary_size()})\"\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 11,
+      "id": "559f7420-6802-45fa-9ca0-b1ff209b461c",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "559f7420-6802-45fa-9ca0-b1ff209b461c",
+        "outputId": "90f0547b-3308-478e-be4a-c89cef1d58e6",
+        "tags": []
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "['',\n",
+              " 'a',\n",
+              " 'b',\n",
+              " 'c',\n",
+              " 'd',\n",
+              " 'e',\n",
+              " 'f',\n",
+              " 'g',\n",
+              " 'h',\n",
+              " 'i',\n",
+              " 'j',\n",
+              " 'k',\n",
+              " 'l',\n",
+              " 'm',\n",
+              " 'n',\n",
+              " 'o',\n",
+              " 'p',\n",
+              " 'q',\n",
+              " 'r',\n",
+              " 's',\n",
+              " 't',\n",
+              " 'u',\n",
+              " 'v',\n",
+              " 'w',\n",
+              " 'x',\n",
+              " 'y',\n",
+              " 'z',\n",
+              " \"'\",\n",
+              " '?',\n",
+              " '!',\n",
+              " '1',\n",
+              " '2',\n",
+              " '3',\n",
+              " '4',\n",
+              " '5',\n",
+              " '6',\n",
+              " '7',\n",
+              " '8',\n",
+              " '9',\n",
+              " ' ']"
+            ]
+          },
+          "execution_count": 11,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "char_to_num.get_vocabulary()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 12,
+      "id": "797ff78b-b48f-4e14-bb62-8cd0ebf9501a",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "797ff78b-b48f-4e14-bb62-8cd0ebf9501a",
+        "outputId": "cedf213c-95e4-439a-a871-641aec307517",
+        "tags": []
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              ""
+            ]
+          },
+          "execution_count": 12,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "char_to_num(['n','i','c','k'])"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 13,
+      "id": "8cd7f4f4-ae77-4509-a4f4-c723787ebad1",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "8cd7f4f4-ae77-4509-a4f4-c723787ebad1",
+        "outputId": "cb5428be-73ea-47b2-ea8e-4d2c4e3dcb00"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              ""
+            ]
+          },
+          "execution_count": 13,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "num_to_char([14,  9,  3, 11])"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 14,
+      "id": "9491bab5-6a3c-4f79-879a-8f9fbe73ae2e",
+      "metadata": {
+        "id": "9491bab5-6a3c-4f79-879a-8f9fbe73ae2e",
+        "tags": []
+      },
+      "outputs": [],
+      "source": [
+        "def load_alignments(path:str) -> List[str]:\n",
+        "    with open(path, 'r') as f:\n",
+        "        lines = f.readlines()\n",
+        "    tokens = []\n",
+        "    for line in lines:\n",
+        "        line = line.split()\n",
+        "        if line[2] != 'sil':\n",
+        "            tokens = [*tokens,' ',line[2]]\n",
+        "    return char_to_num(tf.reshape(tf.strings.unicode_split(tokens, input_encoding='UTF-8'), (-1)))[1:]"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 15,
+      "id": "dd01ca9f-77fb-4643-a2aa-47dd82c5d66b",
+      "metadata": {
+        "id": "dd01ca9f-77fb-4643-a2aa-47dd82c5d66b",
+        "tags": []
+      },
+      "outputs": [],
+      "source": [
+        "def load_data(path: str):\n",
+        "    path = bytes.decode(path.numpy())\n",
+        "    # file_name = path.split('/')[-1].split('.')[0]\n",
+        "    # File name splitting for windows\n",
+        "    file_name = path.split('\\\\')[-1].split('.')[0]\n",
+        "    video_path = os.path.join('data','s1',f'{file_name}.mpg')\n",
+        "    alignment_path = os.path.join('data','alignments','s1',f'{file_name}.align')\n",
+        "    frames = load_video(video_path)\n",
+        "    alignments = load_alignments(alignment_path)\n",
+        "\n",
+        "    return frames, alignments"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 16,
+      "id": "8cb7cc58-31ae-4904-a805-1177a82717d2",
+      "metadata": {
+        "id": "8cb7cc58-31ae-4904-a805-1177a82717d2",
+        "tags": []
+      },
+      "outputs": [],
+      "source": [
+        "test_path = '.\\\\data\\\\s1\\\\bbal6n.mpg'\n",
+        "# test_path = './data/s1/bbal6n.mpg'"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 17,
+      "id": "76aa964f-0c84-490d-897a-d00e3966e2c9",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 35
+        },
+        "id": "76aa964f-0c84-490d-897a-d00e3966e2c9",
+        "outputId": "d04d49ad-4eeb-4be1-ffa7-f2655aa86a7a"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "'bbal6n'"
+            ]
+          },
+          "execution_count": 17,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "tf.convert_to_tensor(test_path).numpy().decode('utf-8').split('\\\\')[-1].split('.')[0]\n",
+        "# tf.convert_to_tensor(test_path).numpy().decode('utf-8').split('/')[-1].split('.')[0]"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 18,
+      "id": "eb602c71-8560-4f9e-b26b-08202febb937",
+      "metadata": {
+        "id": "eb602c71-8560-4f9e-b26b-08202febb937",
+        "scrolled": true,
+        "tags": []
+      },
+      "outputs": [],
+      "source": [
+        "frames, alignments = load_data(tf.convert_to_tensor(test_path))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 19,
+      "id": "0e3184a1-6b02-4b4f-84a8-a0a65f951ea2",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 244
+        },
+        "id": "0e3184a1-6b02-4b4f-84a8-a0a65f951ea2",
+        "outputId": "ca65c601-b300-4a41-d8fe-7915a8e5a50b"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              ""
+            ]
+          },
+          "execution_count": 19,
+          "metadata": {},
+          "output_type": "execute_result"
+        },
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "
"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "plt.imshow(frames[40])"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 20,
+      "id": "d7ec0833-d54b-4073-84cf-92d011c60ec1",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "d7ec0833-d54b-4073-84cf-92d011c60ec1",
+        "outputId": "02abd7a1-113b-42b1-a6ba-9a458373b112"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              ""
+            ]
+          },
+          "execution_count": 20,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "alignments"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 21,
+      "id": "fe1ad370-b287-4b46-85a2-7c45b0bd9b10",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "fe1ad370-b287-4b46-85a2-7c45b0bd9b10",
+        "outputId": "8910d788-545b-4e76-b154-4a90d8e63927"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              ""
+            ]
+          },
+          "execution_count": 21,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "tf.strings.reduce_join([bytes.decode(x) for x in num_to_char(alignments.numpy()).numpy()])"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 22,
+      "id": "6871031a-b0ba-4c76-a852-f6329b0f2606",
+      "metadata": {
+        "id": "6871031a-b0ba-4c76-a852-f6329b0f2606",
+        "tags": []
+      },
+      "outputs": [],
+      "source": [
+        "def mappable_function(path:str) ->List[str]:\n",
+        "    result = tf.py_function(load_data, [path], (tf.float32, tf.int64))\n",
+        "    return result"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "c40a7eb4-0c3e-4eab-9291-5611cb68ce08",
+      "metadata": {
+        "id": "c40a7eb4-0c3e-4eab-9291-5611cb68ce08",
+        "tags": []
+      },
+      "source": [
+        "# 2. Create Data Pipeline"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 23,
+      "id": "7686355d-45aa-4c85-ad9c-053e6a9b4d81",
+      "metadata": {
+        "id": "7686355d-45aa-4c85-ad9c-053e6a9b4d81",
+        "tags": []
+      },
+      "outputs": [],
+      "source": [
+        "from matplotlib import pyplot as plt"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 24,
+      "id": "f066fea2-91b1-42ed-a67d-00566a1a53ff",
+      "metadata": {
+        "id": "f066fea2-91b1-42ed-a67d-00566a1a53ff",
+        "tags": []
+      },
+      "outputs": [],
+      "source": [
+        "data = tf.data.Dataset.list_files('.\\\\data\\\\s1\\\\*.mpg')\n",
+        "data = data.shuffle(500, reshuffle_each_iteration=False)\n",
+        "data = data.map(mappable_function)\n",
+        "data = data.padded_batch(2, padded_shapes=([75,None,None,None],[40]))\n",
+        "data = data.prefetch(tf.data.AUTOTUNE)\n",
+        "# Added for split\n",
+        "train = data.take(450)\n",
+        "test = data.skip(450)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 25,
+      "id": "4de3b276",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "(450, 50)"
+            ]
+          },
+          "execution_count": 25,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "len(train), len(test)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 26,
+      "id": "5281bde8-fdc8-4da1-bd55-5a7929a9e80c",
+      "metadata": {
+        "id": "5281bde8-fdc8-4da1-bd55-5a7929a9e80c"
+      },
+      "outputs": [],
+      "source": [
+        "frames, alignments = data.as_numpy_iterator().next()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 27,
+      "id": "cbebe683-6afd-47fd-bba4-c83b4b13bb32",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "cbebe683-6afd-47fd-bba4-c83b4b13bb32",
+        "outputId": "8924019e-28cd-4bfa-9e1e-bb836d71e6f8"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "2"
+            ]
+          },
+          "execution_count": 27,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "len(frames)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 28,
+      "id": "5cf2d676-93a9-434c-b3c7-bdcc2577b2e7",
+      "metadata": {
+        "id": "5cf2d676-93a9-434c-b3c7-bdcc2577b2e7",
+        "tags": []
+      },
+      "outputs": [],
+      "source": [
+        "sample = data.as_numpy_iterator()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 29,
+      "id": "efa6cd46-7079-46c0-b45b-832f339f6cb0",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "efa6cd46-7079-46c0-b45b-832f339f6cb0",
+        "outputId": "c24ecd06-8d12-4531-df65-27a9f77acada",
+        "scrolled": true,
+        "tags": []
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "array([[[[[ 1.2631532 ],\n",
+              "          [ 1.3815738 ],\n",
+              "          [ 1.1842061 ],\n",
+              "          ...,\n",
+              "          [ 0.3157883 ],\n",
+              "          [ 0.27631477],\n",
+              "          [ 0.27631477]],\n",
+              "\n",
+              "         [[ 1.3421003 ],\n",
+              "          [ 1.4210473 ],\n",
+              "          [ 1.1842061 ],\n",
+              "          ...,\n",
+              "          [ 0.3157883 ],\n",
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+            ]
+          },
+          "execution_count": 29,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "val = sample.next(); val[0]"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 30,
+      "id": "acf5eb4f-a0da-4a9a-bf24-af13e9cc2fbe",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "acf5eb4f-a0da-4a9a-bf24-af13e9cc2fbe",
+        "outputId": "2b7ce709-6400-402c-ab9d-0e4c5739cafc",
+        "tags": []
+      },
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n",
+            "Lossy conversion from float32 to uint8. Range [0.0, 10.065752029418945]. Convert image to uint8 prior to saving to suppress this warning.\n"
+          ]
+        }
+      ],
+      "source": [
+        "imageio.mimsave('.\\\\animation.gif', val[0][0], fps=10)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 31,
+      "id": "c33a87a2-d5e0-4ec9-b174-73ebf41bf03a",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 244
+        },
+        "id": "c33a87a2-d5e0-4ec9-b174-73ebf41bf03a",
+        "outputId": "1b7cc028-1990-4714-8d7e-fda85218c0d5",
+        "tags": []
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              ""
+            ]
+          },
+          "execution_count": 31,
+          "metadata": {},
+          "output_type": "execute_result"
+        },
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "
"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# 0:videos, 0: 1st video out of the batch,  0: return the first frame in the video\n",
+        "plt.imshow(val[0][0][35])"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 32,
+      "id": "84593332-133c-4205-b7a6-8e235d5e2b3b",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "84593332-133c-4205-b7a6-8e235d5e2b3b",
+        "outputId": "69f24afc-2cb9-4d07-9d93-3b4e1fde5046",
+        "tags": []
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              ""
+            ]
+          },
+          "execution_count": 32,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "tf.strings.reduce_join([num_to_char(word) for word in val[1][0]])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "0f47733c-83bc-465c-b118-b198b492ad37",
+      "metadata": {
+        "id": "0f47733c-83bc-465c-b118-b198b492ad37",
+        "tags": []
+      },
+      "source": [
+        "# 3. Design the Deep Neural Network"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 33,
+      "id": "d8e9a497-191b-4842-afbd-26f5e13c43ba",
+      "metadata": {
+        "id": "d8e9a497-191b-4842-afbd-26f5e13c43ba",
+        "tags": []
+      },
+      "outputs": [],
+      "source": [
+        "import tensorflow\n",
+        "from tensorflow import keras\n",
+        "\n",
+        "from keras.models import Sequential\n",
+        "from keras.layers import Conv3D, LSTM, Dense, Dropout, Bidirectional, MaxPool3D, Activation, Reshape, SpatialDropout3D, BatchNormalization, TimeDistributed, Flatten\n",
+        "from keras.optimizers import Adam\n",
+        "from keras.callbacks import ModelCheckpoint, LearningRateScheduler\n",
+        "from keras.layers import LSTM, Bidirectional\n",
+        "from keras.initializers import Orthogonal\n",
+        "from keras.layers import Lambda\n",
+        "from keras.layers import Reshape\n",
+        "import tensorflow as tf"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 34,
+      "id": "3f753ed2-70b9-4236-8c1c-08ca065dc8bf",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "3f753ed2-70b9-4236-8c1c-08ca065dc8bf",
+        "outputId": "b06442c3-cc68-4c88-d5e8-c9a915fc8b96"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "(75, 46, 140, 1)"
+            ]
+          },
+          "execution_count": 34,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "data.as_numpy_iterator().next()[0][0].shape"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 35,
+      "id": "f9171056-a352-491a-9ed9-92b28ced268e",
+      "metadata": {
+        "id": "f9171056-a352-491a-9ed9-92b28ced268e",
+        "tags": []
+      },
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\layers\\convolutional\\base_conv.py:99: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
+            "  super().__init__(\n"
+          ]
+        }
+      ],
+      "source": [
+        "model = Sequential()\n",
+        "model.add(Conv3D(128, 3, input_shape=(75,46,140,1), padding='same'))\n",
+        "model.add(Activation('relu'))\n",
+        "model.add(MaxPool3D((1,2,2)))\n",
+        "\n",
+        "model.add(Conv3D(256, 3, padding='same'))\n",
+        "model.add(Activation('relu'))\n",
+        "model.add(MaxPool3D((1,2,2)))\n",
+        "\n",
+        "model.add(Conv3D(75, 3, padding='same'))\n",
+        "model.add(Activation('relu'))\n",
+        "model.add(MaxPool3D((1,2,2)))\n",
+        "\n",
+        "# model.add(Lambda(lambda x: tf.print(\"Shape before TimeDistributed:\", tf.shape(x))))\n",
+        "\n",
+        "model.add(TimeDistributed(Flatten()))\n",
+        "\n",
+        "# model.add(Reshape((-1, 75*5*17)))\n",
+        "\n",
+        "# model.add(Bidirectional(LSTM(128, return_sequences=True)))\n",
+        "model.add(Bidirectional(LSTM(128, return_sequences=True)))\n",
+        "\n",
+        "# # model.add(Bidirectional(LSTM(128, kernel_initializer='Orthogonal', return_sequences=True)))\n",
+        "model.add(Bidirectional(LSTM(128, kernel_initializer=Orthogonal(), return_sequences=True)))\n",
+        "model.add(Dropout(.5))\n",
+        "\n",
+        "# # model.add(Bidirectional(LSTM(128, kernel_initializer='Orthogonal', return_sequences=True)))\n",
+        "model.add(Bidirectional(LSTM(128, kernel_initializer=Orthogonal(), return_sequences=True)))\n",
+        "model.add(Dropout(.5))\n",
+        "\n",
+        "model.add(Dense(char_to_num.vocabulary_size()+1, kernel_initializer='he_normal', activation='softmax'))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 36,
+      "id": "ab905c18",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "
Model: \"sequential\"\n",
+              "
\n"
+            ],
+            "text/plain": [
+              "\u001b[1mModel: \"sequential\"\u001b[0m\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+              "┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃\n",
+              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+              "│ conv3d (Conv3D)                 │ (None, 75, 46, 140,    │         3,584 │\n",
+              "│                                 │ 128)                   │               │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ activation (Activation)         │ (None, 75, 46, 140,    │             0 │\n",
+              "│                                 │ 128)                   │               │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ max_pooling3d (MaxPooling3D)    │ (None, 75, 23, 70,     │             0 │\n",
+              "│                                 │ 128)                   │               │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ conv3d_1 (Conv3D)               │ (None, 75, 23, 70,     │       884,992 │\n",
+              "│                                 │ 256)                   │               │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ activation_1 (Activation)       │ (None, 75, 23, 70,     │             0 │\n",
+              "│                                 │ 256)                   │               │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ max_pooling3d_1 (MaxPooling3D)  │ (None, 75, 11, 35,     │             0 │\n",
+              "│                                 │ 256)                   │               │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ conv3d_2 (Conv3D)               │ (None, 75, 11, 35, 75) │       518,475 │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ activation_2 (Activation)       │ (None, 75, 11, 35, 75) │             0 │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ max_pooling3d_2 (MaxPooling3D)  │ (None, 75, 5, 17, 75)  │             0 │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ time_distributed                │ (None, 75, 6375)       │             0 │\n",
+              "│ (TimeDistributed)               │                        │               │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ bidirectional (Bidirectional)   │ (None, 75, 256)        │     6,660,096 │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ bidirectional_1 (Bidirectional) │ (None, 75, 256)        │       394,240 │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ dropout (Dropout)               │ (None, 75, 256)        │             0 │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ bidirectional_2 (Bidirectional) │ (None, 75, 256)        │       394,240 │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ dropout_1 (Dropout)             │ (None, 75, 256)        │             0 │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ dense (Dense)                   │ (None, 75, 41)         │        10,537 │\n",
+              "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
+              "
\n"
+            ],
+            "text/plain": [
+              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
+              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+              "│ conv3d (\u001b[38;5;33mConv3D\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m46\u001b[0m, \u001b[38;5;34m140\u001b[0m,    │         \u001b[38;5;34m3,584\u001b[0m │\n",
+              "│                                 │ \u001b[38;5;34m128\u001b[0m)                   │               │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ activation (\u001b[38;5;33mActivation\u001b[0m)         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m46\u001b[0m, \u001b[38;5;34m140\u001b[0m,    │             \u001b[38;5;34m0\u001b[0m │\n",
+              "│                                 │ \u001b[38;5;34m128\u001b[0m)                   │               │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ max_pooling3d (\u001b[38;5;33mMaxPooling3D\u001b[0m)    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m23\u001b[0m, \u001b[38;5;34m70\u001b[0m,     │             \u001b[38;5;34m0\u001b[0m │\n",
+              "│                                 │ \u001b[38;5;34m128\u001b[0m)                   │               │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ conv3d_1 (\u001b[38;5;33mConv3D\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m23\u001b[0m, \u001b[38;5;34m70\u001b[0m,     │       \u001b[38;5;34m884,992\u001b[0m │\n",
+              "│                                 │ \u001b[38;5;34m256\u001b[0m)                   │               │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ activation_1 (\u001b[38;5;33mActivation\u001b[0m)       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m23\u001b[0m, \u001b[38;5;34m70\u001b[0m,     │             \u001b[38;5;34m0\u001b[0m │\n",
+              "│                                 │ \u001b[38;5;34m256\u001b[0m)                   │               │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ max_pooling3d_1 (\u001b[38;5;33mMaxPooling3D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m11\u001b[0m, \u001b[38;5;34m35\u001b[0m,     │             \u001b[38;5;34m0\u001b[0m │\n",
+              "│                                 │ \u001b[38;5;34m256\u001b[0m)                   │               │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ conv3d_2 (\u001b[38;5;33mConv3D\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m11\u001b[0m, \u001b[38;5;34m35\u001b[0m, \u001b[38;5;34m75\u001b[0m) │       \u001b[38;5;34m518,475\u001b[0m │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ activation_2 (\u001b[38;5;33mActivation\u001b[0m)       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m11\u001b[0m, \u001b[38;5;34m35\u001b[0m, \u001b[38;5;34m75\u001b[0m) │             \u001b[38;5;34m0\u001b[0m │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ max_pooling3d_2 (\u001b[38;5;33mMaxPooling3D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m17\u001b[0m, \u001b[38;5;34m75\u001b[0m)  │             \u001b[38;5;34m0\u001b[0m │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ time_distributed                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m6375\u001b[0m)       │             \u001b[38;5;34m0\u001b[0m │\n",
+              "│ (\u001b[38;5;33mTimeDistributed\u001b[0m)               │                        │               │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ bidirectional (\u001b[38;5;33mBidirectional\u001b[0m)   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m256\u001b[0m)        │     \u001b[38;5;34m6,660,096\u001b[0m │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ bidirectional_1 (\u001b[38;5;33mBidirectional\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m256\u001b[0m)        │       \u001b[38;5;34m394,240\u001b[0m │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ dropout (\u001b[38;5;33mDropout\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m256\u001b[0m)        │             \u001b[38;5;34m0\u001b[0m │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ bidirectional_2 (\u001b[38;5;33mBidirectional\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m256\u001b[0m)        │       \u001b[38;5;34m394,240\u001b[0m │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ dropout_1 (\u001b[38;5;33mDropout\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m256\u001b[0m)        │             \u001b[38;5;34m0\u001b[0m │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ dense (\u001b[38;5;33mDense\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m41\u001b[0m)         │        \u001b[38;5;34m10,537\u001b[0m │\n",
+              "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
 Total params: 8,866,164 (33.82 MB)\n",
+              "
\n"
+            ],
+            "text/plain": [
+              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m8,866,164\u001b[0m (33.82 MB)\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
 Trainable params: 8,866,164 (33.82 MB)\n",
+              "
\n"
+            ],
+            "text/plain": [
+              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m8,866,164\u001b[0m (33.82 MB)\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
 Non-trainable params: 0 (0.00 B)\n",
+              "
\n"
+            ],
+            "text/plain": [
+              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "model.summary()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 37,
+      "id": "fae71bed",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "6375"
+            ]
+          },
+          "execution_count": 37,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "5*17*75"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 38,
+      "id": "7a5815ab",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 3s/step\n"
+          ]
+        }
+      ],
+      "source": [
+        "yhat = model.predict(val[0])"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 39,
+      "id": "65eccd93",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              ""
+            ]
+          },
+          "execution_count": 39,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "tf.strings.reduce_join([num_to_char(x) for x in tf.argmax(yhat[0],axis=1)])"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 40,
+      "id": "6ed47531-8317-4255-9a12-b757642258e6",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "6ed47531-8317-4255-9a12-b757642258e6",
+        "outputId": "5b755432-4136-4834-fd69-78aace01b57b",
+        "tags": []
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              ""
+            ]
+          },
+          "execution_count": 40,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "tf.strings.reduce_join([num_to_char(tf.argmax(x)) for x in yhat[0]])"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 41,
+      "id": "7c37b9b9-5298-4038-9c33-5031d1b457f0",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "7c37b9b9-5298-4038-9c33-5031d1b457f0",
+        "outputId": "fcdf9c94-35db-4aa0-fcf7-3029f32eac97",
+        "tags": []
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "(None, 75, 46, 140, 1)"
+            ]
+          },
+          "execution_count": 41,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "model.input_shape"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 42,
+      "id": "98b316a4-5322-4782-8e36-4b3c1a696d85",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "98b316a4-5322-4782-8e36-4b3c1a696d85",
+        "outputId": "5f9ebad7-b4cc-4ac8-c5e6-f5471120f50f",
+        "tags": []
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "(None, 75, 41)"
+            ]
+          },
+          "execution_count": 42,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "model.output_shape"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "2ec02176-5c26-46c3-aff7-8352e6563c7d",
+      "metadata": {
+        "id": "2ec02176-5c26-46c3-aff7-8352e6563c7d",
+        "tags": []
+      },
+      "source": [
+        "# 4. Setup Training Options and Train"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 43,
+      "id": "ab015fd0-7fb4-4d5d-9fa2-30a05dbd515a",
+      "metadata": {
+        "id": "ab015fd0-7fb4-4d5d-9fa2-30a05dbd515a",
+        "tags": []
+      },
+      "outputs": [],
+      "source": [
+        "def scheduler(epoch, lr):\n",
+        "    if epoch < 30:\n",
+        "        return lr\n",
+        "    else:\n",
+        "        return lr * tf.math.exp(-0.1)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 44,
+      "id": "c564d5c9-db54-4e88-b311-9aeab7fb3e69",
+      "metadata": {
+        "id": "c564d5c9-db54-4e88-b311-9aeab7fb3e69",
+        "tags": []
+      },
+      "outputs": [],
+      "source": [
+        "def CTCLoss(y_true, y_pred):\n",
+        "    batch_len = tf.cast(tf.shape(y_true)[0], dtype=\"int64\")\n",
+        "    input_length = tf.cast(tf.shape(y_pred)[1], dtype=\"int64\")\n",
+        "    label_length = tf.cast(tf.shape(y_true)[1], dtype=\"int64\")\n",
+        "\n",
+        "    input_length = input_length * tf.ones(shape=(batch_len, 1), dtype=\"int64\")\n",
+        "    label_length = label_length * tf.ones(shape=(batch_len, 1), dtype=\"int64\")\n",
+        "\n",
+        "    loss = tf.keras.backend.ctc_batch_cost(y_true, y_pred, input_length, label_length)\n",
+        "    return loss"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 45,
+      "id": "a26dc3fc-a19c-4378-bd8c-e2b597a1d15c",
+      "metadata": {
+        "id": "a26dc3fc-a19c-4378-bd8c-e2b597a1d15c",
+        "tags": []
+      },
+      "outputs": [],
+      "source": [
+        "class ProduceExample(tf.keras.callbacks.Callback):\n",
+        "    def __init__(self, dataset) -> None:\n",
+        "        self.dataset = dataset.as_numpy_iterator()\n",
+        "\n",
+        "    def on_epoch_end(self, epoch, logs=None) -> None:\n",
+        "        data = self.dataset.next()\n",
+        "        yhat = self.model.predict(data[0])\n",
+        "        decoded = tf.keras.backend.ctc_decode(yhat, [75,75], greedy=False)[0][0].numpy()\n",
+        "        for x in range(len(yhat)):\n",
+        "            print('Original:', tf.strings.reduce_join(num_to_char(data[1][x])).numpy().decode('utf-8'))\n",
+        "            print('Prediction:', tf.strings.reduce_join(num_to_char(decoded[x])).numpy().decode('utf-8'))\n",
+        "            print('~'*100)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 46,
+      "id": "04be90d8-2482-46f9-b513-d5f4f8001c7e",
+      "metadata": {
+        "id": "04be90d8-2482-46f9-b513-d5f4f8001c7e",
+        "tags": []
+      },
+      "outputs": [],
+      "source": [
+        "model.compile(optimizer=Adam(learning_rate=0.0001), loss=CTCLoss)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 47,
+      "id": "eab49367-3f1e-4464-ae76-dbd07549d97e",
+      "metadata": {
+        "id": "eab49367-3f1e-4464-ae76-dbd07549d97e",
+        "tags": []
+      },
+      "outputs": [],
+      "source": [
+        "# Adjust the file path to end with '.weights.h5'\n",
+        "checkpoint_path = os.path.join('models', 'overlapped-weights368.weights.h5')\n",
+        "\n",
+        "# Create the ModelCheckpoint callback\n",
+        "checkpoint_callback = ModelCheckpoint(\n",
+        "    filepath=checkpoint_path,\n",
+        "    monitor='loss',\n",
+        "    save_weights_only=True\n",
+        ")\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 48,
+      "id": "e085a632-d464-46ef-8777-959cad4adb2c",
+      "metadata": {
+        "id": "e085a632-d464-46ef-8777-959cad4adb2c",
+        "tags": []
+      },
+      "outputs": [],
+      "source": [
+        "schedule_callback = LearningRateScheduler(scheduler)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 49,
+      "id": "48eca991-90ab-4592-8a79-b50e9ca015b6",
+      "metadata": {
+        "id": "48eca991-90ab-4592-8a79-b50e9ca015b6",
+        "tags": []
+      },
+      "outputs": [],
+      "source": [
+        "example_callback = ProduceExample(test)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 50,
+      "id": "8ffba483-aa61-4bbe-a15f-a73e1ddf097c",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "8ffba483-aa61-4bbe-a15f-a73e1ddf097c",
+        "outputId": "5ac8771e-cd3b-4d4d-eaf5-15add3c1e8ae",
+        "tags": []
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Epoch 1/100\n",
+            "WARNING:tensorflow:From c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\legacy\\backend.py:666: The name tf.nn.ctc_loss is deprecated. Please use tf.compat.v1.nn.ctc_loss instead.\n",
+            "\n"
+          ]
+        },
+        {
+          "ename": "InvalidArgumentError",
+          "evalue": "Graph execution error:\n\nDetected at node sequential_1/time_distributed_1/Reshape_17 defined at (most recent call last):\n  File \"\", line 198, in _run_module_as_main\n\n  File \"\", line 88, in _run_code\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel_launcher.py\", line 18, in \n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\traitlets\\config\\application.py\", line 1075, in launch_instance\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel\\kernelapp.py\", line 739, in start\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\tornado\\platform\\asyncio.py\", line 205, in start\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\asyncio\\base_events.py\", line 641, in run_forever\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\asyncio\\base_events.py\", line 1987, in _run_once\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\asyncio\\events.py\", line 88, in _run\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 545, in dispatch_queue\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 534, in process_one\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 437, in dispatch_shell\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel\\ipkernel.py\", line 362, in execute_request\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 778, in execute_request\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel\\ipkernel.py\", line 449, in do_execute\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel\\zmqshell.py\", line 549, in run_cell\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3075, in run_cell\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3130, in _run_cell\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\async_helpers.py\", line 129, in _pseudo_sync_runner\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3334, in run_cell_async\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3517, in run_ast_nodes\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3577, in run_code\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Temp\\ipykernel_11284\\3536403359.py\", line 1, in \n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 117, in error_handler\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\backend\\tensorflow\\trainer.py\", line 329, in fit\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\backend\\tensorflow\\trainer.py\", line 122, in one_step_on_iterator\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\backend\\tensorflow\\trainer.py\", line 110, in one_step_on_data\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\backend\\tensorflow\\trainer.py\", line 57, in train_step\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 117, in error_handler\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\layers\\layer.py\", line 826, in __call__\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 117, in error_handler\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\ops\\operation.py\", line 48, in __call__\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 156, in error_handler\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\models\\sequential.py\", line 206, in call\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\models\\functional.py\", line 199, in call\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\ops\\function.py\", line 151, in _run_through_graph\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\models\\functional.py\", line 583, in call\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 117, in error_handler\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\layers\\layer.py\", line 826, in __call__\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 117, in error_handler\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\ops\\operation.py\", line 48, in __call__\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 156, in error_handler\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\layers\\rnn\\time_distributed.py\", line 108, in call\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\layers\\rnn\\time_distributed.py\", line 102, in step_function\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\layers\\reshaping\\flatten.py\", line 54, in call\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\ops\\numpy.py\", line 4527, in reshape\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\backend\\tensorflow\\numpy.py\", line 1618, in reshape\n\nOnly one input size may be -1, not both 0 and 1\n\t [[{{node sequential_1/time_distributed_1/Reshape_17}}]] [Op:__inference_one_step_on_iterator_34683]",
+          "output_type": "error",
+          "traceback": [
+            "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+            "\u001b[1;31mInvalidArgumentError\u001b[0m                      Traceback (most recent call last)",
+            "Cell \u001b[1;32mIn[50], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtrain\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalidation_data\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtest\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mepochs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m100\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcallbacks\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[43mcheckpoint_callback\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mschedule_callback\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mexample_callback\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n",
+            "File \u001b[1;32mc:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\utils\\traceback_utils.py:122\u001b[0m, in \u001b[0;36mfilter_traceback..error_handler\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    119\u001b[0m     filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n\u001b[0;32m    120\u001b[0m     \u001b[38;5;66;03m# To get the full stack trace, call:\u001b[39;00m\n\u001b[0;32m    121\u001b[0m     \u001b[38;5;66;03m# `keras.config.disable_traceback_filtering()`\u001b[39;00m\n\u001b[1;32m--> 122\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m e\u001b[38;5;241m.\u001b[39mwith_traceback(filtered_tb) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m    123\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[0;32m    124\u001b[0m     \u001b[38;5;28;01mdel\u001b[39;00m filtered_tb\n",
+            "File \u001b[1;32mc:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\tensorflow\\python\\eager\\execute.py:53\u001b[0m, in \u001b[0;36mquick_execute\u001b[1;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[0;32m     51\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m     52\u001b[0m   ctx\u001b[38;5;241m.\u001b[39mensure_initialized()\n\u001b[1;32m---> 53\u001b[0m   tensors \u001b[38;5;241m=\u001b[39m pywrap_tfe\u001b[38;5;241m.\u001b[39mTFE_Py_Execute(ctx\u001b[38;5;241m.\u001b[39m_handle, device_name, op_name,\n\u001b[0;32m     54\u001b[0m                                       inputs, attrs, num_outputs)\n\u001b[0;32m     55\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m core\u001b[38;5;241m.\u001b[39m_NotOkStatusException \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m     56\u001b[0m   \u001b[38;5;28;01mif\u001b[39;00m name \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
+            "\u001b[1;31mInvalidArgumentError\u001b[0m: Graph execution error:\n\nDetected at node sequential_1/time_distributed_1/Reshape_17 defined at (most recent call last):\n  File \"\", line 198, in _run_module_as_main\n\n  File \"\", line 88, in _run_code\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel_launcher.py\", line 18, in \n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\traitlets\\config\\application.py\", line 1075, in launch_instance\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel\\kernelapp.py\", line 739, in start\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\tornado\\platform\\asyncio.py\", line 205, in start\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\asyncio\\base_events.py\", line 641, in run_forever\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\asyncio\\base_events.py\", line 1987, in _run_once\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\asyncio\\events.py\", line 88, in _run\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 545, in dispatch_queue\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 534, in process_one\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 437, in dispatch_shell\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel\\ipkernel.py\", line 362, in execute_request\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 778, in execute_request\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel\\ipkernel.py\", line 449, in do_execute\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel\\zmqshell.py\", line 549, in run_cell\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3075, in run_cell\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3130, in _run_cell\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\async_helpers.py\", line 129, in _pseudo_sync_runner\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3334, in run_cell_async\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3517, in run_ast_nodes\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3577, in run_code\n\n  File \"C:\\Users\\lhdto\\AppData\\Local\\Temp\\ipykernel_11284\\3536403359.py\", line 1, in \n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 117, in error_handler\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\backend\\tensorflow\\trainer.py\", line 329, in fit\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\backend\\tensorflow\\trainer.py\", line 122, in one_step_on_iterator\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\backend\\tensorflow\\trainer.py\", line 110, in one_step_on_data\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\backend\\tensorflow\\trainer.py\", line 57, in train_step\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 117, in error_handler\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\layers\\layer.py\", line 826, in __call__\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 117, in error_handler\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\ops\\operation.py\", line 48, in __call__\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 156, in error_handler\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\models\\sequential.py\", line 206, in call\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\models\\functional.py\", line 199, in call\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\ops\\function.py\", line 151, in _run_through_graph\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\models\\functional.py\", line 583, in call\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 117, in error_handler\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\layers\\layer.py\", line 826, in __call__\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 117, in error_handler\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\ops\\operation.py\", line 48, in __call__\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 156, in error_handler\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\layers\\rnn\\time_distributed.py\", line 108, in call\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\layers\\rnn\\time_distributed.py\", line 102, in step_function\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\layers\\reshaping\\flatten.py\", line 54, in call\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\ops\\numpy.py\", line 4527, in reshape\n\n  File \"c:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\backend\\tensorflow\\numpy.py\", line 1618, in reshape\n\nOnly one input size may be -1, not both 0 and 1\n\t [[{{node sequential_1/time_distributed_1/Reshape_17}}]] [Op:__inference_one_step_on_iterator_34683]"
+          ]
+        }
+      ],
+      "source": [
+        "model.fit(train, validation_data=test, epochs=100, callbacks=[checkpoint_callback, schedule_callback, example_callback])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "fa8ee94b-89f7-4733-8a0c-a86f86ff590a",
+      "metadata": {
+        "id": "fa8ee94b-89f7-4733-8a0c-a86f86ff590a",
+        "tags": []
+      },
+      "source": [
+        "# 5. Make a Prediction"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "01fa7204-ce0e-49a8-8dbd-14fe5dfead40",
+      "metadata": {
+        "id": "01fa7204-ce0e-49a8-8dbd-14fe5dfead40",
+        "tags": []
+      },
+      "outputs": [],
+      "source": [
+        "# url = 'https://drive.google.com/uc?id=1vWscXs4Vt0a_1IH1-ct2TCgXAZT-N3_Y'\n",
+        "# output = 'checkpoints.zip'\n",
+        "# gdown.download(url, output, quiet=False)\n",
+        "# gdown.extractall('checkpoints.zip', 'models')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 51,
+      "id": "247f664d-3c87-4e96-946e-930dad0e1c2c",
+      "metadata": {
+        "id": "247f664d-3c87-4e96-946e-930dad0e1c2c",
+        "tags": []
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "models\\unseen-weights178.h5\n"
+          ]
+        },
+        {
+          "ename": "ValueError",
+          "evalue": "Layer count mismatch when loading weights from file. Model expected 7 layers, found 9 saved layers.",
+          "output_type": "error",
+          "traceback": [
+            "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+            "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
+            "Cell \u001b[1;32mIn[51], line 3\u001b[0m\n\u001b[0;32m      1\u001b[0m checkpoint_path \u001b[38;5;241m=\u001b[39m os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mjoin(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmodels\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124munseen-weights178.h5\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m      2\u001b[0m \u001b[38;5;28mprint\u001b[39m(checkpoint_path)\n\u001b[1;32m----> 3\u001b[0m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mload_weights\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcheckpoint_path\u001b[49m\u001b[43m)\u001b[49m\n",
+            "File \u001b[1;32mc:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\utils\\traceback_utils.py:122\u001b[0m, in \u001b[0;36mfilter_traceback..error_handler\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    119\u001b[0m     filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n\u001b[0;32m    120\u001b[0m     \u001b[38;5;66;03m# To get the full stack trace, call:\u001b[39;00m\n\u001b[0;32m    121\u001b[0m     \u001b[38;5;66;03m# `keras.config.disable_traceback_filtering()`\u001b[39;00m\n\u001b[1;32m--> 122\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m e\u001b[38;5;241m.\u001b[39mwith_traceback(filtered_tb) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m    123\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[0;32m    124\u001b[0m     \u001b[38;5;28;01mdel\u001b[39;00m filtered_tb\n",
+            "File \u001b[1;32mc:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\legacy\\saving\\legacy_h5_format.py:357\u001b[0m, in \u001b[0;36mload_weights_from_hdf5_group\u001b[1;34m(f, model)\u001b[0m\n\u001b[0;32m    355\u001b[0m layer_names \u001b[38;5;241m=\u001b[39m filtered_layer_names\n\u001b[0;32m    356\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(layer_names) \u001b[38;5;241m!=\u001b[39m \u001b[38;5;28mlen\u001b[39m(filtered_layers):\n\u001b[1;32m--> 357\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m    358\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mLayer count mismatch when loading weights from file. \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    359\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mModel expected \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlen\u001b[39m(filtered_layers)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m layers, found \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    360\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlen\u001b[39m(layer_names)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m saved layers.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    361\u001b[0m     )\n\u001b[0;32m    363\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m k, name \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(layer_names):\n\u001b[0;32m    364\u001b[0m     g \u001b[38;5;241m=\u001b[39m f[name]\n",
+            "\u001b[1;31mValueError\u001b[0m: Layer count mismatch when loading weights from file. Model expected 7 layers, found 9 saved layers."
+          ]
+        }
+      ],
+      "source": [
+        "checkpoint_path = os.path.join('models', 'unseen-weights178.h5')\n",
+        "print(checkpoint_path)\n",
+        "model.load_weights(checkpoint_path)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "7f8d689f-b7bb-443c-9b88-e40c1d800828",
+      "metadata": {
+        "id": "7f8d689f-b7bb-443c-9b88-e40c1d800828",
+        "tags": []
+      },
+      "outputs": [],
+      "source": [
+        "test_data = test.as_numpy_iterator()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "38546dc2-bee9-4837-864b-8a884df40ad7",
+      "metadata": {
+        "id": "38546dc2-bee9-4837-864b-8a884df40ad7",
+        "tags": []
+      },
+      "outputs": [],
+      "source": [
+        "sample = test_data.next()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "a43621f0-229d-4c0d-9554-9c3a3da9c61a",
+      "metadata": {
+        "id": "a43621f0-229d-4c0d-9554-9c3a3da9c61a",
+        "tags": []
+      },
+      "outputs": [],
+      "source": [
+        "yhat = model.predict(sample[0])"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "ea462999-f87e-4a7e-a057-5be7b6d8f7d5",
+      "metadata": {
+        "id": "ea462999-f87e-4a7e-a057-5be7b6d8f7d5",
+        "tags": []
+      },
+      "outputs": [],
+      "source": [
+        "print('~'*100, 'REAL TEXT')\n",
+        "[tf.strings.reduce_join([num_to_char(word) for word in sentence]) for sentence in sample[1]]"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "82bd4c10-dd6e-411e-834b-2a3b43fd12c5",
+      "metadata": {
+        "id": "82bd4c10-dd6e-411e-834b-2a3b43fd12c5",
+        "tags": []
+      },
+      "outputs": [],
+      "source": [
+        "decoded = tf.keras.backend.ctc_decode(yhat, input_length=[75,75], greedy=True)[0][0].numpy()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "5d68ac46-c90b-4eab-a709-f19aee569ff5",
+      "metadata": {
+        "id": "5d68ac46-c90b-4eab-a709-f19aee569ff5",
+        "tags": []
+      },
+      "outputs": [],
+      "source": [
+        "print('~'*100, 'PREDICTIONS')\n",
+        "[tf.strings.reduce_join([num_to_char(word) for word in sentence]) for sentence in decoded]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "64622f98-e99b-4fed-a2cc-f0da82eb5431",
+      "metadata": {
+        "id": "64622f98-e99b-4fed-a2cc-f0da82eb5431"
+      },
+      "source": [
+        "# Test on a Video"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "a8b0c4d0-2031-4331-b91d-d87b1ae6f6e2",
+      "metadata": {
+        "id": "a8b0c4d0-2031-4331-b91d-d87b1ae6f6e2"
+      },
+      "outputs": [],
+      "source": [
+        "sample = load_data(tf.convert_to_tensor('.\\\\data\\\\s1\\\\bras9a.mpg'))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "0cca60e4-47a9-4683-8a75-48f4684f723d",
+      "metadata": {
+        "id": "0cca60e4-47a9-4683-8a75-48f4684f723d"
+      },
+      "outputs": [],
+      "source": [
+        "print('~'*100, 'REAL TEXT')\n",
+        "[tf.strings.reduce_join([num_to_char(word) for word in sentence]) for sentence in [sample[1]]]"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "8cc5037c-1e32-435c-b0cc-01e1fb3b863c",
+      "metadata": {
+        "id": "8cc5037c-1e32-435c-b0cc-01e1fb3b863c"
+      },
+      "outputs": [],
+      "source": [
+        "yhat = model.predict(tf.expand_dims(sample[0], axis=0))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "22c4f77d-715d-409f-bc5e-3ebe48704e8f",
+      "metadata": {
+        "id": "22c4f77d-715d-409f-bc5e-3ebe48704e8f"
+      },
+      "outputs": [],
+      "source": [
+        "# decoded = tf.keras.backend.ctc_decode(yhat, input_length=[75], greedy=True)[0][0].numpy()\n",
+        "decoded = keras.backend.ctc_decode(yhat, input_length=[75], greedy=True)[0][0].numpy()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "e4d12ecc-b634-499e-a4bc-db9f010835fb",
+      "metadata": {
+        "id": "e4d12ecc-b634-499e-a4bc-db9f010835fb"
+      },
+      "outputs": [],
+      "source": [
+        "print('~'*100, 'PREDICTIONS')\n",
+        "[tf.strings.reduce_join([num_to_char(word) for word in sentence]) for sentence in decoded]"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "551dfea2-de6b-4400-b71a-a17631529e3f",
+      "metadata": {
+        "id": "551dfea2-de6b-4400-b71a-a17631529e3f"
+      },
+      "outputs": [],
+      "source": []
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "fa95863d-3832-47bf-8a77-ebaa38054ace",
+      "metadata": {
+        "id": "fa95863d-3832-47bf-8a77-ebaa38054ace"
+      },
+      "outputs": [],
+      "source": []
+    }
+  ],
+  "metadata": {
+    "accelerator": "GPU",
+    "colab": {
+      "gpuType": "T4",
+      "include_colab_link": true,
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.12.3"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 5
+}
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HcmV?d00001


From 2cde8a3c009375c2d78d5db4a8337bc201cc5ba7 Mon Sep 17 00:00:00 2001
From: Haidun Li 
Date: Fri, 21 Jun 2024 13:52:58 -0300
Subject: [PATCH 12/17] Refactor modelutil.py: Import Orthogonal initializer
 and update LSTM layers

---
 LipNet.ipynb    | 4 ++--
 LipNet_my.ipynb | 4 ++--
 2 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/LipNet.ipynb b/LipNet.ipynb
index 628862ec..268e12e5 100644
--- a/LipNet.ipynb
+++ b/LipNet.ipynb
@@ -194,7 +194,7 @@
     }
    ],
    "source": [
-    "!pip list"
+    "!pip3 list"
    ]
   },
   {
@@ -268,7 +268,7 @@
     }
    ],
    "source": [
-    "!pip install opencv-python matplotlib imageio gdown tensorflow"
+    "!pip3 install opencv-python matplotlib imageio gdown tensorflow"
    ]
   },
   {
diff --git a/LipNet_my.ipynb b/LipNet_my.ipynb
index 0141a560..ac240332 100755
--- a/LipNet_my.ipynb
+++ b/LipNet_my.ipynb
@@ -209,7 +209,7 @@
         }
       ],
       "source": [
-        "!pip list"
+        "!pip3 list"
       ]
     },
     {
@@ -292,7 +292,7 @@
         }
       ],
       "source": [
-        "!pip install opencv-python matplotlib imageio gdown tensorflow"
+        "!pip3 install opencv-python matplotlib imageio gdown tensorflow"
       ]
     },
     {

From e87ba91bec368b610116ba3e007237725246df30 Mon Sep 17 00:00:00 2001
From: Haidun Li 
Date: Fri, 21 Jun 2024 14:03:51 -0300
Subject: [PATCH 13/17] Refactor modelutil.py: Import Orthogonal initializer
 and update LSTM layers

---
 LipNet.ipynb | 3158 +++++++++++++++++++++++++++++---------------------
 1 file changed, 1835 insertions(+), 1323 deletions(-)

diff --git a/LipNet.ipynb b/LipNet.ipynb
index 268e12e5..1722d245 100644
--- a/LipNet.ipynb
+++ b/LipNet.ipynb
@@ -12,7 +12,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 158,
+   "execution_count": 1,
    "id": "ddfbccbe-41ae-4c23-98b1-a13868e2b499",
    "metadata": {
     "scrolled": true,
@@ -23,173 +23,87 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Package                   Version\n",
-      "------------------------- --------------\n",
-      "absl-py                   2.1.0\n",
-      "altair                    5.3.0\n",
-      "anyio                     4.3.0\n",
-      "argon2-cffi               23.1.0\n",
-      "argon2-cffi-bindings      21.2.0\n",
-      "arrow                     1.3.0\n",
-      "asttokens                 2.4.1\n",
-      "astunparse                1.6.3\n",
-      "async-lru                 2.0.4\n",
-      "attrs                     23.2.0\n",
-      "av                        12.0.0\n",
-      "Babel                     2.14.0\n",
-      "beautifulsoup4            4.12.3\n",
-      "bleach                    6.1.0\n",
-      "blinker                   1.7.0\n",
-      "cachetools                5.3.3\n",
-      "certifi                   2024.2.2\n",
-      "cffi                      1.16.0\n",
-      "charset-normalizer        3.3.2\n",
-      "click                     8.1.7\n",
-      "colorama                  0.4.6\n",
-      "comm                      0.2.2\n",
-      "contourpy                 1.2.1\n",
-      "cycler                    0.12.1\n",
-      "debugpy                   1.8.1\n",
-      "decorator                 5.1.1\n",
-      "defusedxml                0.7.1\n",
-      "executing                 2.0.1\n",
-      "fastjsonschema            2.19.1\n",
-      "filelock                  3.13.4\n",
-      "flatbuffers               24.3.25\n",
-      "fonttools                 4.51.0\n",
-      "fqdn                      1.5.1\n",
-      "fsspec                    2024.3.1\n",
-      "gast                      0.5.4\n",
-      "gdown                     5.1.0\n",
-      "gitdb                     4.0.11\n",
-      "GitPython                 3.1.43\n",
-      "google-pasta              0.2.0\n",
-      "grpcio                    1.62.1\n",
-      "h11                       0.14.0\n",
-      "h5py                      3.11.0\n",
-      "httpcore                  1.0.5\n",
-      "httpx                     0.27.0\n",
-      "idna                      3.7\n",
-      "imageio                   2.23.0\n",
-      "ipykernel                 6.29.4\n",
-      "ipynb-py-convert          0.4.6\n",
-      "ipython                   8.23.0\n",
-      "ipywidgets                8.1.2\n",
-      "isoduration               20.11.0\n",
-      "jedi                      0.19.1\n",
-      "Jinja2                    3.1.3\n",
-      "json5                     0.9.24\n",
-      "jsonpointer               2.4\n",
-      "jsonschema                4.21.1\n",
-      "jsonschema-specifications 2023.12.1\n",
-      "jupyter                   1.0.0\n",
-      "jupyter_client            8.6.1\n",
-      "jupyter-console           6.6.3\n",
-      "jupyter_core              5.7.2\n",
-      "jupyter-events            0.10.0\n",
-      "jupyter-lsp               2.2.5\n",
-      "jupyter_server            2.13.0\n",
-      "jupyter_server_terminals  0.5.3\n",
-      "jupyterlab                4.1.6\n",
-      "jupyterlab_pygments       0.3.0\n",
-      "jupyterlab_server         2.26.0\n",
-      "jupyterlab_widgets        3.0.10\n",
-      "keras                     3.2.1\n",
-      "kiwisolver                1.4.5\n",
-      "libclang                  18.1.1\n",
-      "Markdown                  3.6\n",
-      "markdown-it-py            3.0.0\n",
-      "MarkupSafe                2.1.5\n",
-      "matplotlib                3.8.4\n",
-      "matplotlib-inline         0.1.6\n",
-      "mdurl                     0.1.2\n",
-      "mistune                   3.0.2\n",
-      "ml-dtypes                 0.3.2\n",
-      "mpmath                    1.3.0\n",
-      "namex                     0.0.7\n",
-      "nbclient                  0.10.0\n",
-      "nbconvert                 7.16.3\n",
-      "nbformat                  5.10.4\n",
-      "nest-asyncio              1.6.0\n",
-      "networkx                  3.3\n",
-      "notebook                  7.1.2\n",
-      "notebook_shim             0.2.4\n",
-      "numpy                     1.26.4\n",
-      "opencv-python             4.6.0.66\n",
-      "opt-einsum                3.3.0\n",
-      "optree                    0.11.0\n",
-      "overrides                 7.7.0\n",
-      "packaging                 24.0\n",
-      "pandas                    2.2.1\n",
-      "pandocfilters             1.5.1\n",
-      "parso                     0.8.4\n",
-      "pillow                    10.3.0\n",
-      "pip                       24.0\n",
-      "platformdirs              4.2.0\n",
-      "prometheus_client         0.20.0\n",
-      "prompt-toolkit            3.0.43\n",
-      "protobuf                  4.25.3\n",
-      "psutil                    5.9.8\n",
-      "pure-eval                 0.2.2\n",
-      "pyarrow                   15.0.2\n",
-      "pycparser                 2.22\n",
-      "pydeck                    0.8.1b0\n",
-      "Pygments                  2.17.2\n",
-      "pyparsing                 3.1.2\n",
-      "PySocks                   1.7.1\n",
-      "python-dateutil           2.9.0.post0\n",
-      "python-json-logger        2.0.7\n",
-      "pytz                      2024.1\n",
-      "pywin32                   306\n",
-      "pywinpty                  2.0.13\n",
-      "PyYAML                    6.0.1\n",
-      "pyzmq                     25.1.2\n",
-      "qtconsole                 5.5.1\n",
-      "QtPy                      2.4.1\n",
-      "referencing               0.34.0\n",
-      "requests                  2.31.0\n",
-      "rfc3339-validator         0.1.4\n",
-      "rfc3986-validator         0.1.1\n",
-      "rich                      13.7.1\n",
-      "rpds-py                   0.18.0\n",
-      "Send2Trash                1.8.3\n",
-      "setuptools                69.2.0\n",
-      "six                       1.16.0\n",
-      "smmap                     5.0.1\n",
-      "sniffio                   1.3.1\n",
-      "soupsieve                 2.5\n",
-      "stack-data                0.6.3\n",
-      "streamlit                 1.33.0\n",
-      "sympy                     1.12\n",
-      "tenacity                  8.2.3\n",
-      "tensorboard               2.16.2\n",
-      "tensorboard-data-server   0.7.2\n",
-      "tensorflow                2.16.1\n",
-      "tensorflow-intel          2.16.1\n",
-      "termcolor                 2.4.0\n",
-      "terminado                 0.18.1\n",
-      "tinycss2                  1.2.1\n",
-      "toml                      0.10.2\n",
-      "toolz                     0.12.1\n",
-      "torch                     2.2.2\n",
-      "torchvision               0.17.2\n",
-      "tornado                   6.4\n",
-      "tqdm                      4.66.2\n",
-      "traitlets                 5.14.2\n",
-      "types-python-dateutil     2.9.0.20240316\n",
-      "typing_extensions         4.11.0\n",
-      "tzdata                    2024.1\n",
-      "uri-template              1.3.0\n",
-      "urllib3                   2.2.1\n",
-      "watchdog                  4.0.0\n",
-      "wcwidth                   0.2.13\n",
-      "webcolors                 1.13\n",
-      "webencodings              0.5.1\n",
-      "websocket-client          1.7.0\n",
-      "Werkzeug                  3.0.2\n",
-      "wheel                     0.43.0\n",
-      "widgetsnbextension        4.0.10\n",
-      "wrapt                     1.16.0\n"
+      "Package                 Version\n",
+      "----------------------- -----------\n",
+      "absl-py                 2.1.0\n",
+      "appnope                 0.1.4\n",
+      "asttokens               2.4.1\n",
+      "astunparse              1.6.3\n",
+      "beautifulsoup4          4.12.3\n",
+      "certifi                 2024.6.2\n",
+      "charset-normalizer      3.3.2\n",
+      "comm                    0.2.2\n",
+      "contourpy               1.2.1\n",
+      "cycler                  0.12.1\n",
+      "debugpy                 1.8.1\n",
+      "decorator               5.1.1\n",
+      "executing               2.0.1\n",
+      "filelock                3.15.3\n",
+      "flatbuffers             24.3.25\n",
+      "fonttools               4.53.0\n",
+      "gast                    0.5.4\n",
+      "gdown                   5.2.0\n",
+      "google-pasta            0.2.0\n",
+      "grpcio                  1.64.1\n",
+      "h5py                    3.11.0\n",
+      "idna                    3.7\n",
+      "imageio                 2.34.1\n",
+      "ipykernel               6.29.4\n",
+      "ipython                 8.25.0\n",
+      "jedi                    0.19.1\n",
+      "jupyter_client          8.6.2\n",
+      "jupyter_core            5.7.2\n",
+      "keras                   3.3.3\n",
+      "kiwisolver              1.4.5\n",
+      "libclang                18.1.1\n",
+      "Markdown                3.6\n",
+      "markdown-it-py          3.0.0\n",
+      "MarkupSafe              2.1.5\n",
+      "matplotlib              3.9.0\n",
+      "matplotlib-inline       0.1.7\n",
+      "mdurl                   0.1.2\n",
+      "ml-dtypes               0.3.2\n",
+      "namex                   0.0.8\n",
+      "nest-asyncio            1.6.0\n",
+      "numpy                   1.26.4\n",
+      "opencv-python           4.10.0.84\n",
+      "opt-einsum              3.3.0\n",
+      "optree                  0.11.0\n",
+      "packaging               24.1\n",
+      "parso                   0.8.4\n",
+      "pexpect                 4.9.0\n",
+      "pillow                  10.3.0\n",
+      "pip                     24.1\n",
+      "platformdirs            4.2.2\n",
+      "prompt_toolkit          3.0.47\n",
+      "protobuf                4.25.3\n",
+      "psutil                  6.0.0\n",
+      "ptyprocess              0.7.0\n",
+      "pure-eval               0.2.2\n",
+      "Pygments                2.18.0\n",
+      "pyparsing               3.1.2\n",
+      "PySocks                 1.7.1\n",
+      "python-dateutil         2.9.0.post0\n",
+      "pyzmq                   26.0.3\n",
+      "requests                2.32.3\n",
+      "rich                    13.7.1\n",
+      "setuptools              70.1.0\n",
+      "six                     1.16.0\n",
+      "soupsieve               2.5\n",
+      "stack-data              0.6.3\n",
+      "tensorboard             2.16.2\n",
+      "tensorboard-data-server 0.7.2\n",
+      "tensorflow              2.16.1\n",
+      "termcolor               2.4.0\n",
+      "tornado                 6.4.1\n",
+      "tqdm                    4.66.4\n",
+      "traitlets               5.14.3\n",
+      "typing_extensions       4.12.2\n",
+      "urllib3                 2.2.2\n",
+      "wcwidth                 0.2.13\n",
+      "Werkzeug                3.0.3\n",
+      "wheel                   0.43.0\n",
+      "wrapt                   1.16.0\n"
      ]
     }
    ],
@@ -199,7 +113,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 159,
+   "execution_count": 2,
    "id": "02f907ea-f669-46c7-adcf-7f257e663448",
    "metadata": {
     "tags": []
@@ -209,61 +123,59 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Requirement already satisfied: opencv-python in c:\\users\\lhdto\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (4.6.0.66)\n",
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+      "Requirement already satisfied: certifi>=2017.4.17 in ./.venv/lib/python3.12/site-packages (from requests[socks]->gdown) (2024.6.2)\n",
+      "Requirement already satisfied: markdown>=2.6.8 in ./.venv/lib/python3.12/site-packages (from tensorboard<2.17,>=2.16->tensorflow) (3.6)\n",
+      "Requirement already satisfied: tensorboard-data-server<0.8.0,>=0.7.0 in ./.venv/lib/python3.12/site-packages (from tensorboard<2.17,>=2.16->tensorflow) (0.7.2)\n",
+      "Requirement already satisfied: werkzeug>=1.0.1 in ./.venv/lib/python3.12/site-packages (from tensorboard<2.17,>=2.16->tensorflow) (3.0.3)\n",
+      "Requirement already satisfied: soupsieve>1.2 in ./.venv/lib/python3.12/site-packages (from beautifulsoup4->gdown) (2.5)\n",
+      "Requirement already satisfied: PySocks!=1.5.7,>=1.5.6 in ./.venv/lib/python3.12/site-packages (from requests[socks]->gdown) (1.7.1)\n",
+      "Requirement already satisfied: MarkupSafe>=2.1.1 in ./.venv/lib/python3.12/site-packages (from werkzeug>=1.0.1->tensorboard<2.17,>=2.16->tensorflow) (2.1.5)\n",
+      "Requirement already satisfied: markdown-it-py>=2.2.0 in ./.venv/lib/python3.12/site-packages (from rich->keras>=3.0.0->tensorflow) (3.0.0)\n",
+      "Requirement already satisfied: pygments<3.0.0,>=2.13.0 in ./.venv/lib/python3.12/site-packages (from rich->keras>=3.0.0->tensorflow) (2.18.0)\n",
+      "Requirement already satisfied: mdurl~=0.1 in ./.venv/lib/python3.12/site-packages (from markdown-it-py>=2.2.0->rich->keras>=3.0.0->tensorflow) (0.1.2)\n"
      ]
     }
    ],
@@ -273,12 +185,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 160,
+   "execution_count": 3,
    "id": "b24af50c-20b8-409d-ad78-30a933fdd669",
    "metadata": {
     "tags": []
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Matplotlib is building the font cache; this may take a moment.\n"
+     ]
+    }
+   ],
    "source": [
     "import os\n",
     "import cv2\n",
@@ -291,7 +211,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 161,
+   "execution_count": 4,
    "id": "1e3db0b0-e559-4ad6-91fd-e7414b7d75e6",
    "metadata": {},
    "outputs": [
@@ -301,7 +221,7 @@
        "[]"
       ]
      },
-     "execution_count": 161,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -312,7 +232,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 162,
+   "execution_count": 5,
    "id": "378d045a-3003-4f93-b7d2-a25a97774a68",
    "metadata": {
     "tags": []
@@ -338,7 +258,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 163,
+   "execution_count": 6,
    "id": "8fb99c90-e05a-437f-839d-6e772f8c1dd5",
    "metadata": {
     "tags": []
@@ -350,22 +270,1044 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 164,
+   "execution_count": 19,
    "id": "c019e4c6-2af3-4160-99ea-5c8cb009f1a7",
    "metadata": {
     "tags": []
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Downloading...\n",
+      "From (original): https://drive.google.com/uc?id=1YlvpDLix3S-U8fd-gqRwPcWXAXm8JwjL\n",
+      "From (redirected): https://drive.google.com/uc?id=1YlvpDLix3S-U8fd-gqRwPcWXAXm8JwjL&confirm=t&uuid=54f6d0f0-0825-45b2-922d-a48e87467e61\n",
+      "To: /Users/lihaidun/NSCC/CodeSpace/LipNet/data.zip\n",
+      "100%|██████████| 423M/423M [00:37<00:00, 11.2MB/s] \n"
+     ]
+    },
+    {
+     "data": {
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+       " 'data/alignments/s1/swiu6p.align',\n",
+       " 'data/alignments/s1/swiu7a.align',\n",
+       " 'data/alignments/s1/swwc4n.align',\n",
+       " 'data/alignments/s1/swwc5s.align',\n",
+       " 'data/alignments/s1/swwc6p.align',\n",
+       " 'data/alignments/s1/swwc7a.align',\n",
+       " 'data/alignments/s1/swwi8n.align',\n",
+       " 'data/alignments/s1/swwi9s.align',\n",
+       " 'data/alignments/s1/swwj1a.align',\n",
+       " 'data/alignments/s1/swwjzp.align',\n",
+       " 'data/alignments/s1/swwp2n.align',\n",
+       " 'data/alignments/s1/swwp3s.align',\n",
+       " 'data/alignments/s1/swwp4p.align',\n",
+       " 'data/alignments/s1/swwp5a.align',\n",
+       " 'data/alignments/s1/swwv6n.align',\n",
+       " ...]"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# url = 'https://drive.google.com/uc?id=1YlvpDLix3S-U8fd-gqRwPcWXAXm8JwjL'\n",
-    "# output = 'data.zip'\n",
-    "# gdown.download(url, output, quiet=False)\n",
-    "# gdown.extractall('data.zip')"
+    "url = 'https://drive.google.com/uc?id=1YlvpDLix3S-U8fd-gqRwPcWXAXm8JwjL'\n",
+    "output = 'data.zip'\n",
+    "gdown.download(url, output, quiet=False)\n",
+    "gdown.extractall('data.zip')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 165,
+   "execution_count": 20,
    "id": "8548cc59-6dfc-4acc-abc3-3e65212db02e",
    "metadata": {
     "tags": []
@@ -389,7 +1331,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 166,
+   "execution_count": 21,
    "id": "ec735e0b-ec98-4eb0-8f49-c35527d6670a",
    "metadata": {
     "tags": []
@@ -401,7 +1343,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 167,
+   "execution_count": 22,
    "id": "be04e972-d7a5-4a72-82d8-a6bdde1f3ce6",
    "metadata": {
     "tags": []
@@ -429,7 +1371,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 168,
+   "execution_count": 23,
    "id": "559f7420-6802-45fa-9ca0-b1ff209b461c",
    "metadata": {
     "tags": []
@@ -480,7 +1422,7 @@
        " ' ']"
       ]
      },
-     "execution_count": 168,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -491,7 +1433,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 169,
+   "execution_count": 24,
    "id": "797ff78b-b48f-4e14-bb62-8cd0ebf9501a",
    "metadata": {
     "tags": []
@@ -500,10 +1442,10 @@
     {
      "data": {
       "text/plain": [
-       ""
+       ""
       ]
      },
-     "execution_count": 169,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -514,7 +1456,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 170,
+   "execution_count": 25,
    "id": "8cd7f4f4-ae77-4509-a4f4-c723787ebad1",
    "metadata": {},
    "outputs": [
@@ -524,7 +1466,7 @@
        ""
       ]
      },
-     "execution_count": 170,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -535,7 +1477,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 171,
+   "execution_count": 26,
    "id": "9491bab5-6a3c-4f79-879a-8f9fbe73ae2e",
    "metadata": {
     "tags": []
@@ -555,7 +1497,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 172,
+   "execution_count": 27,
    "id": "dd01ca9f-77fb-4643-a2aa-47dd82c5d66b",
    "metadata": {
     "tags": []
@@ -577,19 +1519,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 173,
+   "execution_count": 43,
    "id": "8cb7cc58-31ae-4904-a805-1177a82717d2",
    "metadata": {
     "tags": []
    },
    "outputs": [],
    "source": [
+    "import os\n",
+    "\n",
+    "pwd = os.getcwd()\n",
+    "\n",
+    "# test_path = os.path.join(pwd,'data','s1','bbal6n.mpg')\n",
+    "\n",
     "test_path = '.\\\\data\\\\s1\\\\bbal6n.mpg'"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 174,
+   "execution_count": 44,
    "id": "76aa964f-0c84-490d-897a-d00e3966e2c9",
    "metadata": {},
    "outputs": [
@@ -599,7 +1547,7 @@
        "'bbal6n'"
       ]
      },
-     "execution_count": 174,
+     "execution_count": 44,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -610,7 +1558,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 175,
+   "execution_count": 45,
    "id": "eb602c71-8560-4f9e-b26b-08202febb937",
    "metadata": {
     "scrolled": true,
@@ -623,23 +1571,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 176,
+   "execution_count": 46,
    "id": "0e3184a1-6b02-4b4f-84a8-a0a65f951ea2",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       ""
+       ""
       ]
      },
-     "execution_count": 176,
+     "execution_count": 46,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "
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       ]
@@ -654,7 +1602,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 177,
+   "execution_count": 47,
    "id": "d7ec0833-d54b-4073-84cf-92d011c60ec1",
    "metadata": {},
    "outputs": [
@@ -663,10 +1611,10 @@
       "text/plain": [
        ""
+       "       39, 14, 15, 23])>"
       ]
      },
-     "execution_count": 177,
+     "execution_count": 47,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -677,7 +1625,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 178,
+   "execution_count": 48,
    "id": "fe1ad370-b287-4b46-85a2-7c45b0bd9b10",
    "metadata": {},
    "outputs": [
@@ -687,7 +1635,7 @@
        ""
       ]
      },
-     "execution_count": 178,
+     "execution_count": 48,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -698,7 +1646,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 179,
+   "execution_count": 49,
    "id": "6871031a-b0ba-4c76-a852-f6329b0f2606",
    "metadata": {
     "tags": []
@@ -722,7 +1670,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 180,
+   "execution_count": 50,
    "id": "7686355d-45aa-4c85-ad9c-053e6a9b4d81",
    "metadata": {
     "tags": []
@@ -734,7 +1682,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 181,
+   "execution_count": 51,
    "id": "f066fea2-91b1-42ed-a67d-00566a1a53ff",
    "metadata": {
     "tags": []
@@ -753,7 +1701,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 182,
+   "execution_count": 52,
    "id": "6b1365bd-7742-41d1-95d4-247021751c3a",
    "metadata": {},
    "outputs": [
@@ -763,7 +1711,7 @@
        "50"
       ]
      },
-     "execution_count": 182,
+     "execution_count": 52,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -774,27 +1722,156 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 183,
+   "execution_count": 53,
    "id": "5281bde8-fdc8-4da1-bd55-5a7929a9e80c",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "OpenCV: Couldn't read video stream from file \"data/s1/.mpg\"\n",
+      "2024-06-21 14:03:10.432204: W tensorflow/core/framework/op_kernel.cc:1827] UNKNOWN: FileNotFoundError: [Errno 2] No such file or directory: 'data/alignments/s1/.align'\n",
+      "Traceback (most recent call last):\n",
+      "\n",
+      "  File \"/Users/lihaidun/NSCC/CodeSpace/LipNet/.venv/lib/python3.12/site-packages/tensorflow/python/ops/script_ops.py\", line 268, in __call__\n",
+      "    return func(device, token, args)\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "\n",
+      "  File \"/Users/lihaidun/NSCC/CodeSpace/LipNet/.venv/lib/python3.12/site-packages/tensorflow/python/ops/script_ops.py\", line 146, in __call__\n",
+      "    outputs = self._call(device, args)\n",
+      "              ^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "\n",
+      "  File \"/Users/lihaidun/NSCC/CodeSpace/LipNet/.venv/lib/python3.12/site-packages/tensorflow/python/ops/script_ops.py\", line 153, in _call\n",
+      "    ret = self._func(*args)\n",
+      "          ^^^^^^^^^^^^^^^^^\n",
+      "\n",
+      "  File \"/Users/lihaidun/NSCC/CodeSpace/LipNet/.venv/lib/python3.12/site-packages/tensorflow/python/autograph/impl/api.py\", line 643, in wrapper\n",
+      "    return func(*args, **kwargs)\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^\n",
+      "\n",
+      "  File \"/var/folders/5y/8wf3zbrx6k91qcb2jgf69fl40000gn/T/ipykernel_80855/1624089498.py\", line 9, in load_data\n",
+      "    alignments = load_alignments(alignment_path)\n",
+      "                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "\n",
+      "  File \"/var/folders/5y/8wf3zbrx6k91qcb2jgf69fl40000gn/T/ipykernel_80855/1342149016.py\", line 2, in load_alignments\n",
+      "    with open(path, 'r') as f:\n",
+      "         ^^^^^^^^^^^^^^^\n",
+      "\n",
+      "  File \"/Users/lihaidun/NSCC/CodeSpace/LipNet/.venv/lib/python3.12/site-packages/IPython/core/interactiveshell.py\", line 324, in _modified_open\n",
+      "    return io_open(file, *args, **kwargs)\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "\n",
+      "FileNotFoundError: [Errno 2] No such file or directory: 'data/alignments/s1/.align'\n",
+      "\n",
+      "\n",
+      "2024-06-21 14:03:10.432297: W tensorflow/core/framework/local_rendezvous.cc:404] Local rendezvous is aborting with status: UNKNOWN: FileNotFoundError: [Errno 2] No such file or directory: 'data/alignments/s1/.align'\n",
+      "Traceback (most recent call last):\n",
+      "\n",
+      "  File \"/Users/lihaidun/NSCC/CodeSpace/LipNet/.venv/lib/python3.12/site-packages/tensorflow/python/ops/script_ops.py\", line 268, in __call__\n",
+      "    return func(device, token, args)\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "\n",
+      "  File \"/Users/lihaidun/NSCC/CodeSpace/LipNet/.venv/lib/python3.12/site-packages/tensorflow/python/ops/script_ops.py\", line 146, in __call__\n",
+      "    outputs = self._call(device, args)\n",
+      "              ^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "\n",
+      "  File \"/Users/lihaidun/NSCC/CodeSpace/LipNet/.venv/lib/python3.12/site-packages/tensorflow/python/ops/script_ops.py\", line 153, in _call\n",
+      "    ret = self._func(*args)\n",
+      "          ^^^^^^^^^^^^^^^^^\n",
+      "\n",
+      "  File \"/Users/lihaidun/NSCC/CodeSpace/LipNet/.venv/lib/python3.12/site-packages/tensorflow/python/autograph/impl/api.py\", line 643, in wrapper\n",
+      "    return func(*args, **kwargs)\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^\n",
+      "\n",
+      "  File \"/var/folders/5y/8wf3zbrx6k91qcb2jgf69fl40000gn/T/ipykernel_80855/1624089498.py\", line 9, in load_data\n",
+      "    alignments = load_alignments(alignment_path)\n",
+      "                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "\n",
+      "  File \"/var/folders/5y/8wf3zbrx6k91qcb2jgf69fl40000gn/T/ipykernel_80855/1342149016.py\", line 2, in load_alignments\n",
+      "    with open(path, 'r') as f:\n",
+      "         ^^^^^^^^^^^^^^^\n",
+      "\n",
+      "  File \"/Users/lihaidun/NSCC/CodeSpace/LipNet/.venv/lib/python3.12/site-packages/IPython/core/interactiveshell.py\", line 324, in _modified_open\n",
+      "    return io_open(file, *args, **kwargs)\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "\n",
+      "FileNotFoundError: [Errno 2] No such file or directory: 'data/alignments/s1/.align'\n",
+      "\n",
+      "\n",
+      "\t [[{{node EagerPyFunc}}]]\n",
+      "OpenCV: Couldn't read video stream from file \"data/s1/.mpg\"\n",
+      "2024-06-21 14:03:10.460937: W tensorflow/core/framework/op_kernel.cc:1827] UNKNOWN: FileNotFoundError: [Errno 2] No such file or directory: 'data/alignments/s1/.align'\n",
+      "Traceback (most recent call last):\n",
+      "\n",
+      "  File \"/Users/lihaidun/NSCC/CodeSpace/LipNet/.venv/lib/python3.12/site-packages/tensorflow/python/ops/script_ops.py\", line 268, in __call__\n",
+      "    return func(device, token, args)\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "\n",
+      "  File \"/Users/lihaidun/NSCC/CodeSpace/LipNet/.venv/lib/python3.12/site-packages/tensorflow/python/ops/script_ops.py\", line 146, in __call__\n",
+      "    outputs = self._call(device, args)\n",
+      "              ^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "\n",
+      "  File \"/Users/lihaidun/NSCC/CodeSpace/LipNet/.venv/lib/python3.12/site-packages/tensorflow/python/ops/script_ops.py\", line 153, in _call\n",
+      "    ret = self._func(*args)\n",
+      "          ^^^^^^^^^^^^^^^^^\n",
+      "\n",
+      "  File \"/Users/lihaidun/NSCC/CodeSpace/LipNet/.venv/lib/python3.12/site-packages/tensorflow/python/autograph/impl/api.py\", line 643, in wrapper\n",
+      "    return func(*args, **kwargs)\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^\n",
+      "\n",
+      "  File \"/var/folders/5y/8wf3zbrx6k91qcb2jgf69fl40000gn/T/ipykernel_80855/1624089498.py\", line 9, in load_data\n",
+      "    alignments = load_alignments(alignment_path)\n",
+      "                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "\n",
+      "  File \"/var/folders/5y/8wf3zbrx6k91qcb2jgf69fl40000gn/T/ipykernel_80855/1342149016.py\", line 2, in load_alignments\n",
+      "    with open(path, 'r') as f:\n",
+      "         ^^^^^^^^^^^^^^^\n",
+      "\n",
+      "  File \"/Users/lihaidun/NSCC/CodeSpace/LipNet/.venv/lib/python3.12/site-packages/IPython/core/interactiveshell.py\", line 324, in _modified_open\n",
+      "    return io_open(file, *args, **kwargs)\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "\n",
+      "FileNotFoundError: [Errno 2] No such file or directory: 'data/alignments/s1/.align'\n",
+      "\n",
+      "\n"
+     ]
+    },
+    {
+     "ename": "UnknownError",
+     "evalue": "{{function_node __wrapped__IteratorGetNext_output_types_2_device_/job:localhost/replica:0/task:0/device:CPU:0}} FileNotFoundError: [Errno 2] No such file or directory: 'data/alignments/s1/.align'\nTraceback (most recent call last):\n\n  File \"/Users/lihaidun/NSCC/CodeSpace/LipNet/.venv/lib/python3.12/site-packages/tensorflow/python/ops/script_ops.py\", line 268, in __call__\n    return func(device, token, args)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^\n\n  File \"/Users/lihaidun/NSCC/CodeSpace/LipNet/.venv/lib/python3.12/site-packages/tensorflow/python/ops/script_ops.py\", line 146, in __call__\n    outputs = self._call(device, args)\n              ^^^^^^^^^^^^^^^^^^^^^^^^\n\n  File \"/Users/lihaidun/NSCC/CodeSpace/LipNet/.venv/lib/python3.12/site-packages/tensorflow/python/ops/script_ops.py\", line 153, in _call\n    ret = self._func(*args)\n          ^^^^^^^^^^^^^^^^^\n\n  File \"/Users/lihaidun/NSCC/CodeSpace/LipNet/.venv/lib/python3.12/site-packages/tensorflow/python/autograph/impl/api.py\", line 643, in wrapper\n    return func(*args, **kwargs)\n           ^^^^^^^^^^^^^^^^^^^^^\n\n  File \"/var/folders/5y/8wf3zbrx6k91qcb2jgf69fl40000gn/T/ipykernel_80855/1624089498.py\", line 9, in load_data\n    alignments = load_alignments(alignment_path)\n                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\n  File \"/var/folders/5y/8wf3zbrx6k91qcb2jgf69fl40000gn/T/ipykernel_80855/1342149016.py\", line 2, in load_alignments\n    with open(path, 'r') as f:\n         ^^^^^^^^^^^^^^^\n\n  File \"/Users/lihaidun/NSCC/CodeSpace/LipNet/.venv/lib/python3.12/site-packages/IPython/core/interactiveshell.py\", line 324, in _modified_open\n    return io_open(file, *args, **kwargs)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\nFileNotFoundError: [Errno 2] No such file or directory: 'data/alignments/s1/.align'\n\n\n\t [[{{node EagerPyFunc}}]] [Op:IteratorGetNext] name: ",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mUnknownError\u001b[0m                              Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[53], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m frames, alignments \u001b[38;5;241m=\u001b[39m \u001b[43mdata\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mas_numpy_iterator\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnext\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/NSCC/CodeSpace/LipNet/.venv/lib/python3.12/site-packages/tensorflow/python/data/ops/dataset_ops.py:4729\u001b[0m, in \u001b[0;36mNumpyIterator.next\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   4728\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mnext\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[0;32m-> 4729\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;21;43m__next__\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/NSCC/CodeSpace/LipNet/.venv/lib/python3.12/site-packages/tensorflow/python/data/ops/dataset_ops.py:4726\u001b[0m, in \u001b[0;36mNumpyIterator.__next__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   4723\u001b[0m     numpy\u001b[38;5;241m.\u001b[39msetflags(write\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[1;32m   4724\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m numpy\n\u001b[0;32m-> 4726\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m nest\u001b[38;5;241m.\u001b[39mmap_structure(to_numpy, \u001b[38;5;28;43mnext\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_iterator\u001b[49m\u001b[43m)\u001b[49m)\n",
+      "File \u001b[0;32m~/NSCC/CodeSpace/LipNet/.venv/lib/python3.12/site-packages/tensorflow/python/data/ops/iterator_ops.py:809\u001b[0m, in \u001b[0;36mOwnedIterator.__next__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    807\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__next__\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m    808\u001b[0m   \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 809\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_next_internal\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    810\u001b[0m   \u001b[38;5;28;01mexcept\u001b[39;00m errors\u001b[38;5;241m.\u001b[39mOutOfRangeError:\n\u001b[1;32m    811\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mStopIteration\u001b[39;00m\n",
+      "File \u001b[0;32m~/NSCC/CodeSpace/LipNet/.venv/lib/python3.12/site-packages/tensorflow/python/data/ops/iterator_ops.py:772\u001b[0m, in \u001b[0;36mOwnedIterator._next_internal\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    769\u001b[0m \u001b[38;5;66;03m# TODO(b/77291417): This runs in sync mode as iterators use an error status\u001b[39;00m\n\u001b[1;32m    770\u001b[0m \u001b[38;5;66;03m# to communicate that there is no more data to iterate over.\u001b[39;00m\n\u001b[1;32m    771\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m context\u001b[38;5;241m.\u001b[39mexecution_mode(context\u001b[38;5;241m.\u001b[39mSYNC):\n\u001b[0;32m--> 772\u001b[0m   ret \u001b[38;5;241m=\u001b[39m \u001b[43mgen_dataset_ops\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43miterator_get_next\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    773\u001b[0m \u001b[43m      \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_iterator_resource\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    774\u001b[0m \u001b[43m      \u001b[49m\u001b[43moutput_types\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_flat_output_types\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    775\u001b[0m \u001b[43m      \u001b[49m\u001b[43moutput_shapes\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_flat_output_shapes\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    777\u001b[0m   \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m    778\u001b[0m     \u001b[38;5;66;03m# Fast path for the case `self._structure` is not a nested structure.\u001b[39;00m\n\u001b[1;32m    779\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_element_spec\u001b[38;5;241m.\u001b[39m_from_compatible_tensor_list(ret)  \u001b[38;5;66;03m# pylint: disable=protected-access\u001b[39;00m\n",
+      "File \u001b[0;32m~/NSCC/CodeSpace/LipNet/.venv/lib/python3.12/site-packages/tensorflow/python/ops/gen_dataset_ops.py:3086\u001b[0m, in \u001b[0;36miterator_get_next\u001b[0;34m(iterator, output_types, output_shapes, name)\u001b[0m\n\u001b[1;32m   3084\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m _result\n\u001b[1;32m   3085\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m _core\u001b[38;5;241m.\u001b[39m_NotOkStatusException \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m-> 3086\u001b[0m   \u001b[43m_ops\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mraise_from_not_ok_status\u001b[49m\u001b[43m(\u001b[49m\u001b[43me\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mname\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   3087\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m _core\u001b[38;5;241m.\u001b[39m_FallbackException:\n\u001b[1;32m   3088\u001b[0m   \u001b[38;5;28;01mpass\u001b[39;00m\n",
+      "File \u001b[0;32m~/NSCC/CodeSpace/LipNet/.venv/lib/python3.12/site-packages/tensorflow/python/framework/ops.py:5983\u001b[0m, in \u001b[0;36mraise_from_not_ok_status\u001b[0;34m(e, name)\u001b[0m\n\u001b[1;32m   5981\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mraise_from_not_ok_status\u001b[39m(e, name) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m NoReturn:\n\u001b[1;32m   5982\u001b[0m   e\u001b[38;5;241m.\u001b[39mmessage \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m (\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m name: \u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mstr\u001b[39m(name \u001b[38;5;28;01mif\u001b[39;00m name \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m\"\u001b[39m))\n\u001b[0;32m-> 5983\u001b[0m   \u001b[38;5;28;01mraise\u001b[39;00m core\u001b[38;5;241m.\u001b[39m_status_to_exception(e) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n",
+      "\u001b[0;31mUnknownError\u001b[0m: {{function_node __wrapped__IteratorGetNext_output_types_2_device_/job:localhost/replica:0/task:0/device:CPU:0}} FileNotFoundError: [Errno 2] No such file or directory: 'data/alignments/s1/.align'\nTraceback (most recent call last):\n\n  File \"/Users/lihaidun/NSCC/CodeSpace/LipNet/.venv/lib/python3.12/site-packages/tensorflow/python/ops/script_ops.py\", line 268, in __call__\n    return func(device, token, args)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^\n\n  File \"/Users/lihaidun/NSCC/CodeSpace/LipNet/.venv/lib/python3.12/site-packages/tensorflow/python/ops/script_ops.py\", line 146, in __call__\n    outputs = self._call(device, args)\n              ^^^^^^^^^^^^^^^^^^^^^^^^\n\n  File \"/Users/lihaidun/NSCC/CodeSpace/LipNet/.venv/lib/python3.12/site-packages/tensorflow/python/ops/script_ops.py\", line 153, in _call\n    ret = self._func(*args)\n          ^^^^^^^^^^^^^^^^^\n\n  File \"/Users/lihaidun/NSCC/CodeSpace/LipNet/.venv/lib/python3.12/site-packages/tensorflow/python/autograph/impl/api.py\", line 643, in wrapper\n    return func(*args, **kwargs)\n           ^^^^^^^^^^^^^^^^^^^^^\n\n  File \"/var/folders/5y/8wf3zbrx6k91qcb2jgf69fl40000gn/T/ipykernel_80855/1624089498.py\", line 9, in load_data\n    alignments = load_alignments(alignment_path)\n                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\n  File \"/var/folders/5y/8wf3zbrx6k91qcb2jgf69fl40000gn/T/ipykernel_80855/1342149016.py\", line 2, in load_alignments\n    with open(path, 'r') as f:\n         ^^^^^^^^^^^^^^^\n\n  File \"/Users/lihaidun/NSCC/CodeSpace/LipNet/.venv/lib/python3.12/site-packages/IPython/core/interactiveshell.py\", line 324, in _modified_open\n    return io_open(file, *args, **kwargs)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\nFileNotFoundError: [Errno 2] No such file or directory: 'data/alignments/s1/.align'\n\n\n\t [[{{node EagerPyFunc}}]] [Op:IteratorGetNext] name: "
+     ]
+    }
+   ],
    "source": [
     "frames, alignments = data.as_numpy_iterator().next()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 184,
+   "execution_count": 54,
    "id": "cbebe683-6afd-47fd-bba4-c83b4b13bb32",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "2"
+       "75"
       ]
      },
-     "execution_count": 184,
+     "execution_count": 54,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -805,7 +1882,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 185,
+   "execution_count": 55,
    "id": "5cf2d676-93a9-434c-b3c7-bdcc2577b2e7",
    "metadata": {
     "tags": []
@@ -817,7 +1894,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 186,
+   "execution_count": 56,
    "id": "efa6cd46-7079-46c0-b45b-832f339f6cb0",
    "metadata": {
     "scrolled": true,
@@ -825,630 +1902,132 @@
    },
    "outputs": [
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-      ]
-     },
-     "execution_count": 186,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "OpenCV: Couldn't read video stream from file \"data/s1/.mpg\"\n",
+      "2024-06-21 14:03:24.295650: W tensorflow/core/framework/op_kernel.cc:1827] UNKNOWN: FileNotFoundError: [Errno 2] No such file or directory: 'data/alignments/s1/.align'\n",
+      "Traceback (most recent call last):\n",
+      "\n",
+      "  File \"/Users/lihaidun/NSCC/CodeSpace/LipNet/.venv/lib/python3.12/site-packages/tensorflow/python/ops/script_ops.py\", line 268, in __call__\n",
+      "    return func(device, token, args)\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "\n",
+      "  File \"/Users/lihaidun/NSCC/CodeSpace/LipNet/.venv/lib/python3.12/site-packages/tensorflow/python/ops/script_ops.py\", line 146, in __call__\n",
+      "    outputs = self._call(device, args)\n",
+      "              ^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "\n",
+      "  File \"/Users/lihaidun/NSCC/CodeSpace/LipNet/.venv/lib/python3.12/site-packages/tensorflow/python/ops/script_ops.py\", line 153, in _call\n",
+      "    ret = self._func(*args)\n",
+      "          ^^^^^^^^^^^^^^^^^\n",
+      "\n",
+      "  File \"/Users/lihaidun/NSCC/CodeSpace/LipNet/.venv/lib/python3.12/site-packages/tensorflow/python/autograph/impl/api.py\", line 643, in wrapper\n",
+      "    return func(*args, **kwargs)\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^\n",
+      "\n",
+      "  File \"/var/folders/5y/8wf3zbrx6k91qcb2jgf69fl40000gn/T/ipykernel_80855/1624089498.py\", line 9, in load_data\n",
+      "    alignments = load_alignments(alignment_path)\n",
+      "                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "\n",
+      "  File \"/var/folders/5y/8wf3zbrx6k91qcb2jgf69fl40000gn/T/ipykernel_80855/1342149016.py\", line 2, in load_alignments\n",
+      "    with open(path, 'r') as f:\n",
+      "         ^^^^^^^^^^^^^^^\n",
+      "\n",
+      "  File \"/Users/lihaidun/NSCC/CodeSpace/LipNet/.venv/lib/python3.12/site-packages/IPython/core/interactiveshell.py\", line 324, in _modified_open\n",
+      "    return io_open(file, *args, **kwargs)\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "\n",
+      "FileNotFoundError: [Errno 2] No such file or directory: 'data/alignments/s1/.align'\n",
+      "\n",
+      "\n",
+      "2024-06-21 14:03:24.295742: W tensorflow/core/framework/local_rendezvous.cc:404] Local rendezvous is aborting with status: UNKNOWN: FileNotFoundError: [Errno 2] No such file or directory: 'data/alignments/s1/.align'\n",
+      "Traceback (most recent call last):\n",
+      "\n",
+      "  File \"/Users/lihaidun/NSCC/CodeSpace/LipNet/.venv/lib/python3.12/site-packages/tensorflow/python/ops/script_ops.py\", line 268, in __call__\n",
+      "    return func(device, token, args)\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "\n",
+      "  File \"/Users/lihaidun/NSCC/CodeSpace/LipNet/.venv/lib/python3.12/site-packages/tensorflow/python/ops/script_ops.py\", line 146, in __call__\n",
+      "    outputs = self._call(device, args)\n",
+      "              ^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "\n",
+      "  File \"/Users/lihaidun/NSCC/CodeSpace/LipNet/.venv/lib/python3.12/site-packages/tensorflow/python/ops/script_ops.py\", line 153, in _call\n",
+      "    ret = self._func(*args)\n",
+      "          ^^^^^^^^^^^^^^^^^\n",
+      "\n",
+      "  File \"/Users/lihaidun/NSCC/CodeSpace/LipNet/.venv/lib/python3.12/site-packages/tensorflow/python/autograph/impl/api.py\", line 643, in wrapper\n",
+      "    return func(*args, **kwargs)\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^\n",
+      "\n",
+      "  File \"/var/folders/5y/8wf3zbrx6k91qcb2jgf69fl40000gn/T/ipykernel_80855/1624089498.py\", line 9, in load_data\n",
+      "    alignments = load_alignments(alignment_path)\n",
+      "                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "\n",
+      "  File \"/var/folders/5y/8wf3zbrx6k91qcb2jgf69fl40000gn/T/ipykernel_80855/1342149016.py\", line 2, in load_alignments\n",
+      "    with open(path, 'r') as f:\n",
+      "         ^^^^^^^^^^^^^^^\n",
+      "\n",
+      "  File \"/Users/lihaidun/NSCC/CodeSpace/LipNet/.venv/lib/python3.12/site-packages/IPython/core/interactiveshell.py\", line 324, in _modified_open\n",
+      "    return io_open(file, *args, **kwargs)\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "\n",
+      "FileNotFoundError: [Errno 2] No such file or directory: 'data/alignments/s1/.align'\n",
+      "\n",
+      "\n",
+      "\t [[{{node EagerPyFunc}}]]\n",
+      "OpenCV: Couldn't read video stream from file \"data/s1/.mpg\"\n",
+      "2024-06-21 14:03:24.308939: W tensorflow/core/framework/op_kernel.cc:1827] UNKNOWN: FileNotFoundError: [Errno 2] No such file or directory: 'data/alignments/s1/.align'\n",
+      "Traceback (most recent call last):\n",
+      "\n",
+      "  File \"/Users/lihaidun/NSCC/CodeSpace/LipNet/.venv/lib/python3.12/site-packages/tensorflow/python/ops/script_ops.py\", line 268, in __call__\n",
+      "    return func(device, token, args)\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "\n",
+      "  File \"/Users/lihaidun/NSCC/CodeSpace/LipNet/.venv/lib/python3.12/site-packages/tensorflow/python/ops/script_ops.py\", line 146, in __call__\n",
+      "    outputs = self._call(device, args)\n",
+      "              ^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "\n",
+      "  File \"/Users/lihaidun/NSCC/CodeSpace/LipNet/.venv/lib/python3.12/site-packages/tensorflow/python/ops/script_ops.py\", line 153, in _call\n",
+      "    ret = self._func(*args)\n",
+      "          ^^^^^^^^^^^^^^^^^\n",
+      "\n",
+      "  File \"/Users/lihaidun/NSCC/CodeSpace/LipNet/.venv/lib/python3.12/site-packages/tensorflow/python/autograph/impl/api.py\", line 643, in wrapper\n",
+      "    return func(*args, **kwargs)\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^\n",
+      "\n",
+      "  File \"/var/folders/5y/8wf3zbrx6k91qcb2jgf69fl40000gn/T/ipykernel_80855/1624089498.py\", line 9, in load_data\n",
+      "    alignments = load_alignments(alignment_path)\n",
+      "                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "\n",
+      "  File \"/var/folders/5y/8wf3zbrx6k91qcb2jgf69fl40000gn/T/ipykernel_80855/1342149016.py\", line 2, in load_alignments\n",
+      "    with open(path, 'r') as f:\n",
+      "         ^^^^^^^^^^^^^^^\n",
+      "\n",
+      "  File \"/Users/lihaidun/NSCC/CodeSpace/LipNet/.venv/lib/python3.12/site-packages/IPython/core/interactiveshell.py\", line 324, in _modified_open\n",
+      "    return io_open(file, *args, **kwargs)\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "\n",
+      "FileNotFoundError: [Errno 2] No such file or directory: 'data/alignments/s1/.align'\n",
+      "\n",
+      "\n"
+     ]
+    },
+    {
+     "ename": "UnknownError",
+     "evalue": "{{function_node __wrapped__IteratorGetNext_output_types_2_device_/job:localhost/replica:0/task:0/device:CPU:0}} FileNotFoundError: [Errno 2] No such file or directory: 'data/alignments/s1/.align'\nTraceback (most recent call last):\n\n  File \"/Users/lihaidun/NSCC/CodeSpace/LipNet/.venv/lib/python3.12/site-packages/tensorflow/python/ops/script_ops.py\", line 268, in __call__\n    return func(device, token, args)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^\n\n  File \"/Users/lihaidun/NSCC/CodeSpace/LipNet/.venv/lib/python3.12/site-packages/tensorflow/python/ops/script_ops.py\", line 146, in __call__\n    outputs = self._call(device, args)\n              ^^^^^^^^^^^^^^^^^^^^^^^^\n\n  File \"/Users/lihaidun/NSCC/CodeSpace/LipNet/.venv/lib/python3.12/site-packages/tensorflow/python/ops/script_ops.py\", line 153, in _call\n    ret = self._func(*args)\n          ^^^^^^^^^^^^^^^^^\n\n  File \"/Users/lihaidun/NSCC/CodeSpace/LipNet/.venv/lib/python3.12/site-packages/tensorflow/python/autograph/impl/api.py\", line 643, in wrapper\n    return func(*args, **kwargs)\n           ^^^^^^^^^^^^^^^^^^^^^\n\n  File \"/var/folders/5y/8wf3zbrx6k91qcb2jgf69fl40000gn/T/ipykernel_80855/1624089498.py\", line 9, in load_data\n    alignments = load_alignments(alignment_path)\n                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\n  File \"/var/folders/5y/8wf3zbrx6k91qcb2jgf69fl40000gn/T/ipykernel_80855/1342149016.py\", line 2, in load_alignments\n    with open(path, 'r') as f:\n         ^^^^^^^^^^^^^^^\n\n  File \"/Users/lihaidun/NSCC/CodeSpace/LipNet/.venv/lib/python3.12/site-packages/IPython/core/interactiveshell.py\", line 324, in _modified_open\n    return io_open(file, *args, **kwargs)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\nFileNotFoundError: [Errno 2] No such file or directory: 'data/alignments/s1/.align'\n\n\n\t [[{{node EagerPyFunc}}]] [Op:IteratorGetNext] name: ",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mUnknownError\u001b[0m                              Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[56], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m val \u001b[38;5;241m=\u001b[39m sample\u001b[38;5;241m.\u001b[39mnext(); val[\u001b[38;5;241m0\u001b[39m]\n",
+      "File \u001b[0;32m~/NSCC/CodeSpace/LipNet/.venv/lib/python3.12/site-packages/tensorflow/python/data/ops/dataset_ops.py:4729\u001b[0m, in \u001b[0;36mNumpyIterator.next\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   4728\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mnext\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[0;32m-> 4729\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;21;43m__next__\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/NSCC/CodeSpace/LipNet/.venv/lib/python3.12/site-packages/tensorflow/python/data/ops/dataset_ops.py:4726\u001b[0m, in \u001b[0;36mNumpyIterator.__next__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   4723\u001b[0m     numpy\u001b[38;5;241m.\u001b[39msetflags(write\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[1;32m   4724\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m numpy\n\u001b[0;32m-> 4726\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m nest\u001b[38;5;241m.\u001b[39mmap_structure(to_numpy, \u001b[38;5;28;43mnext\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_iterator\u001b[49m\u001b[43m)\u001b[49m)\n",
+      "File \u001b[0;32m~/NSCC/CodeSpace/LipNet/.venv/lib/python3.12/site-packages/tensorflow/python/data/ops/iterator_ops.py:809\u001b[0m, in \u001b[0;36mOwnedIterator.__next__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    807\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__next__\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m    808\u001b[0m   \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 809\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_next_internal\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    810\u001b[0m   \u001b[38;5;28;01mexcept\u001b[39;00m errors\u001b[38;5;241m.\u001b[39mOutOfRangeError:\n\u001b[1;32m    811\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mStopIteration\u001b[39;00m\n",
+      "File \u001b[0;32m~/NSCC/CodeSpace/LipNet/.venv/lib/python3.12/site-packages/tensorflow/python/data/ops/iterator_ops.py:772\u001b[0m, in \u001b[0;36mOwnedIterator._next_internal\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    769\u001b[0m \u001b[38;5;66;03m# TODO(b/77291417): This runs in sync mode as iterators use an error status\u001b[39;00m\n\u001b[1;32m    770\u001b[0m \u001b[38;5;66;03m# to communicate that there is no more data to iterate over.\u001b[39;00m\n\u001b[1;32m    771\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m context\u001b[38;5;241m.\u001b[39mexecution_mode(context\u001b[38;5;241m.\u001b[39mSYNC):\n\u001b[0;32m--> 772\u001b[0m   ret \u001b[38;5;241m=\u001b[39m \u001b[43mgen_dataset_ops\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43miterator_get_next\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    773\u001b[0m \u001b[43m      \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_iterator_resource\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    774\u001b[0m \u001b[43m      \u001b[49m\u001b[43moutput_types\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_flat_output_types\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    775\u001b[0m \u001b[43m      \u001b[49m\u001b[43moutput_shapes\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_flat_output_shapes\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    777\u001b[0m   \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m    778\u001b[0m     \u001b[38;5;66;03m# Fast path for the case `self._structure` is not a nested structure.\u001b[39;00m\n\u001b[1;32m    779\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_element_spec\u001b[38;5;241m.\u001b[39m_from_compatible_tensor_list(ret)  \u001b[38;5;66;03m# pylint: disable=protected-access\u001b[39;00m\n",
+      "File \u001b[0;32m~/NSCC/CodeSpace/LipNet/.venv/lib/python3.12/site-packages/tensorflow/python/ops/gen_dataset_ops.py:3086\u001b[0m, in \u001b[0;36miterator_get_next\u001b[0;34m(iterator, output_types, output_shapes, name)\u001b[0m\n\u001b[1;32m   3084\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m _result\n\u001b[1;32m   3085\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m _core\u001b[38;5;241m.\u001b[39m_NotOkStatusException \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m-> 3086\u001b[0m   \u001b[43m_ops\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mraise_from_not_ok_status\u001b[49m\u001b[43m(\u001b[49m\u001b[43me\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mname\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   3087\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m _core\u001b[38;5;241m.\u001b[39m_FallbackException:\n\u001b[1;32m   3088\u001b[0m   \u001b[38;5;28;01mpass\u001b[39;00m\n",
+      "File \u001b[0;32m~/NSCC/CodeSpace/LipNet/.venv/lib/python3.12/site-packages/tensorflow/python/framework/ops.py:5983\u001b[0m, in \u001b[0;36mraise_from_not_ok_status\u001b[0;34m(e, name)\u001b[0m\n\u001b[1;32m   5981\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mraise_from_not_ok_status\u001b[39m(e, name) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m NoReturn:\n\u001b[1;32m   5982\u001b[0m   e\u001b[38;5;241m.\u001b[39mmessage \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m (\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m name: \u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mstr\u001b[39m(name \u001b[38;5;28;01mif\u001b[39;00m name \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m\"\u001b[39m))\n\u001b[0;32m-> 5983\u001b[0m   \u001b[38;5;28;01mraise\u001b[39;00m core\u001b[38;5;241m.\u001b[39m_status_to_exception(e) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n",
+      "\u001b[0;31mUnknownError\u001b[0m: {{function_node __wrapped__IteratorGetNext_output_types_2_device_/job:localhost/replica:0/task:0/device:CPU:0}} FileNotFoundError: [Errno 2] No such file or directory: 'data/alignments/s1/.align'\nTraceback (most recent call last):\n\n  File \"/Users/lihaidun/NSCC/CodeSpace/LipNet/.venv/lib/python3.12/site-packages/tensorflow/python/ops/script_ops.py\", line 268, in __call__\n    return func(device, token, args)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^\n\n  File \"/Users/lihaidun/NSCC/CodeSpace/LipNet/.venv/lib/python3.12/site-packages/tensorflow/python/ops/script_ops.py\", line 146, in __call__\n    outputs = self._call(device, args)\n              ^^^^^^^^^^^^^^^^^^^^^^^^\n\n  File \"/Users/lihaidun/NSCC/CodeSpace/LipNet/.venv/lib/python3.12/site-packages/tensorflow/python/ops/script_ops.py\", line 153, in _call\n    ret = self._func(*args)\n          ^^^^^^^^^^^^^^^^^\n\n  File \"/Users/lihaidun/NSCC/CodeSpace/LipNet/.venv/lib/python3.12/site-packages/tensorflow/python/autograph/impl/api.py\", line 643, in wrapper\n    return func(*args, **kwargs)\n           ^^^^^^^^^^^^^^^^^^^^^\n\n  File \"/var/folders/5y/8wf3zbrx6k91qcb2jgf69fl40000gn/T/ipykernel_80855/1624089498.py\", line 9, in load_data\n    alignments = load_alignments(alignment_path)\n                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\n  File \"/var/folders/5y/8wf3zbrx6k91qcb2jgf69fl40000gn/T/ipykernel_80855/1342149016.py\", line 2, in load_alignments\n    with open(path, 'r') as f:\n         ^^^^^^^^^^^^^^^\n\n  File \"/Users/lihaidun/NSCC/CodeSpace/LipNet/.venv/lib/python3.12/site-packages/IPython/core/interactiveshell.py\", line 324, in _modified_open\n    return io_open(file, *args, **kwargs)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\nFileNotFoundError: [Errno 2] No such file or directory: 'data/alignments/s1/.align'\n\n\n\t [[{{node EagerPyFunc}}]] [Op:IteratorGetNext] name: "
+     ]
     }
    ],
    "source": [
@@ -1457,91 +2036,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 187,
+   "execution_count": 57,
    "id": "acf5eb4f-a0da-4a9a-bf24-af13e9cc2fbe",
    "metadata": {
     "tags": []
    },
    "outputs": [
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Lossy conversion from float32 to uint8. Range [0.0, 10.088857650756836]. Convert image to uint8 prior to saving to suppress this warning.\n",
-      "Lossy conversion from float32 to uint8. Range [0.0, 10.088857650756836]. Convert image to uint8 prior to saving to suppress this warning.\n",
-      "Lossy conversion from float32 to uint8. Range [0.0, 10.088857650756836]. Convert image to uint8 prior to saving to suppress this warning.\n",
-      "Lossy conversion from float32 to uint8. Range [0.0, 10.088857650756836]. Convert image to uint8 prior to saving to suppress this warning.\n",
-      "Lossy conversion from float32 to uint8. Range [0.0, 10.088857650756836]. Convert image to uint8 prior to saving to suppress this warning.\n",
-      "Lossy conversion from float32 to uint8. Range [0.0, 10.088857650756836]. Convert image to uint8 prior to saving to suppress this warning.\n",
-      "Lossy conversion from float32 to uint8. Range [0.0, 10.088857650756836]. Convert image to uint8 prior to saving to suppress this warning.\n",
-      "Lossy conversion from float32 to uint8. Range [0.0, 10.088857650756836]. Convert image to uint8 prior to saving to suppress this warning.\n",
-      "Lossy conversion from float32 to uint8. Range [0.0, 10.088857650756836]. Convert image to uint8 prior to saving to suppress this warning.\n",
-      "Lossy conversion from float32 to uint8. Range [0.0, 10.088857650756836]. Convert image to uint8 prior to saving to suppress this warning.\n",
-      "Lossy conversion from float32 to uint8. Range [0.0, 10.088857650756836]. Convert image to uint8 prior to saving to suppress this warning.\n",
-      "Lossy conversion from float32 to uint8. Range [0.0, 10.088857650756836]. Convert image to uint8 prior to saving to suppress this warning.\n",
-      "Lossy conversion from float32 to uint8. Range [0.0, 10.088857650756836]. Convert image to uint8 prior to saving to suppress this warning.\n",
-      "Lossy conversion from float32 to uint8. Range [0.0, 10.088857650756836]. Convert image to uint8 prior to saving to suppress this warning.\n",
-      "Lossy conversion from float32 to uint8. Range [0.0, 10.088857650756836]. Convert image to uint8 prior to saving to suppress this warning.\n",
-      "Lossy conversion from float32 to uint8. Range [0.0, 10.088857650756836]. Convert image to uint8 prior to saving to suppress this warning.\n",
-      "Lossy conversion from float32 to uint8. Range [0.0, 10.088857650756836]. Convert image to uint8 prior to saving to suppress this warning.\n",
-      "Lossy conversion from float32 to uint8. Range [0.0, 10.088857650756836]. Convert image to uint8 prior to saving to suppress this warning.\n",
-      "Lossy conversion from float32 to uint8. Range [0.0, 10.088857650756836]. Convert image to uint8 prior to saving to suppress this warning.\n",
-      "Lossy conversion from float32 to uint8. Range [0.0, 10.088857650756836]. Convert image to uint8 prior to saving to suppress this warning.\n",
-      "Lossy conversion from float32 to uint8. Range [0.0, 10.088857650756836]. Convert image to uint8 prior to saving to suppress this warning.\n",
-      "Lossy conversion from float32 to uint8. Range [0.0, 10.088857650756836]. Convert image to uint8 prior to saving to suppress this warning.\n",
-      "Lossy conversion from float32 to uint8. Range [0.0, 10.088857650756836]. Convert image to uint8 prior to saving to suppress this warning.\n",
-      "Lossy conversion from float32 to uint8. Range [0.0, 10.088857650756836]. Convert image to uint8 prior to saving to suppress this warning.\n",
-      "Lossy conversion from float32 to uint8. Range [0.0, 10.088857650756836]. Convert image to uint8 prior to saving to suppress this warning.\n",
-      "Lossy conversion from float32 to uint8. Range [0.0, 10.088857650756836]. Convert image to uint8 prior to saving to suppress this warning.\n",
-      "Lossy conversion from float32 to uint8. Range [0.0, 10.088857650756836]. Convert image to uint8 prior to saving to suppress this warning.\n",
-      "Lossy conversion from float32 to uint8. Range [0.0, 10.088857650756836]. Convert image to uint8 prior to saving to suppress this warning.\n",
-      "Lossy conversion from float32 to uint8. Range [0.0, 10.088857650756836]. Convert image to uint8 prior to saving to suppress this warning.\n",
-      "Lossy conversion from float32 to uint8. Range [0.0, 10.088857650756836]. Convert image to uint8 prior to saving to suppress this warning.\n",
-      "Lossy conversion from float32 to uint8. Range [0.0, 10.088857650756836]. Convert image to uint8 prior to saving to suppress this warning.\n",
-      "Lossy conversion from float32 to uint8. Range [0.0, 10.088857650756836]. Convert image to uint8 prior to saving to suppress this warning.\n",
-      "Lossy conversion from float32 to uint8. Range [0.0, 10.088857650756836]. Convert image to uint8 prior to saving to suppress this warning.\n",
-      "Lossy conversion from float32 to uint8. Range [0.0, 10.088857650756836]. Convert image to uint8 prior to saving to suppress this warning.\n",
-      "Lossy conversion from float32 to uint8. Range [0.0, 10.088857650756836]. Convert image to uint8 prior to saving to suppress this warning.\n",
-      "Lossy conversion from float32 to uint8. Range [0.0, 10.088857650756836]. Convert image to uint8 prior to saving to suppress this warning.\n",
-      "Lossy conversion from float32 to uint8. Range [0.0, 10.088857650756836]. Convert image to uint8 prior to saving to suppress this warning.\n",
-      "Lossy conversion from float32 to uint8. Range [0.0, 10.088857650756836]. Convert image to uint8 prior to saving to suppress this warning.\n",
-      "Lossy conversion from float32 to uint8. Range [0.0, 10.088857650756836]. Convert image to uint8 prior to saving to suppress this warning.\n",
-      "Lossy conversion from float32 to uint8. Range [0.0, 10.088857650756836]. Convert image to uint8 prior to saving to suppress this warning.\n",
-      "Lossy conversion from float32 to uint8. Range [0.0, 10.088857650756836]. Convert image to uint8 prior to saving to suppress this warning.\n",
-      "Lossy conversion from float32 to uint8. Range [0.0, 10.088857650756836]. Convert image to uint8 prior to saving to suppress this warning.\n",
-      "Lossy conversion from float32 to uint8. Range [0.0, 10.088857650756836]. Convert image to uint8 prior to saving to suppress this warning.\n",
-      "Lossy conversion from float32 to uint8. Range [0.0, 10.088857650756836]. Convert image to uint8 prior to saving to suppress this warning.\n",
-      "Lossy conversion from float32 to uint8. Range [0.0, 10.088857650756836]. Convert image to uint8 prior to saving to suppress this warning.\n",
-      "Lossy conversion from float32 to uint8. Range [0.0, 10.088857650756836]. Convert image to uint8 prior to saving to suppress this warning.\n",
-      "Lossy conversion from float32 to uint8. Range [0.0, 10.088857650756836]. Convert image to uint8 prior to saving to suppress this warning.\n",
-      "Lossy conversion from float32 to uint8. Range [0.0, 10.088857650756836]. Convert image to uint8 prior to saving to suppress this warning.\n",
-      "Lossy conversion from float32 to uint8. Range [0.0, 10.088857650756836]. Convert image to uint8 prior to saving to suppress this warning.\n",
-      "Lossy conversion from float32 to uint8. Range [0.0, 10.088857650756836]. Convert image to uint8 prior to saving to suppress this warning.\n",
-      "Lossy conversion from float32 to uint8. Range [0.0, 10.088857650756836]. Convert image to uint8 prior to saving to suppress this warning.\n",
-      "Lossy conversion from float32 to uint8. Range [0.0, 10.088857650756836]. Convert image to uint8 prior to saving to suppress this warning.\n",
-      "Lossy conversion from float32 to uint8. Range [0.0, 10.088857650756836]. Convert image to uint8 prior to saving to suppress this warning.\n",
-      "Lossy conversion from float32 to uint8. Range [0.0, 10.088857650756836]. Convert image to uint8 prior to saving to suppress this warning.\n",
-      "Lossy conversion from float32 to uint8. Range [0.0, 10.088857650756836]. Convert image to uint8 prior to saving to suppress this warning.\n",
-      "Lossy conversion from float32 to uint8. Range [0.0, 10.088857650756836]. Convert image to uint8 prior to saving to suppress this warning.\n",
-      "Lossy conversion from float32 to uint8. Range [0.0, 10.088857650756836]. Convert image to uint8 prior to saving to suppress this warning.\n",
-      "Lossy conversion from float32 to uint8. Range [0.0, 10.088857650756836]. Convert image to uint8 prior to saving to suppress this warning.\n",
-      "Lossy conversion from float32 to uint8. Range [0.0, 10.088857650756836]. Convert image to uint8 prior to saving to suppress this warning.\n",
-      "Lossy conversion from float32 to uint8. Range [0.0, 10.088857650756836]. Convert image to uint8 prior to saving to suppress this warning.\n",
-      "Lossy conversion from float32 to uint8. Range [0.0, 10.088857650756836]. Convert image to uint8 prior to saving to suppress this warning.\n",
-      "Lossy conversion from float32 to uint8. Range [0.0, 10.088857650756836]. Convert image to uint8 prior to saving to suppress this warning.\n",
-      "Lossy conversion from float32 to uint8. Range [0.0, 10.088857650756836]. Convert image to uint8 prior to saving to suppress this warning.\n",
-      "Lossy conversion from float32 to uint8. Range [0.0, 10.088857650756836]. Convert image to uint8 prior to saving to suppress this warning.\n",
-      "Lossy conversion from float32 to uint8. Range [0.0, 10.088857650756836]. Convert image to uint8 prior to saving to suppress this warning.\n",
-      "Lossy conversion from float32 to uint8. Range [0.0, 10.088857650756836]. Convert image to uint8 prior to saving to suppress this warning.\n",
-      "Lossy conversion from float32 to uint8. Range [0.0, 10.088857650756836]. Convert image to uint8 prior to saving to suppress this warning.\n",
-      "Lossy conversion from float32 to uint8. Range [0.0, 10.088857650756836]. Convert image to uint8 prior to saving to suppress this warning.\n",
-      "Lossy conversion from float32 to uint8. Range [0.0, 10.088857650756836]. Convert image to uint8 prior to saving to suppress this warning.\n",
-      "Lossy conversion from float32 to uint8. Range [0.0, 10.088857650756836]. Convert image to uint8 prior to saving to suppress this warning.\n",
-      "Lossy conversion from float32 to uint8. Range [0.0, 10.088857650756836]. Convert image to uint8 prior to saving to suppress this warning.\n",
-      "Lossy conversion from float32 to uint8. Range [0.0, 10.088857650756836]. Convert image to uint8 prior to saving to suppress this warning.\n",
-      "Lossy conversion from float32 to uint8. Range [0.0, 10.088857650756836]. Convert image to uint8 prior to saving to suppress this warning.\n",
-      "Lossy conversion from float32 to uint8. Range [0.0, 10.088857650756836]. Convert image to uint8 prior to saving to suppress this warning.\n",
-      "Lossy conversion from float32 to uint8. Range [0.0, 10.088857650756836]. Convert image to uint8 prior to saving to suppress this warning.\n"
+     "ename": "NameError",
+     "evalue": "name 'val' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[57], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m imageio\u001b[38;5;241m.\u001b[39mmimsave(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m./animation.gif\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[43mval\u001b[49m[\u001b[38;5;241m0\u001b[39m][\u001b[38;5;241m0\u001b[39m], fps\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m10\u001b[39m)\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'val' is not defined"
      ]
     }
    ],
@@ -1551,31 +2060,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 188,
+   "execution_count": null,
    "id": "c33a87a2-d5e0-4ec9-b174-73ebf41bf03a",
    "metadata": {
     "tags": []
    },
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       ""
-      ]
-     },
-     "execution_count": 188,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "
"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
+     "ename": "",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31mRunning cells with 'Python 3.12.4' requires the ipykernel package.\n",
+      "\u001b[1;31mRun the following command to install 'ipykernel' into the Python environment. \n",
+      "\u001b[1;31mCommand: '/opt/homebrew/bin/python3 -m pip install ipykernel -U --user --force-reinstall'"
+     ]
     }
    ],
    "source": [
@@ -1585,21 +2084,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 189,
+   "execution_count": null,
    "id": "84593332-133c-4205-b7a6-8e235d5e2b3b",
    "metadata": {
     "tags": []
    },
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       ""
-      ]
-     },
-     "execution_count": 189,
-     "metadata": {},
-     "output_type": "execute_result"
+     "ename": "",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31mRunning cells with 'Python 3.12.4' requires the ipykernel package.\n",
+      "\u001b[1;31mRun the following command to install 'ipykernel' into the Python environment. \n",
+      "\u001b[1;31mCommand: '/opt/homebrew/bin/python3 -m pip install ipykernel -U --user --force-reinstall'"
+     ]
     }
    ],
    "source": [
@@ -1618,12 +2117,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 190,
+   "execution_count": null,
    "id": "d8e9a497-191b-4842-afbd-26f5e13c43ba",
    "metadata": {
     "tags": []
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31mRunning cells with 'Python 3.12.4' requires the ipykernel package.\n",
+      "\u001b[1;31mRun the following command to install 'ipykernel' into the Python environment. \n",
+      "\u001b[1;31mCommand: '/opt/homebrew/bin/python3 -m pip install ipykernel -U --user --force-reinstall'"
+     ]
+    }
+   ],
    "source": [
     "import tensorflow\n",
     "from tensorflow import keras\n",
@@ -1635,19 +2145,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 191,
+   "execution_count": null,
    "id": "3f753ed2-70b9-4236-8c1c-08ca065dc8bf",
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "(75, 46, 140, 1)"
-      ]
-     },
-     "execution_count": 191,
-     "metadata": {},
-     "output_type": "execute_result"
+     "ename": "",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31mRunning cells with 'Python 3.12.4' requires the ipykernel package.\n",
+      "\u001b[1;31mRun the following command to install 'ipykernel' into the Python environment. \n",
+      "\u001b[1;31mCommand: '/opt/homebrew/bin/python3 -m pip install ipykernel -U --user --force-reinstall'"
+     ]
     }
    ],
    "source": [
@@ -1656,12 +2166,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 192,
+   "execution_count": null,
    "id": "f9171056-a352-491a-9ed9-92b28ced268e",
    "metadata": {
     "tags": []
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31mRunning cells with 'Python 3.12.4' requires the ipykernel package.\n",
+      "\u001b[1;31mRun the following command to install 'ipykernel' into the Python environment. \n",
+      "\u001b[1;31mCommand: '/opt/homebrew/bin/python3 -m pip install ipykernel -U --user --force-reinstall'"
+     ]
+    }
+   ],
    "source": [
     "model = Sequential()\n",
     "model.add(Conv3D(128, 3, input_shape=(75,46,140,1), padding='same'))\n",
@@ -1690,7 +2211,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 193,
+   "execution_count": null,
    "id": "78851825-2bcd-42a9-b7f2-28bb5a6bf43a",
    "metadata": {
     "collapsed": true,
@@ -1701,147 +2222,14 @@
    },
    "outputs": [
     {
-     "data": {
-      "text/html": [
-       "
Model: \"sequential_5\"\n",
-       "
\n"
-      ],
-      "text/plain": [
-       "\u001b[1mModel: \"sequential_5\"\u001b[0m\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
-       "┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃\n",
-       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
-       "│ conv3d_15 (Conv3D)              │ (None, 75, 46, 140,    │         3,584 │\n",
-       "│                                 │ 128)                   │               │\n",
-       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ activation_15 (Activation)      │ (None, 75, 46, 140,    │             0 │\n",
-       "│                                 │ 128)                   │               │\n",
-       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ max_pooling3d_15 (MaxPooling3D) │ (None, 75, 23, 70,     │             0 │\n",
-       "│                                 │ 128)                   │               │\n",
-       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ conv3d_16 (Conv3D)              │ (None, 75, 23, 70,     │       884,992 │\n",
-       "│                                 │ 256)                   │               │\n",
-       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ activation_16 (Activation)      │ (None, 75, 23, 70,     │             0 │\n",
-       "│                                 │ 256)                   │               │\n",
-       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ max_pooling3d_16 (MaxPooling3D) │ (None, 75, 11, 35,     │             0 │\n",
-       "│                                 │ 256)                   │               │\n",
-       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ conv3d_17 (Conv3D)              │ (None, 75, 11, 35, 75) │       518,475 │\n",
-       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ activation_17 (Activation)      │ (None, 75, 11, 35, 75) │             0 │\n",
-       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ max_pooling3d_17 (MaxPooling3D) │ (None, 75, 5, 17, 75)  │             0 │\n",
-       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ time_distributed_5              │ (None, 75, 6375)       │             0 │\n",
-       "│ (TimeDistributed)               │                        │               │\n",
-       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ bidirectional_7 (Bidirectional) │ (None, 75, 256)        │     6,660,096 │\n",
-       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dropout_6 (Dropout)             │ (None, 75, 256)        │             0 │\n",
-       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ bidirectional_8 (Bidirectional) │ (None, 75, 256)        │       394,240 │\n",
-       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dropout_7 (Dropout)             │ (None, 75, 256)        │             0 │\n",
-       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dense_3 (Dense)                 │ (None, 75, 41)         │        10,537 │\n",
-       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
-       "
\n"
-      ],
-      "text/plain": [
-       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
-       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
-       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
-       "│ conv3d_15 (\u001b[38;5;33mConv3D\u001b[0m)              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m46\u001b[0m, \u001b[38;5;34m140\u001b[0m,    │         \u001b[38;5;34m3,584\u001b[0m │\n",
-       "│                                 │ \u001b[38;5;34m128\u001b[0m)                   │               │\n",
-       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ activation_15 (\u001b[38;5;33mActivation\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m46\u001b[0m, \u001b[38;5;34m140\u001b[0m,    │             \u001b[38;5;34m0\u001b[0m │\n",
-       "│                                 │ \u001b[38;5;34m128\u001b[0m)                   │               │\n",
-       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ max_pooling3d_15 (\u001b[38;5;33mMaxPooling3D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m23\u001b[0m, \u001b[38;5;34m70\u001b[0m,     │             \u001b[38;5;34m0\u001b[0m │\n",
-       "│                                 │ \u001b[38;5;34m128\u001b[0m)                   │               │\n",
-       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ conv3d_16 (\u001b[38;5;33mConv3D\u001b[0m)              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m23\u001b[0m, \u001b[38;5;34m70\u001b[0m,     │       \u001b[38;5;34m884,992\u001b[0m │\n",
-       "│                                 │ \u001b[38;5;34m256\u001b[0m)                   │               │\n",
-       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ activation_16 (\u001b[38;5;33mActivation\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m23\u001b[0m, \u001b[38;5;34m70\u001b[0m,     │             \u001b[38;5;34m0\u001b[0m │\n",
-       "│                                 │ \u001b[38;5;34m256\u001b[0m)                   │               │\n",
-       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ max_pooling3d_16 (\u001b[38;5;33mMaxPooling3D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m11\u001b[0m, \u001b[38;5;34m35\u001b[0m,     │             \u001b[38;5;34m0\u001b[0m │\n",
-       "│                                 │ \u001b[38;5;34m256\u001b[0m)                   │               │\n",
-       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ conv3d_17 (\u001b[38;5;33mConv3D\u001b[0m)              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m11\u001b[0m, \u001b[38;5;34m35\u001b[0m, \u001b[38;5;34m75\u001b[0m) │       \u001b[38;5;34m518,475\u001b[0m │\n",
-       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ activation_17 (\u001b[38;5;33mActivation\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m11\u001b[0m, \u001b[38;5;34m35\u001b[0m, \u001b[38;5;34m75\u001b[0m) │             \u001b[38;5;34m0\u001b[0m │\n",
-       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ max_pooling3d_17 (\u001b[38;5;33mMaxPooling3D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m17\u001b[0m, \u001b[38;5;34m75\u001b[0m)  │             \u001b[38;5;34m0\u001b[0m │\n",
-       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ time_distributed_5              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m6375\u001b[0m)       │             \u001b[38;5;34m0\u001b[0m │\n",
-       "│ (\u001b[38;5;33mTimeDistributed\u001b[0m)               │                        │               │\n",
-       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ bidirectional_7 (\u001b[38;5;33mBidirectional\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m256\u001b[0m)        │     \u001b[38;5;34m6,660,096\u001b[0m │\n",
-       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dropout_6 (\u001b[38;5;33mDropout\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m256\u001b[0m)        │             \u001b[38;5;34m0\u001b[0m │\n",
-       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ bidirectional_8 (\u001b[38;5;33mBidirectional\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m256\u001b[0m)        │       \u001b[38;5;34m394,240\u001b[0m │\n",
-       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dropout_7 (\u001b[38;5;33mDropout\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m256\u001b[0m)        │             \u001b[38;5;34m0\u001b[0m │\n",
-       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dense_3 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m41\u001b[0m)         │        \u001b[38;5;34m10,537\u001b[0m │\n",
-       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "
 Total params: 8,471,924 (32.32 MB)\n",
-       "
\n"
-      ],
-      "text/plain": [
-       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m8,471,924\u001b[0m (32.32 MB)\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "
 Trainable params: 8,471,924 (32.32 MB)\n",
-       "
\n"
-      ],
-      "text/plain": [
-       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m8,471,924\u001b[0m (32.32 MB)\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "
 Non-trainable params: 0 (0.00 B)\n",
-       "
\n"
-      ],
-      "text/plain": [
-       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
+     "ename": "",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31mRunning cells with 'Python 3.12.4' requires the ipykernel package.\n",
+      "\u001b[1;31mRun the following command to install 'ipykernel' into the Python environment. \n",
+      "\u001b[1;31mCommand: '/opt/homebrew/bin/python3 -m pip install ipykernel -U --user --force-reinstall'"
+     ]
     }
    ],
    "source": [
@@ -1850,19 +2238,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 194,
+   "execution_count": null,
    "id": "f4b4798c-a65a-4c47-9e2a-3b09dc98d320",
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "6375"
-      ]
-     },
-     "execution_count": 194,
-     "metadata": {},
-     "output_type": "execute_result"
+     "ename": "",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31mRunning cells with 'Python 3.12.4' requires the ipykernel package.\n",
+      "\u001b[1;31mRun the following command to install 'ipykernel' into the Python environment. \n",
+      "\u001b[1;31mCommand: '/opt/homebrew/bin/python3 -m pip install ipykernel -U --user --force-reinstall'"
+     ]
     }
    ],
    "source": [
@@ -1871,17 +2259,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 195,
+   "execution_count": null,
    "id": "e5c2eae0-c359-41a4-97a0-75c44dccb7d1",
    "metadata": {
     "tags": []
    },
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2s/step\n"
+     "ename": "",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31mRunning cells with 'Python 3.12.4' requires the ipykernel package.\n",
+      "\u001b[1;31mRun the following command to install 'ipykernel' into the Python environment. \n",
+      "\u001b[1;31mCommand: '/opt/homebrew/bin/python3 -m pip install ipykernel -U --user --force-reinstall'"
      ]
     }
    ],
@@ -1891,19 +2282,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 196,
+   "execution_count": null,
    "id": "ffdc7319-0d69-4f7e-a6d4-ce72deb81c0b",
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       ""
-      ]
-     },
-     "execution_count": 196,
-     "metadata": {},
-     "output_type": "execute_result"
+     "ename": "",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31mRunning cells with 'Python 3.12.4' requires the ipykernel package.\n",
+      "\u001b[1;31mRun the following command to install 'ipykernel' into the Python environment. \n",
+      "\u001b[1;31mCommand: '/opt/homebrew/bin/python3 -m pip install ipykernel -U --user --force-reinstall'"
+     ]
     }
    ],
    "source": [
@@ -1912,21 +2303,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 197,
+   "execution_count": null,
    "id": "6ed47531-8317-4255-9a12-b757642258e6",
    "metadata": {
     "tags": []
    },
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       ""
-      ]
-     },
-     "execution_count": 197,
-     "metadata": {},
-     "output_type": "execute_result"
+     "ename": "",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31mRunning cells with 'Python 3.12.4' requires the ipykernel package.\n",
+      "\u001b[1;31mRun the following command to install 'ipykernel' into the Python environment. \n",
+      "\u001b[1;31mCommand: '/opt/homebrew/bin/python3 -m pip install ipykernel -U --user --force-reinstall'"
+     ]
     }
    ],
    "source": [
@@ -1935,21 +2326,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 198,
+   "execution_count": null,
    "id": "7c37b9b9-5298-4038-9c33-5031d1b457f0",
    "metadata": {
     "tags": []
    },
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "(None, 75, 46, 140, 1)"
-      ]
-     },
-     "execution_count": 198,
-     "metadata": {},
-     "output_type": "execute_result"
+     "ename": "",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31mRunning cells with 'Python 3.12.4' requires the ipykernel package.\n",
+      "\u001b[1;31mRun the following command to install 'ipykernel' into the Python environment. \n",
+      "\u001b[1;31mCommand: '/opt/homebrew/bin/python3 -m pip install ipykernel -U --user --force-reinstall'"
+     ]
     }
    ],
    "source": [
@@ -1958,21 +2349,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 199,
+   "execution_count": null,
    "id": "98b316a4-5322-4782-8e36-4b3c1a696d85",
    "metadata": {
     "tags": []
    },
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "(None, 75, 41)"
-      ]
-     },
-     "execution_count": 199,
-     "metadata": {},
-     "output_type": "execute_result"
+     "ename": "",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31mRunning cells with 'Python 3.12.4' requires the ipykernel package.\n",
+      "\u001b[1;31mRun the following command to install 'ipykernel' into the Python environment. \n",
+      "\u001b[1;31mCommand: '/opt/homebrew/bin/python3 -m pip install ipykernel -U --user --force-reinstall'"
+     ]
     }
    ],
    "source": [
@@ -1991,12 +2382,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 200,
+   "execution_count": null,
    "id": "ab015fd0-7fb4-4d5d-9fa2-30a05dbd515a",
    "metadata": {
     "tags": []
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31mRunning cells with 'Python 3.12.4' requires the ipykernel package.\n",
+      "\u001b[1;31mRun the following command to install 'ipykernel' into the Python environment. \n",
+      "\u001b[1;31mCommand: '/opt/homebrew/bin/python3 -m pip install ipykernel -U --user --force-reinstall'"
+     ]
+    }
+   ],
    "source": [
     "def scheduler(epoch, lr):\n",
     "    if epoch < 30:\n",
@@ -2007,12 +2409,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 201,
+   "execution_count": null,
    "id": "c564d5c9-db54-4e88-b311-9aeab7fb3e69",
    "metadata": {
     "tags": []
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31mRunning cells with 'Python 3.12.4' requires the ipykernel package.\n",
+      "\u001b[1;31mRun the following command to install 'ipykernel' into the Python environment. \n",
+      "\u001b[1;31mCommand: '/opt/homebrew/bin/python3 -m pip install ipykernel -U --user --force-reinstall'"
+     ]
+    }
+   ],
    "source": [
     "def CTCLoss(y_true, y_pred):\n",
     "    batch_len = tf.cast(tf.shape(y_true)[0], dtype=\"int64\")\n",
@@ -2028,12 +2441,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 202,
+   "execution_count": null,
    "id": "a26dc3fc-a19c-4378-bd8c-e2b597a1d15c",
    "metadata": {
     "tags": []
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31mRunning cells with 'Python 3.12.4' requires the ipykernel package.\n",
+      "\u001b[1;31mRun the following command to install 'ipykernel' into the Python environment. \n",
+      "\u001b[1;31mCommand: '/opt/homebrew/bin/python3 -m pip install ipykernel -U --user --force-reinstall'"
+     ]
+    }
+   ],
    "source": [
     "class ProduceExample(tf.keras.callbacks.Callback):\n",
     "    def __init__(self, dataset) -> None:\n",
@@ -2051,34 +2475,43 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 203,
+   "execution_count": null,
    "id": "04be90d8-2482-46f9-b513-d5f4f8001c7e",
    "metadata": {
     "tags": []
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31mRunning cells with 'Python 3.12.4' requires the ipykernel package.\n",
+      "\u001b[1;31mRun the following command to install 'ipykernel' into the Python environment. \n",
+      "\u001b[1;31mCommand: '/opt/homebrew/bin/python3 -m pip install ipykernel -U --user --force-reinstall'"
+     ]
+    }
+   ],
    "source": [
     "model.compile(optimizer=Adam(learning_rate=0.0001), loss=CTCLoss)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 204,
+   "execution_count": null,
    "id": "eab49367-3f1e-4464-ae76-dbd07549d97e",
    "metadata": {
     "tags": []
    },
    "outputs": [
     {
-     "ename": "ValueError",
-     "evalue": "When using `save_weights_only=True` in `ModelCheckpoint`, the filepath provided must end in `.weights.h5` (Keras weights format). Received: filepath=models\\checkpoint",
+     "ename": "",
+     "evalue": "",
      "output_type": "error",
      "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "Cell \u001b[1;32mIn[204], line 2\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;66;03m# checkpoint_callback = ModelCheckpoint(os.path.join('models','checkpoint'), monitor='loss', save_weights_only=True)\u001b[39;00m\n\u001b[1;32m----> 2\u001b[0m checkpoint_callback \u001b[38;5;241m=\u001b[39m \u001b[43mModelCheckpoint\u001b[49m\u001b[43m(\u001b[49m\u001b[43mos\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpath\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mjoin\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mmodels\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mcheckpoint\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmonitor\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mloss\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msave_weights_only\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n",
-      "File \u001b[1;32mc:\\Users\\lhdto\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\callbacks\\model_checkpoint.py:183\u001b[0m, in \u001b[0;36mModelCheckpoint.__init__\u001b[1;34m(self, filepath, monitor, verbose, save_best_only, save_weights_only, mode, save_freq, initial_value_threshold)\u001b[0m\n\u001b[0;32m    181\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m save_weights_only:\n\u001b[0;32m    182\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfilepath\u001b[38;5;241m.\u001b[39mendswith(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m.weights.h5\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[1;32m--> 183\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m    184\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mWhen using `save_weights_only=True` in `ModelCheckpoint`\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    185\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m, the filepath provided must end in `.weights.h5` \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    186\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m(Keras weights format). Received: \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    187\u001b[0m             \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfilepath=\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfilepath\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    188\u001b[0m         )\n\u001b[0;32m    189\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    190\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfilepath\u001b[38;5;241m.\u001b[39mendswith(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m.keras\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n",
-      "\u001b[1;31mValueError\u001b[0m: When using `save_weights_only=True` in `ModelCheckpoint`, the filepath provided must end in `.weights.h5` (Keras weights format). Received: filepath=models\\checkpoint"
+      "\u001b[1;31mRunning cells with 'Python 3.12.4' requires the ipykernel package.\n",
+      "\u001b[1;31mRun the following command to install 'ipykernel' into the Python environment. \n",
+      "\u001b[1;31mCommand: '/opt/homebrew/bin/python3 -m pip install ipykernel -U --user --force-reinstall'"
      ]
     }
    ],
@@ -2093,7 +2526,18 @@
    "metadata": {
     "tags": []
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31mRunning cells with 'Python 3.12.4' requires the ipykernel package.\n",
+      "\u001b[1;31mRun the following command to install 'ipykernel' into the Python environment. \n",
+      "\u001b[1;31mCommand: '/opt/homebrew/bin/python3 -m pip install ipykernel -U --user --force-reinstall'"
+     ]
+    }
+   ],
    "source": [
     "schedule_callback = LearningRateScheduler(scheduler)"
    ]
@@ -2105,7 +2549,18 @@
    "metadata": {
     "tags": []
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31mRunning cells with 'Python 3.12.4' requires the ipykernel package.\n",
+      "\u001b[1;31mRun the following command to install 'ipykernel' into the Python environment. \n",
+      "\u001b[1;31mCommand: '/opt/homebrew/bin/python3 -m pip install ipykernel -U --user --force-reinstall'"
+     ]
+    }
+   ],
    "source": [
     "example_callback = ProduceExample(test)"
    ]
@@ -2119,20 +2574,13 @@
    },
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 1/100\n",
-      "  2/450 [..............................] - ETA: 3:03 - loss: 213.9969 "
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "KeyboardInterrupt\n",
-      "\n"
+     "ename": "",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31mRunning cells with 'Python 3.12.4' requires the ipykernel package.\n",
+      "\u001b[1;31mRun the following command to install 'ipykernel' into the Python environment. \n",
+      "\u001b[1;31mCommand: '/opt/homebrew/bin/python3 -m pip install ipykernel -U --user --force-reinstall'"
      ]
     }
    ],
@@ -2157,7 +2605,18 @@
    "metadata": {
     "tags": []
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31mRunning cells with 'Python 3.12.4' requires the ipykernel package.\n",
+      "\u001b[1;31mRun the following command to install 'ipykernel' into the Python environment. \n",
+      "\u001b[1;31mCommand: '/opt/homebrew/bin/python3 -m pip install ipykernel -U --user --force-reinstall'"
+     ]
+    }
+   ],
    "source": [
     "url = 'https://drive.google.com/uc?id=1vWscXs4Vt0a_1IH1-ct2TCgXAZT-N3_Y'\n",
     "output = 'checkpoints.zip'\n",
@@ -2174,14 +2633,14 @@
    },
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       ""
-      ]
-     },
-     "execution_count": 53,
-     "metadata": {},
-     "output_type": "execute_result"
+     "ename": "",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31mRunning cells with 'Python 3.12.4' requires the ipykernel package.\n",
+      "\u001b[1;31mRun the following command to install 'ipykernel' into the Python environment. \n",
+      "\u001b[1;31mCommand: '/opt/homebrew/bin/python3 -m pip install ipykernel -U --user --force-reinstall'"
+     ]
     }
    ],
    "source": [
@@ -2195,7 +2654,18 @@
    "metadata": {
     "tags": []
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31mRunning cells with 'Python 3.12.4' requires the ipykernel package.\n",
+      "\u001b[1;31mRun the following command to install 'ipykernel' into the Python environment. \n",
+      "\u001b[1;31mCommand: '/opt/homebrew/bin/python3 -m pip install ipykernel -U --user --force-reinstall'"
+     ]
+    }
+   ],
    "source": [
     "test_data = test.as_numpy_iterator()"
    ]
@@ -2207,7 +2677,18 @@
    "metadata": {
     "tags": []
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31mRunning cells with 'Python 3.12.4' requires the ipykernel package.\n",
+      "\u001b[1;31mRun the following command to install 'ipykernel' into the Python environment. \n",
+      "\u001b[1;31mCommand: '/opt/homebrew/bin/python3 -m pip install ipykernel -U --user --force-reinstall'"
+     ]
+    }
+   ],
    "source": [
     "sample = test_data.next()"
    ]
@@ -2221,10 +2702,13 @@
    },
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 1s 973ms/step\n"
+     "ename": "",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31mRunning cells with 'Python 3.12.4' requires the ipykernel package.\n",
+      "\u001b[1;31mRun the following command to install 'ipykernel' into the Python environment. \n",
+      "\u001b[1;31mCommand: '/opt/homebrew/bin/python3 -m pip install ipykernel -U --user --force-reinstall'"
      ]
     }
    ],
@@ -2241,22 +2725,14 @@
    },
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ REAL TEXT\n"
+     "ename": "",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31mRunning cells with 'Python 3.12.4' requires the ipykernel package.\n",
+      "\u001b[1;31mRun the following command to install 'ipykernel' into the Python environment. \n",
+      "\u001b[1;31mCommand: '/opt/homebrew/bin/python3 -m pip install ipykernel -U --user --force-reinstall'"
      ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "[,\n",
-       " ]"
-      ]
-     },
-     "execution_count": 58,
-     "metadata": {},
-     "output_type": "execute_result"
     }
    ],
    "source": [
@@ -2271,7 +2747,18 @@
    "metadata": {
     "tags": []
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31mRunning cells with 'Python 3.12.4' requires the ipykernel package.\n",
+      "\u001b[1;31mRun the following command to install 'ipykernel' into the Python environment. \n",
+      "\u001b[1;31mCommand: '/opt/homebrew/bin/python3 -m pip install ipykernel -U --user --force-reinstall'"
+     ]
+    }
+   ],
    "source": [
     "decoded = tf.keras.backend.ctc_decode(yhat, input_length=[75,75], greedy=True)[0][0].numpy()"
    ]
@@ -2285,22 +2772,14 @@
    },
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ PREDICTIONS\n"
+     "ename": "",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31mRunning cells with 'Python 3.12.4' requires the ipykernel package.\n",
+      "\u001b[1;31mRun the following command to install 'ipykernel' into the Python environment. \n",
+      "\u001b[1;31mCommand: '/opt/homebrew/bin/python3 -m pip install ipykernel -U --user --force-reinstall'"
      ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "[,\n",
-       " ]"
-      ]
-     },
-     "execution_count": 60,
-     "metadata": {},
-     "output_type": "execute_result"
     }
    ],
    "source": [
@@ -2321,7 +2800,18 @@
    "execution_count": null,
    "id": "a8b0c4d0-2031-4331-b91d-d87b1ae6f6e2",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31mRunning cells with 'Python 3.12.4' requires the ipykernel package.\n",
+      "\u001b[1;31mRun the following command to install 'ipykernel' into the Python environment. \n",
+      "\u001b[1;31mCommand: '/opt/homebrew/bin/python3 -m pip install ipykernel -U --user --force-reinstall'"
+     ]
+    }
+   ],
    "source": [
     "sample = load_data(tf.convert_to_tensor('.\\\\data\\\\s1\\\\bras9a.mpg'))"
    ]
@@ -2333,21 +2823,14 @@
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ REAL TEXT\n"
+     "ename": "",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31mRunning cells with 'Python 3.12.4' requires the ipykernel package.\n",
+      "\u001b[1;31mRun the following command to install 'ipykernel' into the Python environment. \n",
+      "\u001b[1;31mCommand: '/opt/homebrew/bin/python3 -m pip install ipykernel -U --user --force-reinstall'"
      ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "[]"
-      ]
-     },
-     "execution_count": 62,
-     "metadata": {},
-     "output_type": "execute_result"
     }
    ],
    "source": [
@@ -2362,10 +2845,13 @@
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 1s 720ms/step\n"
+     "ename": "",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31mRunning cells with 'Python 3.12.4' requires the ipykernel package.\n",
+      "\u001b[1;31mRun the following command to install 'ipykernel' into the Python environment. \n",
+      "\u001b[1;31mCommand: '/opt/homebrew/bin/python3 -m pip install ipykernel -U --user --force-reinstall'"
      ]
     }
    ],
@@ -2378,7 +2864,18 @@
    "execution_count": null,
    "id": "22c4f77d-715d-409f-bc5e-3ebe48704e8f",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31mRunning cells with 'Python 3.12.4' requires the ipykernel package.\n",
+      "\u001b[1;31mRun the following command to install 'ipykernel' into the Python environment. \n",
+      "\u001b[1;31mCommand: '/opt/homebrew/bin/python3 -m pip install ipykernel -U --user --force-reinstall'"
+     ]
+    }
+   ],
    "source": [
     "decoded = tf.keras.backend.ctc_decode(yhat, input_length=[75], greedy=True)[0][0].numpy()"
    ]
@@ -2390,21 +2887,14 @@
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ PREDICTIONS\n"
+     "ename": "",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31mRunning cells with 'Python 3.12.4' requires the ipykernel package.\n",
+      "\u001b[1;31mRun the following command to install 'ipykernel' into the Python environment. \n",
+      "\u001b[1;31mCommand: '/opt/homebrew/bin/python3 -m pip install ipykernel -U --user --force-reinstall'"
      ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "[]"
-      ]
-     },
-     "execution_count": 65,
-     "metadata": {},
-     "output_type": "execute_result"
     }
    ],
    "source": [
@@ -2417,7 +2907,18 @@
    "execution_count": null,
    "id": "551dfea2-de6b-4400-b71a-a17631529e3f",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31mRunning cells with 'Python 3.12.4' requires the ipykernel package.\n",
+      "\u001b[1;31mRun the following command to install 'ipykernel' into the Python environment. \n",
+      "\u001b[1;31mCommand: '/opt/homebrew/bin/python3 -m pip install ipykernel -U --user --force-reinstall'"
+     ]
+    }
+   ],
    "source": []
   },
   {
@@ -2425,7 +2926,18 @@
    "execution_count": null,
    "id": "fa95863d-3832-47bf-8a77-ebaa38054ace",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31mRunning cells with 'Python 3.12.4' requires the ipykernel package.\n",
+      "\u001b[1;31mRun the following command to install 'ipykernel' into the Python environment. \n",
+      "\u001b[1;31mCommand: '/opt/homebrew/bin/python3 -m pip install ipykernel -U --user --force-reinstall'"
+     ]
+    }
+   ],
    "source": []
   }
  ],

From 34d5f242b8020cb0d220e9185a74bd555651ce3d Mon Sep 17 00:00:00 2001
From: Haidun Li 
Date: Fri, 21 Jun 2024 14:05:08 -0300
Subject: [PATCH 14/17] Refactor .gitignore: Update ignore rules for
 __pycache__ and .vscode/

---
 .gitignore | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/.gitignore b/.gitignore
index 1a6ba95f..1f4df16d 100644
--- a/.gitignore
+++ b/.gitignore
@@ -2,4 +2,5 @@
 data/
 models/
 results/
-__pycache__/
\ No newline at end of file
+__pycache__/
+.vscode/
\ No newline at end of file

From 2919510ceff48bae4559e35d37c39823fd096efb Mon Sep 17 00:00:00 2001
From: Haidun Li 
Date: Fri, 21 Jun 2024 14:45:54 -0300
Subject: [PATCH 15/17] Refactor .gitignore: Update ignore rules for
 __pycache__ and .vscode/

---
 .gitignore | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/.gitignore b/.gitignore
index 1f4df16d..5ea1958c 100644
--- a/.gitignore
+++ b/.gitignore
@@ -3,4 +3,5 @@ data/
 models/
 results/
 __pycache__/
-.vscode/
\ No newline at end of file
+.vscode/
+*.zip
\ No newline at end of file

From 0f5b5c89b872f3f4907951d7717b41efcc7b0cd9 Mon Sep 17 00:00:00 2001
From: Haidun Li 
Date: Fri, 21 Jun 2024 14:54:50 -0300
Subject: [PATCH 16/17] Refactor .gitignore: Update ignore rules for
 __pycache__ and .vscode/

---
 LipNet.ipynb | 106 ++++++++++++++++++++++++---------------------------
 1 file changed, 49 insertions(+), 57 deletions(-)

diff --git a/LipNet.ipynb b/LipNet.ipynb
index 1722d245..b44dc077 100644
--- a/LipNet.ipynb
+++ b/LipNet.ipynb
@@ -185,20 +185,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "id": "b24af50c-20b8-409d-ad78-30a933fdd669",
    "metadata": {
     "tags": []
    },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Matplotlib is building the font cache; this may take a moment.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import os\n",
     "import cv2\n",
@@ -211,7 +203,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "id": "1e3db0b0-e559-4ad6-91fd-e7414b7d75e6",
    "metadata": {},
    "outputs": [
@@ -221,7 +213,7 @@
        "[]"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -232,7 +224,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "id": "378d045a-3003-4f93-b7d2-a25a97774a68",
    "metadata": {
     "tags": []
@@ -1307,7 +1299,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 6,
    "id": "8548cc59-6dfc-4acc-abc3-3e65212db02e",
    "metadata": {
     "tags": []
@@ -1331,7 +1323,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 7,
    "id": "ec735e0b-ec98-4eb0-8f49-c35527d6670a",
    "metadata": {
     "tags": []
@@ -1343,7 +1335,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 8,
    "id": "be04e972-d7a5-4a72-82d8-a6bdde1f3ce6",
    "metadata": {
     "tags": []
@@ -1371,7 +1363,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 9,
    "id": "559f7420-6802-45fa-9ca0-b1ff209b461c",
    "metadata": {
     "tags": []
@@ -1422,7 +1414,7 @@
        " ' ']"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1433,7 +1425,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 10,
    "id": "797ff78b-b48f-4e14-bb62-8cd0ebf9501a",
    "metadata": {
     "tags": []
@@ -1445,7 +1437,7 @@
        ""
       ]
      },
-     "execution_count": 24,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1456,7 +1448,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 11,
    "id": "8cd7f4f4-ae77-4509-a4f4-c723787ebad1",
    "metadata": {},
    "outputs": [
@@ -1466,7 +1458,7 @@
        ""
       ]
      },
-     "execution_count": 25,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1477,7 +1469,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 12,
    "id": "9491bab5-6a3c-4f79-879a-8f9fbe73ae2e",
    "metadata": {
     "tags": []
@@ -1497,7 +1489,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 13,
    "id": "dd01ca9f-77fb-4643-a2aa-47dd82c5d66b",
    "metadata": {
     "tags": []
@@ -1519,7 +1511,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 14,
    "id": "8cb7cc58-31ae-4904-a805-1177a82717d2",
    "metadata": {
     "tags": []
@@ -1537,7 +1529,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 15,
    "id": "76aa964f-0c84-490d-897a-d00e3966e2c9",
    "metadata": {},
    "outputs": [
@@ -1547,7 +1539,7 @@
        "'bbal6n'"
       ]
      },
-     "execution_count": 44,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1558,7 +1550,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 16,
    "id": "eb602c71-8560-4f9e-b26b-08202febb937",
    "metadata": {
     "scrolled": true,
@@ -1571,17 +1563,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 17,
    "id": "0e3184a1-6b02-4b4f-84a8-a0a65f951ea2",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       ""
+       ""
       ]
      },
-     "execution_count": 46,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -1602,7 +1594,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 18,
    "id": "d7ec0833-d54b-4073-84cf-92d011c60ec1",
    "metadata": {},
    "outputs": [
@@ -1614,7 +1606,7 @@
        "       39, 14, 15, 23])>"
       ]
      },
-     "execution_count": 47,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1625,7 +1617,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 19,
    "id": "fe1ad370-b287-4b46-85a2-7c45b0bd9b10",
    "metadata": {},
    "outputs": [
@@ -1635,7 +1627,7 @@
        ""
       ]
      },
-     "execution_count": 48,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1646,7 +1638,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 20,
    "id": "6871031a-b0ba-4c76-a852-f6329b0f2606",
    "metadata": {
     "tags": []
@@ -1670,7 +1662,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 21,
    "id": "7686355d-45aa-4c85-ad9c-053e6a9b4d81",
    "metadata": {
     "tags": []
@@ -1682,7 +1674,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 22,
    "id": "f066fea2-91b1-42ed-a67d-00566a1a53ff",
    "metadata": {
     "tags": []
@@ -1701,7 +1693,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 23,
    "id": "6b1365bd-7742-41d1-95d4-247021751c3a",
    "metadata": {},
    "outputs": [
@@ -1711,7 +1703,7 @@
        "50"
       ]
      },
-     "execution_count": 52,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1722,7 +1714,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 24,
    "id": "5281bde8-fdc8-4da1-bd55-5a7929a9e80c",
    "metadata": {},
    "outputs": [
@@ -1731,7 +1723,7 @@
      "output_type": "stream",
      "text": [
       "OpenCV: Couldn't read video stream from file \"data/s1/.mpg\"\n",
-      "2024-06-21 14:03:10.432204: W tensorflow/core/framework/op_kernel.cc:1827] UNKNOWN: FileNotFoundError: [Errno 2] No such file or directory: 'data/alignments/s1/.align'\n",
+      "2024-06-21 14:54:32.104702: W tensorflow/core/framework/op_kernel.cc:1827] UNKNOWN: FileNotFoundError: [Errno 2] No such file or directory: 'data/alignments/s1/.align'\n",
       "Traceback (most recent call last):\n",
       "\n",
       "  File \"/Users/lihaidun/NSCC/CodeSpace/LipNet/.venv/lib/python3.12/site-packages/tensorflow/python/ops/script_ops.py\", line 268, in __call__\n",
@@ -1750,11 +1742,11 @@
       "    return func(*args, **kwargs)\n",
       "           ^^^^^^^^^^^^^^^^^^^^^\n",
       "\n",
-      "  File \"/var/folders/5y/8wf3zbrx6k91qcb2jgf69fl40000gn/T/ipykernel_80855/1624089498.py\", line 9, in load_data\n",
+      "  File \"/var/folders/5y/8wf3zbrx6k91qcb2jgf69fl40000gn/T/ipykernel_16494/1624089498.py\", line 9, in load_data\n",
       "    alignments = load_alignments(alignment_path)\n",
       "                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
       "\n",
-      "  File \"/var/folders/5y/8wf3zbrx6k91qcb2jgf69fl40000gn/T/ipykernel_80855/1342149016.py\", line 2, in load_alignments\n",
+      "  File \"/var/folders/5y/8wf3zbrx6k91qcb2jgf69fl40000gn/T/ipykernel_16494/1342149016.py\", line 2, in load_alignments\n",
       "    with open(path, 'r') as f:\n",
       "         ^^^^^^^^^^^^^^^\n",
       "\n",
@@ -1765,7 +1757,7 @@
       "FileNotFoundError: [Errno 2] No such file or directory: 'data/alignments/s1/.align'\n",
       "\n",
       "\n",
-      "2024-06-21 14:03:10.432297: W tensorflow/core/framework/local_rendezvous.cc:404] Local rendezvous is aborting with status: UNKNOWN: FileNotFoundError: [Errno 2] No such file or directory: 'data/alignments/s1/.align'\n",
+      "2024-06-21 14:54:32.104778: W tensorflow/core/framework/local_rendezvous.cc:404] Local rendezvous is aborting with status: UNKNOWN: FileNotFoundError: [Errno 2] No such file or directory: 'data/alignments/s1/.align'\n",
       "Traceback (most recent call last):\n",
       "\n",
       "  File \"/Users/lihaidun/NSCC/CodeSpace/LipNet/.venv/lib/python3.12/site-packages/tensorflow/python/ops/script_ops.py\", line 268, in __call__\n",
@@ -1784,11 +1776,11 @@
       "    return func(*args, **kwargs)\n",
       "           ^^^^^^^^^^^^^^^^^^^^^\n",
       "\n",
-      "  File \"/var/folders/5y/8wf3zbrx6k91qcb2jgf69fl40000gn/T/ipykernel_80855/1624089498.py\", line 9, in load_data\n",
+      "  File \"/var/folders/5y/8wf3zbrx6k91qcb2jgf69fl40000gn/T/ipykernel_16494/1624089498.py\", line 9, in load_data\n",
       "    alignments = load_alignments(alignment_path)\n",
       "                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
       "\n",
-      "  File \"/var/folders/5y/8wf3zbrx6k91qcb2jgf69fl40000gn/T/ipykernel_80855/1342149016.py\", line 2, in load_alignments\n",
+      "  File \"/var/folders/5y/8wf3zbrx6k91qcb2jgf69fl40000gn/T/ipykernel_16494/1342149016.py\", line 2, in load_alignments\n",
       "    with open(path, 'r') as f:\n",
       "         ^^^^^^^^^^^^^^^\n",
       "\n",
@@ -1801,7 +1793,7 @@
       "\n",
       "\t [[{{node EagerPyFunc}}]]\n",
       "OpenCV: Couldn't read video stream from file \"data/s1/.mpg\"\n",
-      "2024-06-21 14:03:10.460937: W tensorflow/core/framework/op_kernel.cc:1827] UNKNOWN: FileNotFoundError: [Errno 2] No such file or directory: 'data/alignments/s1/.align'\n",
+      "2024-06-21 14:54:32.108771: W tensorflow/core/framework/op_kernel.cc:1827] UNKNOWN: FileNotFoundError: [Errno 2] No such file or directory: 'data/alignments/s1/.align'\n",
       "Traceback (most recent call last):\n",
       "\n",
       "  File \"/Users/lihaidun/NSCC/CodeSpace/LipNet/.venv/lib/python3.12/site-packages/tensorflow/python/ops/script_ops.py\", line 268, in __call__\n",
@@ -1820,11 +1812,11 @@
       "    return func(*args, **kwargs)\n",
       "           ^^^^^^^^^^^^^^^^^^^^^\n",
       "\n",
-      "  File \"/var/folders/5y/8wf3zbrx6k91qcb2jgf69fl40000gn/T/ipykernel_80855/1624089498.py\", line 9, in load_data\n",
+      "  File \"/var/folders/5y/8wf3zbrx6k91qcb2jgf69fl40000gn/T/ipykernel_16494/1624089498.py\", line 9, in load_data\n",
       "    alignments = load_alignments(alignment_path)\n",
       "                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
       "\n",
-      "  File \"/var/folders/5y/8wf3zbrx6k91qcb2jgf69fl40000gn/T/ipykernel_80855/1342149016.py\", line 2, in load_alignments\n",
+      "  File \"/var/folders/5y/8wf3zbrx6k91qcb2jgf69fl40000gn/T/ipykernel_16494/1342149016.py\", line 2, in load_alignments\n",
       "    with open(path, 'r') as f:\n",
       "         ^^^^^^^^^^^^^^^\n",
       "\n",
@@ -1839,19 +1831,19 @@
     },
     {
      "ename": "UnknownError",
-     "evalue": "{{function_node __wrapped__IteratorGetNext_output_types_2_device_/job:localhost/replica:0/task:0/device:CPU:0}} FileNotFoundError: [Errno 2] No such file or directory: 'data/alignments/s1/.align'\nTraceback (most recent call last):\n\n  File \"/Users/lihaidun/NSCC/CodeSpace/LipNet/.venv/lib/python3.12/site-packages/tensorflow/python/ops/script_ops.py\", line 268, in __call__\n    return func(device, token, args)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^\n\n  File \"/Users/lihaidun/NSCC/CodeSpace/LipNet/.venv/lib/python3.12/site-packages/tensorflow/python/ops/script_ops.py\", line 146, in __call__\n    outputs = self._call(device, args)\n              ^^^^^^^^^^^^^^^^^^^^^^^^\n\n  File \"/Users/lihaidun/NSCC/CodeSpace/LipNet/.venv/lib/python3.12/site-packages/tensorflow/python/ops/script_ops.py\", line 153, in _call\n    ret = self._func(*args)\n          ^^^^^^^^^^^^^^^^^\n\n  File \"/Users/lihaidun/NSCC/CodeSpace/LipNet/.venv/lib/python3.12/site-packages/tensorflow/python/autograph/impl/api.py\", line 643, in wrapper\n    return func(*args, **kwargs)\n           ^^^^^^^^^^^^^^^^^^^^^\n\n  File \"/var/folders/5y/8wf3zbrx6k91qcb2jgf69fl40000gn/T/ipykernel_80855/1624089498.py\", line 9, in load_data\n    alignments = load_alignments(alignment_path)\n                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\n  File \"/var/folders/5y/8wf3zbrx6k91qcb2jgf69fl40000gn/T/ipykernel_80855/1342149016.py\", line 2, in load_alignments\n    with open(path, 'r') as f:\n         ^^^^^^^^^^^^^^^\n\n  File \"/Users/lihaidun/NSCC/CodeSpace/LipNet/.venv/lib/python3.12/site-packages/IPython/core/interactiveshell.py\", line 324, in _modified_open\n    return io_open(file, *args, **kwargs)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\nFileNotFoundError: [Errno 2] No such file or directory: 'data/alignments/s1/.align'\n\n\n\t [[{{node EagerPyFunc}}]] [Op:IteratorGetNext] name: ",
+     "evalue": "{{function_node __wrapped__IteratorGetNext_output_types_2_device_/job:localhost/replica:0/task:0/device:CPU:0}} FileNotFoundError: [Errno 2] No such file or directory: 'data/alignments/s1/.align'\nTraceback (most recent call last):\n\n  File \"/Users/lihaidun/NSCC/CodeSpace/LipNet/.venv/lib/python3.12/site-packages/tensorflow/python/ops/script_ops.py\", line 268, in __call__\n    return func(device, token, args)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^\n\n  File \"/Users/lihaidun/NSCC/CodeSpace/LipNet/.venv/lib/python3.12/site-packages/tensorflow/python/ops/script_ops.py\", line 146, in __call__\n    outputs = self._call(device, args)\n              ^^^^^^^^^^^^^^^^^^^^^^^^\n\n  File \"/Users/lihaidun/NSCC/CodeSpace/LipNet/.venv/lib/python3.12/site-packages/tensorflow/python/ops/script_ops.py\", line 153, in _call\n    ret = self._func(*args)\n          ^^^^^^^^^^^^^^^^^\n\n  File \"/Users/lihaidun/NSCC/CodeSpace/LipNet/.venv/lib/python3.12/site-packages/tensorflow/python/autograph/impl/api.py\", line 643, in wrapper\n    return func(*args, **kwargs)\n           ^^^^^^^^^^^^^^^^^^^^^\n\n  File \"/var/folders/5y/8wf3zbrx6k91qcb2jgf69fl40000gn/T/ipykernel_16494/1624089498.py\", line 9, in load_data\n    alignments = load_alignments(alignment_path)\n                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\n  File \"/var/folders/5y/8wf3zbrx6k91qcb2jgf69fl40000gn/T/ipykernel_16494/1342149016.py\", line 2, in load_alignments\n    with open(path, 'r') as f:\n         ^^^^^^^^^^^^^^^\n\n  File \"/Users/lihaidun/NSCC/CodeSpace/LipNet/.venv/lib/python3.12/site-packages/IPython/core/interactiveshell.py\", line 324, in _modified_open\n    return io_open(file, *args, **kwargs)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\nFileNotFoundError: [Errno 2] No such file or directory: 'data/alignments/s1/.align'\n\n\n\t [[{{node EagerPyFunc}}]] [Op:IteratorGetNext] name: ",
      "output_type": "error",
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mUnknownError\u001b[0m                              Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[53], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m frames, alignments \u001b[38;5;241m=\u001b[39m \u001b[43mdata\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mas_numpy_iterator\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnext\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
+      "Cell \u001b[0;32mIn[24], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m frames, alignments \u001b[38;5;241m=\u001b[39m \u001b[43mdata\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mas_numpy_iterator\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnext\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
       "File \u001b[0;32m~/NSCC/CodeSpace/LipNet/.venv/lib/python3.12/site-packages/tensorflow/python/data/ops/dataset_ops.py:4729\u001b[0m, in \u001b[0;36mNumpyIterator.next\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   4728\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mnext\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[0;32m-> 4729\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;21;43m__next__\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
       "File \u001b[0;32m~/NSCC/CodeSpace/LipNet/.venv/lib/python3.12/site-packages/tensorflow/python/data/ops/dataset_ops.py:4726\u001b[0m, in \u001b[0;36mNumpyIterator.__next__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   4723\u001b[0m     numpy\u001b[38;5;241m.\u001b[39msetflags(write\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[1;32m   4724\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m numpy\n\u001b[0;32m-> 4726\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m nest\u001b[38;5;241m.\u001b[39mmap_structure(to_numpy, \u001b[38;5;28;43mnext\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_iterator\u001b[49m\u001b[43m)\u001b[49m)\n",
       "File \u001b[0;32m~/NSCC/CodeSpace/LipNet/.venv/lib/python3.12/site-packages/tensorflow/python/data/ops/iterator_ops.py:809\u001b[0m, in \u001b[0;36mOwnedIterator.__next__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    807\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__next__\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m    808\u001b[0m   \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 809\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_next_internal\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    810\u001b[0m   \u001b[38;5;28;01mexcept\u001b[39;00m errors\u001b[38;5;241m.\u001b[39mOutOfRangeError:\n\u001b[1;32m    811\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mStopIteration\u001b[39;00m\n",
       "File \u001b[0;32m~/NSCC/CodeSpace/LipNet/.venv/lib/python3.12/site-packages/tensorflow/python/data/ops/iterator_ops.py:772\u001b[0m, in \u001b[0;36mOwnedIterator._next_internal\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    769\u001b[0m \u001b[38;5;66;03m# TODO(b/77291417): This runs in sync mode as iterators use an error status\u001b[39;00m\n\u001b[1;32m    770\u001b[0m \u001b[38;5;66;03m# to communicate that there is no more data to iterate over.\u001b[39;00m\n\u001b[1;32m    771\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m context\u001b[38;5;241m.\u001b[39mexecution_mode(context\u001b[38;5;241m.\u001b[39mSYNC):\n\u001b[0;32m--> 772\u001b[0m   ret \u001b[38;5;241m=\u001b[39m \u001b[43mgen_dataset_ops\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43miterator_get_next\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    773\u001b[0m \u001b[43m      \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_iterator_resource\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    774\u001b[0m \u001b[43m      \u001b[49m\u001b[43moutput_types\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_flat_output_types\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    775\u001b[0m \u001b[43m      \u001b[49m\u001b[43moutput_shapes\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_flat_output_shapes\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    777\u001b[0m   \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m    778\u001b[0m     \u001b[38;5;66;03m# Fast path for the case `self._structure` is not a nested structure.\u001b[39;00m\n\u001b[1;32m    779\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_element_spec\u001b[38;5;241m.\u001b[39m_from_compatible_tensor_list(ret)  \u001b[38;5;66;03m# pylint: disable=protected-access\u001b[39;00m\n",
       "File \u001b[0;32m~/NSCC/CodeSpace/LipNet/.venv/lib/python3.12/site-packages/tensorflow/python/ops/gen_dataset_ops.py:3086\u001b[0m, in \u001b[0;36miterator_get_next\u001b[0;34m(iterator, output_types, output_shapes, name)\u001b[0m\n\u001b[1;32m   3084\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m _result\n\u001b[1;32m   3085\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m _core\u001b[38;5;241m.\u001b[39m_NotOkStatusException \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m-> 3086\u001b[0m   \u001b[43m_ops\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mraise_from_not_ok_status\u001b[49m\u001b[43m(\u001b[49m\u001b[43me\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mname\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   3087\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m _core\u001b[38;5;241m.\u001b[39m_FallbackException:\n\u001b[1;32m   3088\u001b[0m   \u001b[38;5;28;01mpass\u001b[39;00m\n",
       "File \u001b[0;32m~/NSCC/CodeSpace/LipNet/.venv/lib/python3.12/site-packages/tensorflow/python/framework/ops.py:5983\u001b[0m, in \u001b[0;36mraise_from_not_ok_status\u001b[0;34m(e, name)\u001b[0m\n\u001b[1;32m   5981\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mraise_from_not_ok_status\u001b[39m(e, name) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m NoReturn:\n\u001b[1;32m   5982\u001b[0m   e\u001b[38;5;241m.\u001b[39mmessage \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m (\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m name: \u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mstr\u001b[39m(name \u001b[38;5;28;01mif\u001b[39;00m name \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m\"\u001b[39m))\n\u001b[0;32m-> 5983\u001b[0m   \u001b[38;5;28;01mraise\u001b[39;00m core\u001b[38;5;241m.\u001b[39m_status_to_exception(e) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n",
-      "\u001b[0;31mUnknownError\u001b[0m: {{function_node __wrapped__IteratorGetNext_output_types_2_device_/job:localhost/replica:0/task:0/device:CPU:0}} FileNotFoundError: [Errno 2] No such file or directory: 'data/alignments/s1/.align'\nTraceback (most recent call last):\n\n  File \"/Users/lihaidun/NSCC/CodeSpace/LipNet/.venv/lib/python3.12/site-packages/tensorflow/python/ops/script_ops.py\", line 268, in __call__\n    return func(device, token, args)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^\n\n  File \"/Users/lihaidun/NSCC/CodeSpace/LipNet/.venv/lib/python3.12/site-packages/tensorflow/python/ops/script_ops.py\", line 146, in __call__\n    outputs = self._call(device, args)\n              ^^^^^^^^^^^^^^^^^^^^^^^^\n\n  File \"/Users/lihaidun/NSCC/CodeSpace/LipNet/.venv/lib/python3.12/site-packages/tensorflow/python/ops/script_ops.py\", line 153, in _call\n    ret = self._func(*args)\n          ^^^^^^^^^^^^^^^^^\n\n  File \"/Users/lihaidun/NSCC/CodeSpace/LipNet/.venv/lib/python3.12/site-packages/tensorflow/python/autograph/impl/api.py\", line 643, in wrapper\n    return func(*args, **kwargs)\n           ^^^^^^^^^^^^^^^^^^^^^\n\n  File \"/var/folders/5y/8wf3zbrx6k91qcb2jgf69fl40000gn/T/ipykernel_80855/1624089498.py\", line 9, in load_data\n    alignments = load_alignments(alignment_path)\n                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\n  File \"/var/folders/5y/8wf3zbrx6k91qcb2jgf69fl40000gn/T/ipykernel_80855/1342149016.py\", line 2, in load_alignments\n    with open(path, 'r') as f:\n         ^^^^^^^^^^^^^^^\n\n  File \"/Users/lihaidun/NSCC/CodeSpace/LipNet/.venv/lib/python3.12/site-packages/IPython/core/interactiveshell.py\", line 324, in _modified_open\n    return io_open(file, *args, **kwargs)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\nFileNotFoundError: [Errno 2] No such file or directory: 'data/alignments/s1/.align'\n\n\n\t [[{{node EagerPyFunc}}]] [Op:IteratorGetNext] name: "
+      "\u001b[0;31mUnknownError\u001b[0m: {{function_node __wrapped__IteratorGetNext_output_types_2_device_/job:localhost/replica:0/task:0/device:CPU:0}} FileNotFoundError: [Errno 2] No such file or directory: 'data/alignments/s1/.align'\nTraceback (most recent call last):\n\n  File \"/Users/lihaidun/NSCC/CodeSpace/LipNet/.venv/lib/python3.12/site-packages/tensorflow/python/ops/script_ops.py\", line 268, in __call__\n    return func(device, token, args)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^\n\n  File \"/Users/lihaidun/NSCC/CodeSpace/LipNet/.venv/lib/python3.12/site-packages/tensorflow/python/ops/script_ops.py\", line 146, in __call__\n    outputs = self._call(device, args)\n              ^^^^^^^^^^^^^^^^^^^^^^^^\n\n  File \"/Users/lihaidun/NSCC/CodeSpace/LipNet/.venv/lib/python3.12/site-packages/tensorflow/python/ops/script_ops.py\", line 153, in _call\n    ret = self._func(*args)\n          ^^^^^^^^^^^^^^^^^\n\n  File \"/Users/lihaidun/NSCC/CodeSpace/LipNet/.venv/lib/python3.12/site-packages/tensorflow/python/autograph/impl/api.py\", line 643, in wrapper\n    return func(*args, **kwargs)\n           ^^^^^^^^^^^^^^^^^^^^^\n\n  File \"/var/folders/5y/8wf3zbrx6k91qcb2jgf69fl40000gn/T/ipykernel_16494/1624089498.py\", line 9, in load_data\n    alignments = load_alignments(alignment_path)\n                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\n  File \"/var/folders/5y/8wf3zbrx6k91qcb2jgf69fl40000gn/T/ipykernel_16494/1342149016.py\", line 2, in load_alignments\n    with open(path, 'r') as f:\n         ^^^^^^^^^^^^^^^\n\n  File \"/Users/lihaidun/NSCC/CodeSpace/LipNet/.venv/lib/python3.12/site-packages/IPython/core/interactiveshell.py\", line 324, in _modified_open\n    return io_open(file, *args, **kwargs)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\nFileNotFoundError: [Errno 2] No such file or directory: 'data/alignments/s1/.align'\n\n\n\t [[{{node EagerPyFunc}}]] [Op:IteratorGetNext] name: "
      ]
     }
    ],
@@ -1861,7 +1853,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": null,
    "id": "cbebe683-6afd-47fd-bba4-c83b4b13bb32",
    "metadata": {},
    "outputs": [
@@ -1882,7 +1874,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": null,
    "id": "5cf2d676-93a9-434c-b3c7-bdcc2577b2e7",
    "metadata": {
     "tags": []
@@ -1894,7 +1886,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": null,
    "id": "efa6cd46-7079-46c0-b45b-832f339f6cb0",
    "metadata": {
     "scrolled": true,
@@ -2036,7 +2028,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": null,
    "id": "acf5eb4f-a0da-4a9a-bf24-af13e9cc2fbe",
    "metadata": {
     "tags": []

From 7399d2972defd1466be38a29830ae93df0063e36 Mon Sep 17 00:00:00 2001
From: Haidun Li 
Date: Fri, 21 Jun 2024 15:00:23 -0300
Subject: [PATCH 17/17] Update ipynb

---
 LipNet_my.ipynb | 386 ++++++++++++++++++++----------------------------
 1 file changed, 158 insertions(+), 228 deletions(-)

diff --git a/LipNet_my.ipynb b/LipNet_my.ipynb
index ac240332..a67e8019 100755
--- a/LipNet_my.ipynb
+++ b/LipNet_my.ipynb
@@ -39,172 +39,87 @@
           "name": "stdout",
           "output_type": "stream",
           "text": [
-            "Package                   Version\n",
-            "------------------------- --------------\n",
-            "absl-py                   2.1.0\n",
-            "altair                    5.3.0\n",
-            "anyio                     4.3.0\n",
-            "argon2-cffi               23.1.0\n",
-            "argon2-cffi-bindings      21.2.0\n",
-            "arrow                     1.3.0\n",
-            "asttokens                 2.4.1\n",
-            "astunparse                1.6.3\n",
-            "async-lru                 2.0.4\n",
-            "attrs                     23.2.0\n",
-            "av                        12.0.0\n",
-            "Babel                     2.14.0\n",
-            "beautifulsoup4            4.12.3\n",
-            "bleach                    6.1.0\n",
-            "blinker                   1.7.0\n",
-            "cachetools                5.3.3\n",
-            "certifi                   2024.2.2\n",
-            "cffi                      1.16.0\n",
-            "charset-normalizer        3.3.2\n",
-            "click                     8.1.7\n",
-            "colorama                  0.4.6\n",
-            "comm                      0.2.2\n",
-            "contourpy                 1.2.1\n",
-            "cycler                    0.12.1\n",
-            "debugpy                   1.8.1\n",
-            "decorator                 5.1.1\n",
-            "defusedxml                0.7.1\n",
-            "executing                 2.0.1\n",
-            "fastjsonschema            2.19.1\n",
-            "filelock                  3.13.4\n",
-            "flatbuffers               24.3.25\n",
-            "fonttools                 4.51.0\n",
-            "fqdn                      1.5.1\n",
-            "fsspec                    2024.3.1\n",
-            "gast                      0.5.4\n",
-            "gdown                     5.1.0\n",
-            "gitdb                     4.0.11\n",
-            "GitPython                 3.1.43\n",
-            "google-pasta              0.2.0\n",
-            "grpcio                    1.62.1\n",
-            "h11                       0.14.0\n",
-            "h5py                      3.11.0\n",
-            "httpcore                  1.0.5\n",
-            "httpx                     0.27.0\n",
-            "idna                      3.7\n",
-            "imageio                   2.23.0\n",
-            "ipykernel                 6.29.4\n",
-            "ipynb-py-convert          0.4.6\n",
-            "ipython                   8.23.0\n",
-            "ipywidgets                8.1.2\n",
-            "isoduration               20.11.0\n",
-            "jedi                      0.19.1\n",
-            "Jinja2                    3.1.3\n",
-            "json5                     0.9.24\n",
-            "jsonpointer               2.4\n",
-            "jsonschema                4.21.1\n",
-            "jsonschema-specifications 2023.12.1\n",
-            "jupyter                   1.0.0\n",
-            "jupyter_client            8.6.1\n",
-            "jupyter-console           6.6.3\n",
-            "jupyter_core              5.7.2\n",
-            "jupyter-events            0.10.0\n",
-            "jupyter-lsp               2.2.5\n",
-            "jupyter_server            2.13.0\n",
-            "jupyter_server_terminals  0.5.3\n",
-            "jupyterlab                4.1.6\n",
-            "jupyterlab_pygments       0.3.0\n",
-            "jupyterlab_server         2.26.0\n",
-            "jupyterlab_widgets        3.0.10\n",
-            "kiwisolver                1.4.5\n",
-            "libclang                  18.1.1\n",
-            "Markdown                  3.6\n",
-            "markdown-it-py            3.0.0\n",
-            "MarkupSafe                2.1.5\n",
-            "matplotlib                3.8.4\n",
-            "matplotlib-inline         0.1.6\n",
-            "mdurl                     0.1.2\n",
-            "mistune                   3.0.2\n",
-            "ml-dtypes                 0.3.2\n",
-            "mpmath                    1.3.0\n",
-            "namex                     0.0.7\n",
-            "nbclient                  0.10.0\n",
-            "nbconvert                 7.16.3\n",
-            "nbformat                  5.10.4\n",
-            "nest-asyncio              1.6.0\n",
-            "networkx                  3.3\n",
-            "notebook                  7.1.2\n",
-            "notebook_shim             0.2.4\n",
-            "numpy                     1.26.4\n",
-            "opencv-python             4.6.0.66\n",
-            "opt-einsum                3.3.0\n",
-            "optree                    0.11.0\n",
-            "overrides                 7.7.0\n",
-            "packaging                 24.0\n",
-            "pandas                    2.2.1\n",
-            "pandocfilters             1.5.1\n",
-            "parso                     0.8.4\n",
-            "pillow                    10.3.0\n",
-            "pip                       24.0\n",
-            "platformdirs              4.2.0\n",
-            "prometheus_client         0.20.0\n",
-            "prompt-toolkit            3.0.43\n",
-            "protobuf                  4.25.3\n",
-            "psutil                    5.9.8\n",
-            "pure-eval                 0.2.2\n",
-            "pyarrow                   15.0.2\n",
-            "pycparser                 2.22\n",
-            "pydeck                    0.8.1b0\n",
-            "Pygments                  2.17.2\n",
-            "pyparsing                 3.1.2\n",
-            "PySocks                   1.7.1\n",
-            "python-dateutil           2.9.0.post0\n",
-            "python-json-logger        2.0.7\n",
-            "pytz                      2024.1\n",
-            "pywin32                   306\n",
-            "pywinpty                  2.0.13\n",
-            "PyYAML                    6.0.1\n",
-            "pyzmq                     25.1.2\n",
-            "qtconsole                 5.5.1\n",
-            "QtPy                      2.4.1\n",
-            "referencing               0.34.0\n",
-            "requests                  2.31.0\n",
-            "rfc3339-validator         0.1.4\n",
-            "rfc3986-validator         0.1.1\n",
-            "rich                      13.7.1\n",
-            "rpds-py                   0.18.0\n",
-            "Send2Trash                1.8.3\n",
-            "setuptools                69.2.0\n",
-            "six                       1.16.0\n",
-            "smmap                     5.0.1\n",
-            "sniffio                   1.3.1\n",
-            "soupsieve                 2.5\n",
-            "stack-data                0.6.3\n",
-            "streamlit                 1.33.0\n",
-            "sympy                     1.12\n",
-            "tenacity                  8.2.3\n",
-            "tensorboard               2.16.2\n",
-            "tensorboard-data-server   0.7.2\n",
-            "tensorflow                2.16.1\n",
-            "tensorflow-intel          2.16.1\n",
-            "termcolor                 2.4.0\n",
-            "terminado                 0.18.1\n",
-            "tinycss2                  1.2.1\n",
-            "toml                      0.10.2\n",
-            "toolz                     0.12.1\n",
-            "torch                     2.2.2\n",
-            "torchvision               0.17.2\n",
-            "tornado                   6.4\n",
-            "tqdm                      4.66.2\n",
-            "traitlets                 5.14.2\n",
-            "types-python-dateutil     2.9.0.20240316\n",
-            "typing_extensions         4.11.0\n",
-            "tzdata                    2024.1\n",
-            "uri-template              1.3.0\n",
-            "urllib3                   2.2.1\n",
-            "watchdog                  4.0.0\n",
-            "wcwidth                   0.2.13\n",
-            "webcolors                 1.13\n",
-            "webencodings              0.5.1\n",
-            "websocket-client          1.7.0\n",
-            "Werkzeug                  3.0.2\n",
-            "wheel                     0.43.0\n",
-            "widgetsnbextension        4.0.10\n",
-            "wrapt                     1.16.0\n"
+            "Package                 Version\n",
+            "----------------------- -----------\n",
+            "absl-py                 2.1.0\n",
+            "appnope                 0.1.4\n",
+            "asttokens               2.4.1\n",
+            "astunparse              1.6.3\n",
+            "beautifulsoup4          4.12.3\n",
+            "certifi                 2024.6.2\n",
+            "charset-normalizer      3.3.2\n",
+            "comm                    0.2.2\n",
+            "contourpy               1.2.1\n",
+            "cycler                  0.12.1\n",
+            "debugpy                 1.8.1\n",
+            "decorator               5.1.1\n",
+            "executing               2.0.1\n",
+            "filelock                3.15.3\n",
+            "flatbuffers             24.3.25\n",
+            "fonttools               4.53.0\n",
+            "gast                    0.5.4\n",
+            "gdown                   5.2.0\n",
+            "google-pasta            0.2.0\n",
+            "grpcio                  1.64.1\n",
+            "h5py                    3.11.0\n",
+            "idna                    3.7\n",
+            "imageio                 2.34.1\n",
+            "ipykernel               6.29.4\n",
+            "ipython                 8.25.0\n",
+            "jedi                    0.19.1\n",
+            "jupyter_client          8.6.2\n",
+            "jupyter_core            5.7.2\n",
+            "keras                   3.3.3\n",
+            "kiwisolver              1.4.5\n",
+            "libclang                18.1.1\n",
+            "Markdown                3.6\n",
+            "markdown-it-py          3.0.0\n",
+            "MarkupSafe              2.1.5\n",
+            "matplotlib              3.9.0\n",
+            "matplotlib-inline       0.1.7\n",
+            "mdurl                   0.1.2\n",
+            "ml-dtypes               0.3.2\n",
+            "namex                   0.0.8\n",
+            "nest-asyncio            1.6.0\n",
+            "numpy                   1.26.4\n",
+            "opencv-python           4.10.0.84\n",
+            "opt-einsum              3.3.0\n",
+            "optree                  0.11.0\n",
+            "packaging               24.1\n",
+            "parso                   0.8.4\n",
+            "pexpect                 4.9.0\n",
+            "pillow                  10.3.0\n",
+            "pip                     24.1\n",
+            "platformdirs            4.2.2\n",
+            "prompt_toolkit          3.0.47\n",
+            "protobuf                4.25.3\n",
+            "psutil                  6.0.0\n",
+            "ptyprocess              0.7.0\n",
+            "pure-eval               0.2.2\n",
+            "Pygments                2.18.0\n",
+            "pyparsing               3.1.2\n",
+            "PySocks                 1.7.1\n",
+            "python-dateutil         2.9.0.post0\n",
+            "pyzmq                   26.0.3\n",
+            "requests                2.32.3\n",
+            "rich                    13.7.1\n",
+            "setuptools              70.1.0\n",
+            "six                     1.16.0\n",
+            "soupsieve               2.5\n",
+            "stack-data              0.6.3\n",
+            "tensorboard             2.16.2\n",
+            "tensorboard-data-server 0.7.2\n",
+            "tensorflow              2.16.1\n",
+            "termcolor               2.4.0\n",
+            "tornado                 6.4.1\n",
+            "tqdm                    4.66.4\n",
+            "traitlets               5.14.3\n",
+            "typing_extensions       4.12.2\n",
+            "urllib3                 2.2.2\n",
+            "wcwidth                 0.2.13\n",
+            "Werkzeug                3.0.3\n",
+            "wheel                   0.43.0\n",
+            "wrapt                   1.16.0\n"
           ]
         }
       ],
@@ -297,7 +212,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 3,
+      "execution_count": 5,
       "id": "b24af50c-20b8-409d-ad78-30a933fdd669",
       "metadata": {
         "id": "b24af50c-20b8-409d-ad78-30a933fdd669",
@@ -316,7 +231,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 52,
+      "execution_count": 27,
       "id": "1e3db0b0-e559-4ad6-91fd-e7414b7d75e6",
       "metadata": {
         "colab": {
@@ -332,7 +247,7 @@
               "[]"
             ]
           },
-          "execution_count": 52,
+          "execution_count": 27,
           "metadata": {},
           "output_type": "execute_result"
         }
@@ -343,7 +258,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 5,
+      "execution_count": 7,
       "id": "378d045a-3003-4f93-b7d2-a25a97774a68",
       "metadata": {
         "id": "378d045a-3003-4f93-b7d2-a25a97774a68",
@@ -371,7 +286,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 6,
+      "execution_count": 8,
       "id": "8fb99c90-e05a-437f-839d-6e772f8c1dd5",
       "metadata": {
         "id": "8fb99c90-e05a-437f-839d-6e772f8c1dd5",
@@ -384,7 +299,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 7,
+      "execution_count": 9,
       "id": "c019e4c6-2af3-4160-99ea-5c8cb009f1a7",
       "metadata": {
         "colab": {
@@ -404,7 +319,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 8,
+      "execution_count": 10,
       "id": "8548cc59-6dfc-4acc-abc3-3e65212db02e",
       "metadata": {
         "id": "8548cc59-6dfc-4acc-abc3-3e65212db02e",
@@ -429,7 +344,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 9,
+      "execution_count": 11,
       "id": "ec735e0b-ec98-4eb0-8f49-c35527d6670a",
       "metadata": {
         "id": "ec735e0b-ec98-4eb0-8f49-c35527d6670a",
@@ -442,7 +357,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 10,
+      "execution_count": 12,
       "id": "be04e972-d7a5-4a72-82d8-a6bdde1f3ce6",
       "metadata": {
         "colab": {
@@ -475,7 +390,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 11,
+      "execution_count": 13,
       "id": "559f7420-6802-45fa-9ca0-b1ff209b461c",
       "metadata": {
         "colab": {
@@ -531,7 +446,7 @@
               " ' ']"
             ]
           },
-          "execution_count": 11,
+          "execution_count": 13,
           "metadata": {},
           "output_type": "execute_result"
         }
@@ -542,7 +457,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 12,
+      "execution_count": 14,
       "id": "797ff78b-b48f-4e14-bb62-8cd0ebf9501a",
       "metadata": {
         "colab": {
@@ -556,10 +471,10 @@
         {
           "data": {
             "text/plain": [
-              ""
+              ""
             ]
           },
-          "execution_count": 12,
+          "execution_count": 14,
           "metadata": {},
           "output_type": "execute_result"
         }
@@ -570,7 +485,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 13,
+      "execution_count": 15,
       "id": "8cd7f4f4-ae77-4509-a4f4-c723787ebad1",
       "metadata": {
         "colab": {
@@ -586,7 +501,7 @@
               ""
             ]
           },
-          "execution_count": 13,
+          "execution_count": 15,
           "metadata": {},
           "output_type": "execute_result"
         }
@@ -597,7 +512,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 14,
+      "execution_count": 16,
       "id": "9491bab5-6a3c-4f79-879a-8f9fbe73ae2e",
       "metadata": {
         "id": "9491bab5-6a3c-4f79-879a-8f9fbe73ae2e",
@@ -618,7 +533,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 15,
+      "execution_count": 17,
       "id": "dd01ca9f-77fb-4643-a2aa-47dd82c5d66b",
       "metadata": {
         "id": "dd01ca9f-77fb-4643-a2aa-47dd82c5d66b",
@@ -641,7 +556,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 16,
+      "execution_count": 18,
       "id": "8cb7cc58-31ae-4904-a805-1177a82717d2",
       "metadata": {
         "id": "8cb7cc58-31ae-4904-a805-1177a82717d2",
@@ -655,7 +570,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 17,
+      "execution_count": 19,
       "id": "76aa964f-0c84-490d-897a-d00e3966e2c9",
       "metadata": {
         "colab": {
@@ -672,7 +587,7 @@
               "'bbal6n'"
             ]
           },
-          "execution_count": 17,
+          "execution_count": 19,
           "metadata": {},
           "output_type": "execute_result"
         }
@@ -684,7 +599,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 18,
+      "execution_count": 20,
       "id": "eb602c71-8560-4f9e-b26b-08202febb937",
       "metadata": {
         "id": "eb602c71-8560-4f9e-b26b-08202febb937",
@@ -698,7 +613,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 19,
+      "execution_count": 21,
       "id": "0e3184a1-6b02-4b4f-84a8-a0a65f951ea2",
       "metadata": {
         "colab": {
@@ -712,16 +627,16 @@
         {
           "data": {
             "text/plain": [
-              ""
+              ""
             ]
           },
-          "execution_count": 19,
+          "execution_count": 21,
           "metadata": {},
           "output_type": "execute_result"
         },
         {
           "data": {
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",
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2N/JE7r4r2z3CXJtdLvCzwcYD0ZUPRVEURVFqik4+FEVRFEWpKTNWdvFKJfJ+tzxcqZxShXwwK0D3q9gEZSeXa5ir78FtjMsldEwu9QZxJqW42hlqj+NYV9uwzJVFmF8aM+xiBloutWD/uyKlVioJ4dKro618ARm7NAbnueQTLplUI+WkPcfng0V5ROHkaCCllQKL3InyBcoSssxuY1bfViatNPkyWygnFpd90exLuXCYtTUA+YBHQ0VppZVlrsUyzjyQfVqY1JADuYTfL/7XmITnnWXdgdmQU0Iuk3BppWBk3/OopinHsw9LIrKPUdri8Ci2eI88G3PaqyJzbTVUKG+YIrr6OmKzisiost3cLbnFl+7L1WSYPhnRlQ9FURRFUWqKTj4URVEURakpOvlQFEVRFKWmzFibD4HLrmOiTId7LWcq3EJd7rTV1GMcLqOuOpibmhmVWrZfjL7HkOvt8WxCIo7zKuxTl7sw2niEcLnaci07FM6e9aPLHgU0560Hny9vv1uCkPVsGzX/BNjKCFdbdKdlh6J9QELYB0SHV88ZdCe1FWG4bSTNbDLeHxsVZU3MBmMehDPnGnlHrPK2HQ1sPZ0xOb6zJiP232Yh1QdZyHYiooOFU9g9yGtwd95OsOtIsL6ZBxlnU569xicWLhNl5DvsHGDc/GffjvI2v18ioiY/OmT/KOsrHENj7NgxI+1oeMbjBNh4oDtvwfE/Ly/BLMo8TH/Kg1r5ffiQEoK717pc98fb5/Ww6wclTHvB2uqwoTPwPT3K3JLnxSpPnzAbqHrl4+mnn6arrrqKFixYQJ7n0UMPPSTKjTH0ta99jU477TSqq6ujVatW0auvvjpZ7VUURVEU5SSn6snH2NgYffjDH6ZNmzaNW/73f//39J3vfIe++93v0q5du6ihoYFWr15N2Wx23OMVRVEURZlbVC27XH755XT55ZePW2aMobvuuov+5m/+hq655hoiIvq3f/s36ujooIceeog+/elPn1hr37vIxMpO5NipYKKZFicqQTkjk2LkSrZMGI9e6vUSuKDKgOVcv2D3gyQMO1RvxuySrknA9V3LorwM+5dJGyglGeYybJLR0RGJpNTjFR39hi67PAMwRkdkzxQzchaYyypGnMwwmSeAshTcf4L9n5GG/uautymInMnlnFNi9aKMR199oO+/RdlR1t8jkCm2BP/ztPtWljgrId1LuUvnJ7v+H9lwttS99UCvKDpcsnWOQJ9mWeTQQUK5Sj5T7vqJLrPvTx4ub6M7L3c3xeijaZa5tg/G0LyY/Sft4YN7RNm177f37yWlWya6fv7vJR9jZbLdUsoDmYn11QhEKm3yo12G+RgahCzCSANzn0aJpsCeRxa+IfXMvZa/F0SyPzx8Z7lEgmqJK+IpZPQ2eXv/eB5/b/2GhsjzkAx7N0pwT+gu7wK/G5XiMRlqonVMlEk1OH399depv7+fVq1aVf5dS0sLrVy5knbs2OE4U1EURVGUucKkGpz29/cTEVFHR4f4fUdHR7kMyeVylMvZ/3aGh4cns0mKoiiKoswwpt3VduPGjdTS0lL+6erqmu4mKYqiKIoyhUzqykdnZycREQ0MDNBpp51W/v3AwACdd955456zYcMGWr9+fXl/eHh4bkxAXDYYjiyIwgbBFRYdwXom6urrOo/bPBSkBh0btatbpQapV2M0Zud9cFc5l2cxhjrndi2uvkfX4lC4dcc1XfB2g17M7UG8GGQ5ZRo4urpyD2W0B3GRBW2Z3xK62ibZJUcDaTT+nwd2suvLtrUxm5OOGNijQAj1q05fWd5GW4Ygy21AZOd//82ny9u/LchrjLGQ7SOBrPNIqbG8nQH7hDZw9eUZcXk4dSKiebGR8nYa7ukoy87rw6DJsAd3pCTtA94OrH1AAbLa/n+vPFXeboKw8x0xeY9rzvq4vX6jvMYVS609yCMvPyXKhKsx2Hjw7LRZg2HCeTZc+c6grQw/F+uRNiCYGdrux6BPDVs9pzi42lZhXycyTKOtBrfVAjdgz7ftcdl4hGzh2H1gpmC0a5kKam3nwZnUlY8lS5ZQZ2cnbdu2rfy74eFh2rVrF/X09Ix7TiqVoubmZvGjKIqiKMrspeqVj9HRUfrNb35T3n/99ddp79691NbWRt3d3XTrrbfSN7/5TTrzzDNpyZIl9NWvfpUWLFhA11577WS2W1EURVGUk5SqJx/PPvssffzjdknvd5LJ2rVr6b777qMvfelLNDY2Rp///OdpcHCQPvrRj9Ljjz9O6XQ6qkpFURRFUeYQnjHTHfBCMjw8TC0tLXTpWespHnP7i5/UuOwaCtFpxCeMS/d02Y5gGY+X4WhncHRQVtllbYAK86QG7UHodT9rNdMgBfot96fPyNgK/sixyPaIWBoYu4TbYICNh/HR5oNpthgfhfeNo4yyMpaFYVqyOSbtKoKM1eB/eEC6qx9g3V8PNgdtMdSPA7Yt+5ufmYGYFPVe9OeBxwCpB716zaKLyts+/OOBdh0ctEH4bdE+U7QP6Cu2lrfHwHaD23JgWY7FVsgE0W0hIkrwFO9gZ1DPYnvEwB4FbUmiQLuORhbnIzBy7DXFosf3vLj0EuyKD9ptCNt9/Rn2n0eMV/HT39h4LTxWChHRkZJ93hkThzL7TqM9Btp88BgoCRi3vKwewuk3sbGYBpuLZt+OsStPXyHKRIwdjOuB30Vu1wFl3I7Ng+9E4IgBwu29/HoZK+cbz20tb58H8Y9yxl7vuiUfJRcGw70zPAwpz8/jNh9mogZtlqIp0FPmIRoaGjquCcW0e7soiqIoijK30MmHoiiKoig15eTIajsbqTQDrUMVwwysrlDoTqoJ086XIp2uvXIJz8vY5WSvILODBhDS3OfKAypCPBx1hZl5Q1RxnhfKTjvBpUleDy7L8tDY2LYKn00WlugzgRxfBXFsdJ0obcTIti0Fp/Gsp1xmIYLlZRgLD768TewPlKwM9fOsXKp9o7CkvI3yyVBRLmFz3mVlKG1wieRoQUqAhUAem2JL/6mQG7R9pgG4GvN6UK4RbcFw9ux6KOXEmTsnZtEdKLSI/X3egvJ2S0yGUP9/X3mmvP2BBH6HrHzBswYTEflkZaaRIDqc/BvFdlE2RvK5NbMXPA0uw76QVmTfuMKNF5l4iBKEkF0cMvJ7xSyjN7rrM1nC4DvEJQso8+L2/k1e9huOzbmErnwoiqIoilJTdPKhKIqiKEpN0cmHoiiKoig1RW0+pguuPTrDicP8kLuMxuC8oEJ7iErKK8BlY+LVQVwXZisRy0i9Ot8iteX4qMM1jLmNhewxIq733kVZP1bjXY66b1SdWG9IW3Zck9mAoJse38fw6uSyOYAjuZ0HpnhHuwPOCHNL9cHOIMHG5mMHnxNlx4zVtl8Dj+xHx+aL/b5Cm203aOAHc6eUtwNo91jRaunv5qUdEefMprdlu5l758LYoChDN9FMyY7No3lpHzLGynwwThpk7Un60gahPm77xoe+5/foKkP7k6TvcHsHe6BXjlm398Xpd0TZB1MHy9tnJKRLeBuzAYnB9UbYeM9j6PWg8hhPTcweBG2TRB/Dq5BlbqlV2XuhjRcvRzd7ZjtiitH97WF49wQ7D9zsOTFwH6733G7g4hqVutM6zqtBNHeBrnwoiqIoilJTdPKhKIqiKEpNmbmyS2CIHMvBk4pjyWrKmATZ44TqdMk+FWa8RVdfUQUsIfLlRv/dEVHmdcrlbJe04rqmrCTaRRazXJqEo9+wb/ipKO3wa6IrtchUjBl3mZsgtJsvmWLmWp5lFJfoccnalZG0nqKXkHm22gKsdQ8G9ry3jazjpcKp5e0Xj8ks1YfzTWKfRxzNBfKT9NqIddvEe8wV7bHvZqTsckq9jQZ66amviLKlqUPlbcxUmyDMTmsz4O6LnSbKuETUnToiyrhEMgKyQ4Hd4+GC7It3cvZ6CT96HXykIN1Xi4F0O07HozOrDhVse97KShfdN9L2uS1reEOUfSD1lq0fnkUmkP3IyRopH4wEXC6U7+IYOzbhDYoy7pabgu9LgtiY9h3uqxgJ1BX52SW5IhVKuSjXYDZozuqF55e3vZn7l3rC6MqHoiiKoig1RScfiqIoiqLUFJ18KIqiKIpSU2ahkjQBJhqmm8jtQjnVCYNd2WiRidqYTPAevCRowEw/RXezcJh4Oyc24M4aP8ayRxYdNhfYblaPQRsf1zPE6XkQfY1QJtsITMjmg+0XQKtnYdILkHWyxHVuuIVCSEu3rzq6s3LdGW0g+LH9JRn6nIc7P1psFGX7c9ZWY/+xNlFWBNfPfMle42hW2v9wW45CMVrLr0vJsNV1zObhcEG2m2eARTdUzLLK+6M+JsdtR3yovH1x3RuijIeiP1SS9hlvl6ydx8GC7JuXyIZFb4Tr8bbtK3WIsv4xeY98OCRj8p4aErav8uBaPODZtvXSYtnuor1Gyo+2KRktSRsXtGvg9jAYQp4P2wI8mzT7hgkbDyJK8KzKkFrAFF3fRXjBub0ZfDMNf08xRQK324LvAHeX/8mbu0XZnpx9UqsXLRNlWw/2lrcvg0y9swFd+VAURVEUpabo5ENRFEVRlJqisst4TLVcQjQ1rrYiIyNIGS6XshpgmOupycgsm/ERWMLlXYPN5v1WiHYRDbnJBZN0/1yyqUb24fslXBYujrtNRPQoW3p9F4O2otbCwKVuLh8MlqKzwWLZUMnKIPsynfJYlp14FFw/uStopiClnGJJLlmPZa175cJThkTZ/KbR8vbhESnt8Ltf3tEnyupidky9MLRAlP12bF55+9TUqCgbKUjJgLulZkvyPt7XKKODRlEw8jPLo6iiJPTCu7at6FrcwCKjDmRkXwwMtIp9E9jnH0/LMdXUaN2QG0Gu4s/t8DHpBvyyb59/OhYtu7SnxsQ+yke5hO3HErzg7TTKyqK/kUJmIaICC8/J3xnkEwultIFuuR6Tc0D1IVNkkm9cjoXHDthrXokSCfsWv1WS374Ei2KKUUq5DOPNwuS3uvKhKIqiKEpN0cmHoiiKoig1RScfiqIoiqLUFLX5IAqHyeYcz5210uy0gMfsFdCdtPJKHJkMMeNtNXYsFd4Td4lFPND1eWZHArsGvyBtIIqNjmyOaGchLhKdkZLbgHgQ+tzw8xwuwu/tV/i8sYzbeeB4Y+61rgyUGFI6xVwvc+A+ixlgucss2nWMBNZ2IxPIvn+Hhf8+nJN2BkeYWyzadSR8e4+jWWkPciwj99tarc5/QZu03WiLW/uBXYnFoixTtG298pRfiLIk65u7Rz8uyhri9nrzEzLUfwqytXK7i6GCfDY8FPp/FT4gyrgL7yBk3OXPJlOQ/X2UuRbn82ArErN9GoANkxeXY8rkY+xYsP/J22eVjMt3IcHccnMl/PNg+6YIRgg8U+8xsI1JgDvvCHPFxWzATb61R0GbphL7Lo0G0o6k3o8O7+47bEcwizS36TJoU+b4hl7RdWF5O9YM6SKO2XtKw3fhrAQLQQDvvmjbcVOAODKMu+xF+HcJ3Y5NFeHlJ4CufCiKoiiKUlN08qEoiqIoSk1R2WU8auBqy6WWSiNjzjScchHeE48wirLL8DG5n2DrhLDa6FWYcdfk5LIsj0goJCAit3xSaRZdbE8oOy3rD1xCdbhBBywCZA6WQXlG0DGITMqjjxJJaQVJe1b2aYjnoMw+K4xGOZ+5qR46Jl1GOXWQYfVoQso+TSl4Vgwe1bO74V1RtjBl9y9IHY6sY/W8X4n99yftsR9Oymy0AyAZ/DK3sLz9TlHe44KEvX4aIn4eYRFff5udL8q46ynKXMNFK0m8PCRdm1e2v1HexkisP3r9w2J/dNTW09wk36+mtL1+a1qWNSey5W0f5JLT0jYy7KKkfBYtMSuPYT9lIeNtwF5q7LcYk7nC7uLjH0dENBJY2ScB8kE9c2fdemivKLuse7nYF0mNg+iswiFJgu/D9QP8FlWKuEZ0RNUTwSXzTjW68qEoiqIoSk2pavKxceNGuvDCC6mpqYnmz59P1157Le3bt08ck81mad26ddTe3k6NjY20Zs0aGhgYmNRGK4qiKIpy8lLV5GP79u20bt062rlzJz3xxBNUKBToD//wD2lszC653XbbbfTII4/Qli1baPv27XTo0CG67rrrJr3hiqIoiqKcnFRl8/H444+L/fvuu4/mz59Pvb299LGPfYyGhobo3nvvpfvvv58uueQSIiLavHkznX322bRz50666KKLJtbKqbDB4Do/uFNOlg0Gz54aDnc+BeHVa4yzn9COgt2/B7YiXl7qvn7W2hkEaRiiLjdg/hyhTmJuipQEV15ujwJ1TvgpYcZbZtcRGs38mg73tjzUyTVxtPEYDKRdBXf9TIK9QINv9fL2mAw33pSyNgBjdbLfeJbT5xOny+sX2PXB3GS4Xra1GFg9e6woyw4GrZFlLfXcLVMSY316ZrJflLUz+4QWH926Zbjx7sRRimIpq7cJbBfejrFsvOAGfVHda+XtNghT/gazFdmROlOUXd70Qnl7BGxFXp4n7UNeS9iswp2N0p2Y23W0JsDmI85Cr0NY9LPSb5W3FyeiQ8sfYdl+32urHABjrO0Yep7bHyGur7K7LNp2ImTzIGw3osMV4Hk8vPrVyy4XZQ8fsJlscSz47P//kNsv5zi2GWaiNiCuv61TkQKEcUI2H0ND7w2ytrb30kL39vZSoVCgVatWlY9ZunQpdXd3044dO8atI5fL0fDwsPhRFEVRFGX2MuHJRxAEdOutt9LFF19M55xzDhER9ff3UzKZpNbWVnFsR0cH9ff3j1PLe3YkLS0t5Z+urq6JNklRFEVRlJOACbvarlu3jl588UV65plnTqgBGzZsoPXr15f3h4eHaz8BgeWlyXKDRaklCpRgKj2vKmqRqdd1PVek1GNZse9zV9i4jBYoXHjxGqzfDJbl7XK653A7DYHyGI+OOll9WuGSaQO48A05stoi3DUz4UlX5wafuV76chn+VCYLBCBJtPpMkgEZ4K3YKZFtQdfLgbyVb3KB/CTxzLn5QC5L8+irrxZl9NUk85nsK7SLsv5iS3m73tsvytKOleYMSFt59r9bHlKgDgfW1fWdoswOmxVSg5QZuDxW78v+fptlGMZ7akvKbKnUajdbQFqZl7TSGo8gS0TUkbCSSWtMli2OW/faJl+O2RIbiihBlQzIMMwt/GhJPrdKwf+aJy1YQYXRnbcewMy5TFaF78LbJft+tYHMF5tiaeOEmMj3rYpzJjT5uPnmm+nRRx+lp59+mhYtWlT+fWdnJ+XzeRocHBSrHwMDA9TZ2TlOTUSpVIpSqdS4ZYqiKIqizD6qkl2MMXTzzTfTgw8+SE8++SQtWbJElC9btowSiQRt27at/Lt9+/bR/v37qaenZ3JarCiKoijKSU1VKx/r1q2j+++/n3784x9TU1NT2Y6jpaWF6urqqKWlhW688UZav349tbW1UXNzM33xi1+knp6eiXu6KIqiKIoyq6hq8nHPPfcQEdHv//7vi99v3ryZPvvZzxIR0be+9S3yfZ/WrFlDuVyOVq9eTXfffXf1LTPG6kdTYa/gqPNkDXfupAobjCnp7zjT6+Ny2HnYlhGmNbdIl9FQllkOhG2PBG0s+PNOomsvLA6ykNOYHVce5+hfuL6pMGQ8kmZ2HM2+tJuJQWhsniE0ZPPB3Bvr/eg+xGXSTpYB9bz0AVkns1dAexAMjd0St/YK7xaljU9/ztqD+BD6/DCz+Xh6dKkoa4zZ/ngr3yrKuF1Je1y6FiP9BXvugXybKGuN2XZjmPBhlrk1U5L3/0bh1PL24ZJ0Zz1UsLYyAwUZpvydor3HEozLJDy399VbV9j6mLQdOSNlgz7Oi0vvQm7zkwaXbG7ngdlZuWlUIfStkftBYNs6CNYaGWMleLSxaWLjtAAu6S7bCd+xwO+BTZfh2aDhPnho9tULz4+sM3aqPI8nQ/7kohXRbUlNUpqHk4SqJh8hI75xSKfTtGnTJtq0adOEG6UoiqIoyuxFc7soiqIoilJTZm5WWy67TDVT5Grrbn+NXaxwWXIqXLz4/WLGW14GfWpAIjAjNiKjNx9cNlk9oWfDy+D6PKtsaAUviHbfNTFwg44OwOh2A+b7uITKZJjHDj4ninLsvKwj+mk9LPvXgwsnX0KPVeGim2TjJAWuvi2+dYNt9OTSfozs0v4RcG1OklzOX5x4u7z9dklKDfv808rbQ0VZD88I++Yx6Xpax6SGIkSVLLGl9d1j7ycXXDIZK0kZ4Dd+R+R5XBZB92EeGRYlsBHmoovn8eiY6IaL+y0sUukSyPj7vxJ2HyOsZlnfJGCcSNFLEmPfM5RAcNzyyLyFUGxaC2bV5dlwszCE07yt8GnLmeiXNhThlI3xrQelOy2vZ+vB50UZl2FMRro9Z1G6jQLbMguiYLvQlQ9FURRFUWqKTj4URVEURakpOvlQFEVRFKWmzFybj5mKy2W11iHMp4pK76ma++X2EOhqC/YZATs2pHry0OsBaLm83Zghkl8fXF2F7UgR7E98x/wcXW25XQlm1XXYa3DduQj2EAWKduflLWsA2wFsNQ8bjpp8iT3HEYN6vS2rh/NWLzivvP0o6OP1zAZkzIsOIY6gC2kLc2fFMt+LHn8xR8DtOuYyOgqZclPgsopuqhweJj6AfkuwazTGpTvtUMnarvhgV8HtGvDa/Fh07eX9RCSzxfLw+chRcF/m9hj1oTD8fCyAHQ1rWxDKviwpOJ4/t4FxPcMCfBm4zQe64XI7jgDrdLyXfHwTET3IstNedvpyONrepQGX/1affc8SkEWZXx/a4sWSrKjCMAInEbryoSiKoihKTdHJh6IoiqIoNUUnH4qiKIqi1JSTwuaDa/IG40dMBqBRVhXbo1K7B1c49+kInTtR+5SJnsfsIUIxODDuB7PJ8Iuo31bo++6DzUeBaeI4hgpMT43L87ykI7oB3keeafQB2oPYfjMY3p03xciyDKsHbTW4zo1xD3AE81oxsgKvt94RAwTDVD98cE95G0NqN7AYBQFJm4MsxN3gcUAwLPy8uI35guG2uX0E2kBwe4xSSbY7zjR4PA/jZfBYE67YGhivg9frYwhxdh9DJZk+oI3ZbmSNHHtpZjuD9i7NLCw6nsvtP94rs/tpaDePAYNh8LltUAnK0p6tMwNxNXKOT0arL21VEiweTauPsWPY9cHmg49hHPvCBgU/H47vGdoxZUz0e8vjfpTAduNIEP3N4vZeGOpd2K0V3DYfHjvW9X2piim2Z9SVD0VRFEVRaopOPhRFURRFqSkzVnbxgoC8cVytJhzufLJcRieJiu9jsto2WeHUK12KQymJu6Fi9lmUvfjyI7izmnq29A596PGVZ7iGCKkObrCea5kSljtFJlu8PnPLNdjfrmk+W6YtOWSPEvQTv4tq3goMk85BiYCDbopcakH3xryJXiIPLZmzeus9KdGMGCvJnAoZWEeYXDPKssgSEZXYPZ6akJlrfeGWKSUg3OfCB4b75qB8w/sxa6R7ZVNMZiDmlNhASYOLMpck0H22BAOMXx9dlMegPVHXwDD8ace4GQmiXZJdz98V6j/kksv6H0O/lxyfogR7F1HWxHQGMXaPJXSfdoSC5265WGejZ0eRl5B/crlE4tXJ9AFUsM8/dB7KMIbL2jEoMuMeF8L1NwLLJuHvkq58KIqiKIpSU3TyoSiKoihKTdHJh6IoiqIoNWXG2nxMCrV2J3Xh0symIr398ZhqOxes3xWmvIr7N3Fbj+dKWw/aZkgj5WXMHuWE+kVcX9ZjslajNzmp1z/MwjZnwFYm4XAhRBsQDvY2V4FD4acZWaizibksJyCkNjH9HAN48yuE9Hm4kwZm21CCccJTrqM9RitzS22PSbuOPDs2gN7gbrjozopwmwS0+eB2F2grw91bMUw42hLI67ncgO310OYj5E7L7nEQ3Hk5ibi0gajnzwJsNcbYuEmGEx/Y8wzaisjyJnaNAtiRYJh6WS8rg8O4Oy2OfT7eYjD2Sz68bybarmOUuRD/5M3douxIwA3OwHZDNBTsxLgtB9qeOb6Z4XAFfGeCdpGI62/UJLjh6sqHoiiKoig1RScfiqIoiqLUlBkruxjfP+FophN2Z61GBqnQPclA9DrvOBHr7IlTJI/U2r2YLSF6kNUWM9B6yWhXwCBhj/UTlQ9f4b6Ly5vM9dbLyaVuk45uSzhSqqMB3A3XEdE24aHLpD02B8upMXGcBEc+z1Ybg2fvesukRBM9ZsNZdO31siC7oHslv8fQsjhzt0TZIx8wSciXZWl2LEoS3A0XI4WitMPdXVEGcWVdTTKX1bxjKR/rdF2PSzt4T2MQ/ZXvo5TB5ZSwqzF39ZXvCY9UihFtubRyvGi7InIqPO+AjZsEPO+EiL4q63S52hZ4GZyH7xQvR9daLidd0XWhKLvvje3l7StPXyHKHt//rN0BKUVEJs1KKU1EV560kAvH+1LwY7nMBedNgrSjKx+KoiiKotQUnXwoiqIoilJTdPKhKIqiKEpNmbE2HzXFZbcxSa6XjsjMJ2ZzMpMQGiGGF68idC/TQUO2MdzDKxGLKqoKHu7dQ1c4tBtyuezykO5g1+Gxe8TslXGuLWO3sbGBWTV5Js+GkAYur89daAtwkbzDvTEttN2J6byFKl6hBNgZCJdSfBQ8kyrYPLjCxMu2RdtjnAguV19xj6Cdc9ffbCnaDZi70hIRjQQyvHzAbDdOiY9Fnpv1ZT0lcoV+99g2wMbX8Xqe224k0OamwrcYx1SKnRZ2Sa+oSiKSNiCYRZpb4DzaJ11tD7BX32XThd86/l04EasO7nqLyXc9Zg8Vahu35ZgsF90KqWrl45577qFzzz2Xmpubqbm5mXp6euinP/1puTybzdK6deuovb2dGhsbac2aNTQwMDDpjVYURVEU5eSlqsnHokWL6M4776Te3l569tln6ZJLLqFrrrmGfvWrXxER0W233UaPPPIIbdmyhbZv306HDh2i6667bkoariiKoijKyUlVsstVV10l9u+44w665557aOfOnbRo0SK699576f7776dLLrmEiIg2b95MZ599Nu3cuZMuuuii6lpmzLRkmw1xItn8Ks366pIkaoFrmbAaucQFPxZca531QFZbvjYZJGU9Pq/XcU+49Oix7JEYfdSr5vlziQbceUVWXbj/KxYti6x/66G95e0hR+bQeow+Cs3m8skILK+OMdklAy6caWOvmXA8JlywdWW1zQQQjdNEf4ZcWU85mLkVs7zK60XLGQHWw5al0Z3V5Wrrul6OyR7YN1ySCbWFHYtlLvkIy5LsntBl1wWXS1zyCI4TfBJJ9k5hWUGMG4no7aqkPLuNvYTvTUlIQpjhOFpybeWhBCAEwOXvY3/7EhiZlN0lfGuFHIzfTIx+GrhcZlnb/OjjTIB18icw+ZLMhA1OS6USPfDAAzQ2NkY9PT3U29tLhUKBVq1aVT5m6dKl1N3dTTt27IisJ5fL0fDwsPhRFEVRFGX2UvXk44UXXqDGxkZKpVJ000030YMPPkgf+MAHqL+/n5LJJLW2torjOzo6qL+/P7K+jRs3UktLS/mnq6ur6ptQFEVRFOXkoerJx1lnnUV79+6lXbt20Re+8AVau3YtvfTSSxNuwIYNG2hoaKj809fXN+G6FEVRFEWZ+VTtaptMJumMM84gIqJly5bRnj176Nvf/jZ96lOfonw+T4ODg2L1Y2BggDo7OyPrS6VSlEqlIstnHKi1zQS7lBNlojYnU+UizPVL0DL9gtUhS2kYvjwcMeqnXFvFjLNF5gYLrrWmADHTub0IZs4tVhYyP2RHErftNmArsnrh+eXt7+9/JrLOlCftCnyHLQEF8p4KHrcHkee5lN4008Sz6AbsOA/tHIR7KdhHiDJwL+W2DGjjwe0xYuDnHjBtG9uC9hGlgIWlD2XnrcwGA+1RMoEN2Y8h1HPE7EHgPH4fWBbAfYiw/NBvPIR7witCmX2OmI2Y206kHUYXaKuRhvGeRvskBn9uWYfrZ8jVl5dh0xw2HyW4D+5qGxj5bPi70WQcuRTQ3ouHDsBjeTgG6CczFX9bpiBM+kQ54SBjQRBQLpejZcuWUSKRoG3btpXL9u3bR/v376eenp4TvYyiKIqiKLOEqlY+NmzYQJdffjl1d3fTyMgI3X///fTUU0/R1q1bqaWlhW688UZav349tbW1UXNzM33xi1+knp6e6j1dFEVRFEWZtVQ1+Th8+DB95jOfobfeeotaWlro3HPPpa1bt9If/MEfEBHRt771LfJ9n9asWUO5XI5Wr15Nd99995Q0fNpwuTS5suhOtzstMhVRXYU7aRWLao7roZThcdmlVcp1po7tZ0DKY8uNWw/2iqLLeBZKzHhLcslaRBIslKLLcCy4xo2oQy4MczWj3ot2EUWuWHhBZNmDB2R0Rp+sOy1KC3mxvB/9nHApnS+fF0C8KUAU0xKLYpovyXp4pFB0dcWonlGgJDJUqrPXrsJl1X2NaPkGyzijXrTknPLl2OfSWQ7clTEy7CkJG9W0MSajls6LW4/CebERWY+Qq2R7GkQ24Oh7CgzKNRjhlWWmhsV3nwkqJUgTXXBE6eUyUABN428NRi1FeGTghOPYOk9mu27krrYuM4IKvwPVnickGvxb44y4yiOcgpTkymorL8BPqtgNuqrJx7333ussT6fTtGnTJtq0aVM11SqKoiiKMofQxHKKoiiKotQUnXwoiqIoilJTTr6stujqypmonoZMNIR6NXYdrmOnwsWqFi7BImS8owzBtnG7i7zUIb0iy9AIorRJ2uHs10l7AD/JXRjRndbaPBgIjexhu9k1CF1rHS51opaQXQk7DstYW1Ne9OsaHE9oZfotho1OC1fE6Hrw7eL1xMGJ0WehqLNGhoWvB/sE7hYai8kMrBzMXMvtKjKl6LJjkB12uFhHUeCxPnNvLQbyHgPhaiufeJG586LNSdy399+UkOH8k8zOIw92HXV+dHh935fPLc1caLsTR0RZZ8zafDSBqy93p8VXmNtu4Bjiz/8YyXa6jkWETUYVnyx+HmZ0dtl55OBbwN1p0dU3E1Roc+UKfe4oOxHXWpEdF+tx/a1xfZenGF35UBRFURSlpujkQ1EURVGUmjJzZRfPG98ddLKkFbxWrc+d6BKb63pT0TdTsCwXcp+NwzDk8kUa3GlZllu/CJFKmQxjkuAi21Bf3r6i60Joka0TZQ+vIJeQjWsJk51rsnI5PWDykeeKvupAZL8lEmOIZ78dF+FCKJe922IpVibvl7s3JgjdUrlbpHwWXCJqgkyaCYiG2sBlF4i+Wu8NlbfbQZI5Em8obx8tNYqywZJ93hg1dbRkJTmMmorRULGcw91dUZJx0ZrIlLfrQUqpj9lxwyOREhH5nu2nJEQmbfKzsH+svN0VHxJlMS/6neZ3i66mOG44x5i0VgC5AqPvxph8gxIoPxZdsgvG3rMfugceiTYa/G+7yU9CeZ6VyW9PomSfzRULV0LFzH24zhWOAd519s0OZa7lVPF3xoO/LQH/FkF/bz34fGQ9+GwqYXgkoFPPquxYXflQFEVRFKWm6ORDURRFUZSaopMPRVEURVFqysy1+ZhsZkP2WWSq7mky7DywDq5too0HwtzRuI0HEZGX4dp2izyPu5ulwWWy3toA+GMZUfbob/dENuXKxTIvkc/vC8MoO0MQR8O1XrSHeezgc+VtDJnO7TxQn0UbkMu6l4+7jdd8+KDsC27XgZo/d2lEd0beVgznHhC62lZG2C3UhgZvjclninYeHB6WPRtIzT+UcTfgGXflsZgttlK4+257bFSU1fvRNh/cHqWV2XS8d2wpcj/lMhOD/TQbw+jazUOhY9bkuAiZ7s7uvHrBeeVtPr6JKrczCGWndXwLs44ytDH6k/d9vLyNtlh3vfZzu+OywUC7Du5eC3Z53C3Wc9nsuUJMQL2h1A7ygmKXP4vj2o1NMrryoSiKoihKTdHJh6IoiqIoNUUnH4qiKIqi1JS5Y/NxsjKT7TpcVNNu0E+9ZLSWbvLWDz+IQ7wMFj/DQ42UhU3H2CEu+4SfvLFT7F/5vp7ytg+h2EUYY7g+xvaIAjVwV9h0ro/HwN5k9cLz5cEsLkKoLSxGwdVd0saFH2tCdjxM24ZnuPWQjR9QMpj+XSr2wq4kJvXyEXYN1PnrWayLEdDLk8yaIQ//Y8VY3JECxDVBW5Ekt6UAKb85bu0u8gZDr3P7CNk2HmejKSZtNxpYe7j9B5EMfV8PcT7SHl7Dbicd9gn436fPjkU7Hm7nERpvzHagGorYqQ5cKe55PJosDlO2PQZjsSUOodg75pW3zfCIKOPh9DEmB7cPCYU3d8XxYe+UccWTr6bO0LmsB/A9ZXE+8BlWagMykXggRLryoSiKoihKjdHJh6IoiqIoNUVll1qBy2az0fV3ooRCDvO+gbKc243vdxhYXjRxHrIdMt6m7fJ2AEvko0YufZuc3TfHcxnmsGVqV+ZahC91oyQzYWDJ2BE1WzwbD2WmgLsIS5dFLqWgW+YnFsow8eK+4LVo86MblzHsmhDCPcber3qKZhBcbdH3lLvepsHVl2erxTIukaQhhDrPKtsKZfy/wQSoDFw+SYXcukH2cXxfhLQCz8aVqRillijQlR7luq0Hesvbo0G0fIBh2vmey7W2EMopHc0fd39U7Me72D362DdcvsC+qEIG4XCp2CEroftsSAI1jrZV+LfGJbNMVFpxoSsfiqIoiqLUFJ18KIqiKIpSU3TyoSiKoihKTZm5Nh/GVG0X4U21+2i1zCS7jlr3jcsVDNxQDYYVFueCSxsPR1yS92Titt5Qxm1m80F10ubDy9qQ7WsWSVfT/zwgXW15+PGQWyqzpUD7CC/GQ8ZL+4DHmAaOujq3nShAKnrhsnocTbZSV99QWm++j5o0dy8EUxxX+vVHD/aKfd5y13loj5MWY0P2TZI11WUfwFPYExFlwAaD24RguPOEFz3GY6yteNy8mL0GOpXHHHYd9Z5tSzjUveybYWPHdL0PqQYcYdIrtesIudY67BVcY68ERj7cVgXL8qwMRztvdcnI6+WYG/SpMfkM/QawCGLpHEqDQ6Lo1UJ7eftHrz8jyvhzu3rRCorCi8MTd/QN/9ahnRi3m0Emy2XW5ebPxw0/znVOuA5FURRFUZQaopMPRVEURVFqysyVXSKYcdJKrXFlLHThypg4FeDyPQeWwdHFLHC6d7KongVwP+PSBowTw6WeOLia1tWVt/2C1A/qPMhkyvSFh/ukJHP1wgvtjmM5lUcVJCLKBLZOdH3kS+QoOwTiWPf/Ebw/nK61OE54v8FzC0VyZHCJCNvtOhb7m0efnWjWTd8hJRRQrgnJV1YiaYCooj5bYo6BzpfgrrYwFFp8e48YRZST8uQS/WWn2+V8fC9wWf4nr+1g14i+/9WLpNszl+9wnArZz5XVFcB3MRPYPh0BV1vXSMma6Gty1+aC4104LSZlFleG7Qf6/lvsb3rXjsUzE1L2mMffd3wveF/hd4FHOHW9wseRVfmzwffEFQm5Uhfa8Dt84usWuvKhKIqiKEpNOaHJx5133kme59Gtt95a/l02m6V169ZRe3s7NTY20po1a2hgYOBE26koiqIoyixhwpOPPXv20D//8z/TueeeK35/22230SOPPEJbtmyh7du306FDh+i666474YYqiqIoijI7mJDNx+joKN1www30ve99j775zW+Wfz80NET33nsv3X///XTJJZcQEdHmzZvp7LPPpp07d9JFF10UVWUIz5gTt++otavrREOo431W6BZZ1fUmy+aD2wBUofsKQJ8O2Q447sPkratcfEi6SRabbLZaDK/u8Tqxfqb7euCG+4n3f0TsP/zb/1PezhnptsddSDGEOO8rdJkVeioIv3F2HroecosA1GRRrxdZbqH/Xbo3HzfVZOvkrqCFUJbRIPJYbuOBoD4twn87hqLv+B8LnwWSFrYc0G72PBrgnU2xe6oHOxbupngF2lwwTR5dVL0kG6eY+Vl6CMt6nd8hef881H3JdZrDrgFtPB7r2yP2D5e4zQdkHA75yFdGlrnTBmAb0sRC31/RJd9nv0Ha8fAz+0qybTuPLilvpyCc/lmpt8rbd78p3XBb2HM8xa8TZdw19RNd0kWXP//HwLW24HimTpdZh42H27U2OgSA/H3lf2cmtPKxbt06uvLKK2nVqlXi9729vVQoFMTvly5dSt3d3bRjxw6shoiIcrkcDQ8Pix9FURRFUWYvVa98PPDAA/Tcc8/Rnj17QmX9/f2UTCaptbVV/L6jo4P6+/vHrW/jxo309a9/vdpmKIqiKIpyklLV5KOvr49uueUWeuKJJygNmUEnyoYNG2j9+vXl/eHhYerq6pqUumvCZEg7IUnEsSBVsZRTY9daxLEkj8v3oWyOXAZJyiXrx9/cXd6+sqdD1tNgj/Vc/RS6HotMihJEOiV2r1rcU97+yRvS1bbIlrB/eECu9CWYRJBxLPWXYBk8YMuYudCSZrSLLsKXbV1L/YiIxlrFWK/G1dYli/BnFWo3a0817oWinfCeuKKhThZyGRzlC1aEcix3A4d2Y9ZTHqn2J+ydwetjhFM+hl3PBTMs8z5GOfJIIDWhoywbMpdLiGQ24lIo4270OJKyi2z36T5zQwX54ooPfjyyzgLU827WSiYvjCwUZZmS/U5gJNzFicHydpPvlvk4oec/QUISMINLxfieut6FqHf6eO86pyrZpbe3lw4fPkwXXHABxeNxisfjtH37dvrOd75D8XicOjo6KJ/P0+DgoDhvYGCAOjs7x60zlUpRc3Oz+FEURVEUZfZS1crHpZdeSi+88IL43ec+9zlaunQp3X777dTV1UWJRIK2bdtGa9asISKiffv20f79+6mnp2e8KhVFURRFmWNUNfloamqic845R/yuoaGB2tvby7+/8cYbaf369dTW1kbNzc30xS9+kXp6eqrydFEURVEUZfYy6eHVv/Wtb5Hv+7RmzRrK5XK0evVquvvuu6uvaAJZbcetY6rPm4pw7y57DZd7ay3sPCZ6De6im5UusgZdi9m+GcuIosczVlv9wf/ZIso++emb7A70k8h4W4Dr8eeNNh9gL8BdHK/oulCUPdpntfW0J+vhobKHgmOijNtyoM7K1WPUSFOOoYD1lJh9yMMHpA0Ab1soW6kDrvvnIK0tvyfMwBqHTMWiH1HX5/1fhRsfx+V6mAjZg8hjR9guZkvlJglj8B3wfXsihnDnurgrZHyloa+JxnFRZuO/CNfnYyOk8/O+oeg4/BgWPsdSBGCdR0ry2DET/WcH7Tw4hcCOUx/GSdbYsmZPfl84+O6ZIrjasu9bK2Q4HspYm4+BhDQPGM7bshi07dX4WHn7soaXRRl3w0V7lMMle16RpO0bwt+pY0a2m2fixnevSNHfHpf9RtQ4qcaO44QnH0899ZTYT6fTtGnTJtq0adOJVq0oiqIoyixEc7soiqIoilJTTo6stlPt/ob1T5aUMtUySK0juFaBKaJLGdsPLSdHL+/e++JjYn/zoHUbOz0uo3iK4Ii4euvqKxG1FdoGMozIIwt1cnkB3WKF62coc60FAlXKKJNwT0MBd4uUuBLX4pNpYK6B6CLc6FmZCyMg5pg7LS7DxxwtQBmAR8DEa3BXUDxPtkXuuyKXYqRYeZ4s43dRTfRNXk/Bk23hz98V7RbLuOtrCmQ97jL53rH2Gm+VpAyRZM/K+d+nQ9bDqLU8Oy1GLX07kJlkuSssyie8zCXBlOAazX62vN0Wk66uKRZhFiVALybrMeybjbJmsWiPHc5LF3yeYbo/1yLKhorMRTchvT674kdZ26QkVM/aihmOR4Os2I87juVjqhoJrlK4rFiYKldbRVEURVGUE0UnH4qiKIqi1BSdfCiKoiiKUlNODpsPzlTYOUyFu+xkgXYj/iTNF6sJP17peeK4aJdJUwD3NrCr+PGvnixvPzy2QJT1562LW19RaqseCzEddt91ZOPlfYyZREOaMCuHei473WalfBxCWoumQNjmBLMlCNkAsEtgltEC05nRDTQZckW0mjBq6QXPPo80Pno/2m0xYGOhHnRmfh+Y6RJtLvi56LJbcNhn1HvRdiVZdn10py047G/QUoTr/mhXkmfPsYBZP1lNMbh/fmTWRNuDhIl23w0dyZ5NBsYGt6tpcoWeh3bztuEz5NcYCqQ9xGBJ2nwkmQ1MmqR9Rp6HSYc+9R32BPUsy2yLL8cFt4+5crGMN+U3R4+heTF5H/x1H8tJ11dug/JGrE2UtSatLccO7wxR9mpihG0fFWVLkzYf2v9KSGuw8Dtl31McQ9wGJANuuMdLyxBF1HnV1KcrH4qiKIqi1BSdfCiKoiiKUlNOPtllsqh1ZNLpzjJbDVxaQYnCJck44JFBTQYKY3Lp8zcFu4TYD9LKWNEuhR4tNcpr5OxyrqmDoT1Ruc7hahvK3MuuwSUYIiJTsMud6BbJM9emYRmcu+hmUSJgS92hZX+QdgLDl8xlPSPsMxAQyB7MhVIKK9K9NhPI5XMul+SP0/cFthSMYgJKBpwx1tYGkMvyFboQHg2iXWuJ3P+dxYRcJsu4zDUYyLtKO85LsbIkvGs8imrsOK8hl+hKBiUhWzhWhWskl2tGAtlTg0xqGQPZJQP7gS/HCmc4iM6W3urbD0cDyIFtvh0LLb78LlzWvby87dXJtoRg7/S1Z/6eKEreb6+RzYHMWLDvUCmQDydXH/1n9p2cbeu7qQZRxt2QT4+/JcrQ1dr1LvI4yYEji/JEJZiJoisfiqIoiqLUFJ18KIqiKIpSU2ac7GL+Z1moGOT5L8ffPhFqLrtM1vVcieWquEal/ThBmcXA0h93vgjA4toLIHofy+Z17JiUAfKj9txMQi5nF1kkxwASRpmSbY9XwrZFjDWi8DNlS+gG7sOYAtsuRZYNj8g6eTTUIlixj7H9Y8Ylu7hxyS4J9nAMeMnE2aEu2SWBHjRVyC58AR9lF7xn0TZ2jcAhu6B8wRmFxxsDLw5+Jt4F78eso51F6NMCk1aK6EHDqpks2QW9dLgnVL6K15vfxWiAbQvYtnyKmQA9uErjbo93LCfhszJf9ulI3O4Px2VZkb17noEIp/jNZH2O73cpw74vOXi/fVtPKZDnFY09rwBlHvM0yxXkW8z7YqQI3wwYU/xdPBZEv8P4ffHYeZMhu4z8zwtlKvj74plKjqohBw4coK6uruluhqIoiqIoE6Cvr48WLVrkPGbGTT6CIKBDhw6RMYa6u7upr6+Pmpubj3/iHGJ4eJi6urq0b8ZB+yYa7ZtotG/GR/slGu2bMMYYGhkZoQULFpB/nJhUM0528X2fFi1aRMPDw0RE1NzcrA82Au2baLRvotG+iUb7Zny0X6LRvpG0tLQc/yBSg1NFURRFUWqMTj4URVEURakpM3bykUql6G//9m8plTpOUJg5iPZNNNo30WjfRKN9Mz7aL9Fo35wYM87gVFEURVGU2c2MXflQFEVRFGV2opMPRVEURVFqik4+FEVRFEWpKTr5UBRFURSlpszYycemTZto8eLFlE6naeXKlbR79+7pblJN2bhxI1144YXU1NRE8+fPp2uvvZb27dsnjslms7Ru3Tpqb2+nxsZGWrNmDQ0MDExTi6ePO++8kzzPo1tvvbX8u7ncNwcPHqQ/+ZM/ofb2dqqrq6MPfehD9Oyzz5bLjTH0ta99jU477TSqq6ujVatW0auvvjqNLa4NpVKJvvrVr9KSJUuorq6O3v/+99M3vvENkYdirvTN008/TVdddRUtWLCAPM+jhx56SJRX0g9Hjx6lG264gZqbm6m1tZVuvPFGGh0dreFdTA2uvikUCnT77bfThz70IWpoaKAFCxbQZz7zGTp06JCoY7b2zaRiZiAPPPCASSaT5l//9V/Nr371K/Pnf/7nprW11QwMDEx302rG6tWrzebNm82LL75o9u7da6644grT3d1tRkdHy8fcdNNNpqury2zbts08++yz5qKLLjIf+chHprHVtWf37t1m8eLF5txzzzW33HJL+fdztW+OHj1qTj/9dPPZz37W7Nq1y7z22mtm69at5je/+U35mDvvvNO0tLSYhx56yPziF78wV199tVmyZIk5duzYNLZ86rnjjjtMe3u7efTRR83rr79utmzZYhobG823v/3t8jFzpW8ee+wx85WvfMX86Ec/MkRkHnzwQVFeST9cdtll5sMf/rDZuXOn+fnPf27OOOMMc/3119f4TiYfV98MDg6aVatWmR/84AfmlVdeMTt27DArVqwwy5YtE3XM1r6ZTGbk5GPFihVm3bp15f1SqWQWLFhgNm7cOI2tml4OHz5siMhs377dGPPeS5BIJMyWLVvKx7z88suGiMyOHTumq5k1ZWRkxJx55pnmiSeeML/3e79XnnzM5b65/fbbzUc/+tHI8iAITGdnp/mHf/iH8u8GBwdNKpUy//Ef/1GLJk4bV155pfmzP/sz8bvrrrvO3HDDDcaYuds3+Ae2kn546aWXDBGZPXv2lI/56U9/ajzPMwcPHqxZ26ea8SZmyO7duw0RmTfffNMYM3f65kSZcbJLPp+n3t5eWrVqVfl3vu/TqlWraMeOHdPYsullaGiIiIja2tqIiKi3t5cKhYLop6VLl1J3d/ec6ad169bRlVdeKfqAaG73zcMPP0zLly+nP/qjP6L58+fT+eefT9/73vfK5a+//jr19/eLvmlpaaGVK1fO+r75yEc+Qtu2baNf//rXRET0i1/8gp555hm6/PLLiWhu9w2nkn7YsWMHtba20vLly8vHrFq1inzfp127dtW8zdPJ0NAQeZ5Hra2tRKR9UykzLrHcO++8Q6VSiTo6OsTvOzo66JVXXpmmVk0vQRDQrbfeShdffDGdc845RETU399PyWSyPOB/R0dHB/X3909DK2vLAw88QM899xzt2bMnVDaX++a1116je+65h9avX09//dd/TXv27KG//Mu/pGQySWvXri3f/3jv12zvmy9/+cs0PDxMS5cupVgsRqVSie644w664YYbiIjmdN9wKumH/v5+mj9/viiPx+PU1tY2p/oqm83S7bffTtdff305uZz2TWXMuMmHEmbdunX04osv0jPPPDPdTZkR9PX10S233EJPPPEEpdPp6W7OjCIIAlq+fDn93d/9HRERnX/++fTiiy/Sd7/7XVq7du00t256+eEPf0jf//736f7776cPfvCDtHfvXrr11ltpwYIFc75vlOopFAr0x3/8x2SMoXvuuWe6m3PSMeNkl1NPPZVisVjIM2FgYIA6OzunqVXTx80330yPPvoo/exnP6NFixaVf9/Z2Un5fJ4GBwfF8XOhn3p7e+nw4cN0wQUXUDwep3g8Ttu3b6fvfOc7FI/HqaOjY872zWmnnUYf+MAHxO/OPvts2r9/PxFR+f7n4vv1V3/1V/TlL3+ZPv3pT9OHPvQh+tM//VO67bbbaOPGjUQ0t/uGU0k/dHZ20uHDh0V5sViko0ePzom++t3E480336QnnniivOpBpH1TKTNu8pFMJmnZsmW0bdu28u+CIKBt27ZRT0/PNLasthhj6Oabb6YHH3yQnnzySVqyZIkoX7ZsGSUSCdFP+/bto/3798/6frr00kvphRdeoL1795Z/li9fTjfccEN5e672zcUXXxxyyf71r39Np59+OhERLVmyhDo7O0XfDA8P065du2Z932QyGfJ9+cmLxWIUBAERze2+4VTSDz09PTQ4OEi9vb3lY5588kkKgoBWrlxZ8zbXkt9NPF599VX6r//6L2pvbxflc7lvqmK6LV7H44EHHjCpVMrcd9995qWXXjKf//znTWtrq+nv75/uptWML3zhC6alpcU89dRT5q233ir/ZDKZ8jE33XST6e7uNk8++aR59tlnTU9Pj+np6ZnGVk8f3NvFmLnbN7t37zbxeNzccccd5tVXXzXf//73TX19vfn3f//38jF33nmnaW1tNT/+8Y/NL3/5S3PNNdfMSndSZO3atWbhwoVlV9sf/ehH5tRTTzVf+tKXysfMlb4ZGRkxzz//vHn++ecNEZl//Md/NM8//3zZY6OSfrjsssvM+eefb3bt2mWeeeYZc+aZZ84Kd1JX3+TzeXP11VebRYsWmb1794pvcy6XK9cxW/tmMpmRkw9jjPmnf/on093dbZLJpFmxYoXZuXPndDepphDRuD+bN28uH3Ps2DHzF3/xF+aUU04x9fX15pOf/KR56623pq/R0whOPuZy3zzyyCPmnHPOMalUyixdutT8y7/8iygPgsB89atfNR0dHSaVSplLL73U7Nu3b5paWzuGh4fNLbfcYrq7u006nTbve9/7zFe+8hXxR2Ou9M3Pfvazcb8va9euNcZU1g9Hjhwx119/vWlsbDTNzc3mc5/7nBkZGZmGu5lcXH3z+uuvR36bf/azn5XrmK19M5l4xrDwfoqiKIqiKFPMjLP5UBRFURRldqOTD0VRFEVRaopOPhRFURRFqSk6+VAURVEUpabo5ENRFEVRlJqikw9FURRFUWqKTj4URVEURakpOvlQFEVRFKWm6ORDURRFUZSaopMPRVEURVFqik4+FEVRFEWpKTr5UBRFURSlpvxfIjhXX9FTNvYAAAAASUVORK5CYII=",
             "text/plain": [
               "
"
             ]
@@ -736,7 +651,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 20,
+      "execution_count": 22,
       "id": "d7ec0833-d54b-4073-84cf-92d011c60ec1",
       "metadata": {
         "colab": {
@@ -751,10 +666,10 @@
             "text/plain": [
               ""
+              "       39, 14, 15, 23])>"
             ]
           },
-          "execution_count": 20,
+          "execution_count": 22,
           "metadata": {},
           "output_type": "execute_result"
         }
@@ -765,7 +680,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 21,
+      "execution_count": 23,
       "id": "fe1ad370-b287-4b46-85a2-7c45b0bd9b10",
       "metadata": {
         "colab": {
@@ -781,7 +696,7 @@
               ""
             ]
           },
-          "execution_count": 21,
+          "execution_count": 23,
           "metadata": {},
           "output_type": "execute_result"
         }
@@ -792,7 +707,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 22,
+      "execution_count": 24,
       "id": "6871031a-b0ba-4c76-a852-f6329b0f2606",
       "metadata": {
         "id": "6871031a-b0ba-4c76-a852-f6329b0f2606",
@@ -818,7 +733,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 23,
+      "execution_count": 25,
       "id": "7686355d-45aa-4c85-ad9c-053e6a9b4d81",
       "metadata": {
         "id": "7686355d-45aa-4c85-ad9c-053e6a9b4d81",
@@ -831,13 +746,28 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 24,
+      "execution_count": 26,
       "id": "f066fea2-91b1-42ed-a67d-00566a1a53ff",
       "metadata": {
         "id": "f066fea2-91b1-42ed-a67d-00566a1a53ff",
         "tags": []
       },
-      "outputs": [],
+      "outputs": [
+        {
+          "ename": "InvalidArgumentError",
+          "evalue": "Expected 'tf.Tensor(False, shape=(), dtype=bool)' to be true. Summarized data: b'No files matched pattern: .\\\\data\\\\s1\\\\*.mpg'",
+          "output_type": "error",
+          "traceback": [
+            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+            "\u001b[0;31mInvalidArgumentError\u001b[0m                      Traceback (most recent call last)",
+            "Cell \u001b[0;32mIn[26], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[43mtf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdata\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mDataset\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlist_files\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m.\u001b[39;49m\u001b[38;5;130;43;01m\\\\\u001b[39;49;00m\u001b[38;5;124;43mdata\u001b[39;49m\u001b[38;5;130;43;01m\\\\\u001b[39;49;00m\u001b[38;5;124;43ms1\u001b[39;49m\u001b[38;5;130;43;01m\\\\\u001b[39;49;00m\u001b[38;5;124;43m*.mpg\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m      2\u001b[0m data \u001b[38;5;241m=\u001b[39m data\u001b[38;5;241m.\u001b[39mshuffle(\u001b[38;5;241m500\u001b[39m, reshuffle_each_iteration\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[1;32m      3\u001b[0m data \u001b[38;5;241m=\u001b[39m data\u001b[38;5;241m.\u001b[39mmap(mappable_function)\n",
+            "File \u001b[0;32m~/NSCC/CodeSpace/LipNet/.venv/lib/python3.12/site-packages/tensorflow/python/data/ops/dataset_ops.py:1320\u001b[0m, in \u001b[0;36mDatasetV2.list_files\u001b[0;34m(file_pattern, shuffle, seed, name)\u001b[0m\n\u001b[1;32m   1313\u001b[0m condition \u001b[38;5;241m=\u001b[39m math_ops\u001b[38;5;241m.\u001b[39mgreater(array_ops\u001b[38;5;241m.\u001b[39mshape(matching_files)[\u001b[38;5;241m0\u001b[39m], \u001b[38;5;241m0\u001b[39m,\n\u001b[1;32m   1314\u001b[0m                              name\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmatch_not_empty\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m   1316\u001b[0m message \u001b[38;5;241m=\u001b[39m math_ops\u001b[38;5;241m.\u001b[39madd(\n\u001b[1;32m   1317\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNo files matched pattern: \u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m   1318\u001b[0m     string_ops\u001b[38;5;241m.\u001b[39mreduce_join(file_pattern, separator\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m, \u001b[39m\u001b[38;5;124m\"\u001b[39m), name\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmessage\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m-> 1320\u001b[0m assert_not_empty \u001b[38;5;241m=\u001b[39m \u001b[43mcontrol_flow_assert\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mAssert\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1321\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcondition\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m[\u001b[49m\u001b[43mmessage\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msummarize\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mname\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43massert_not_empty\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1322\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m ops\u001b[38;5;241m.\u001b[39mcontrol_dependencies([assert_not_empty]):\n\u001b[1;32m   1323\u001b[0m   matching_files \u001b[38;5;241m=\u001b[39m array_ops\u001b[38;5;241m.\u001b[39midentity(matching_files)\n",
+            "File \u001b[0;32m~/NSCC/CodeSpace/LipNet/.venv/lib/python3.12/site-packages/tensorflow/python/util/traceback_utils.py:153\u001b[0m, in \u001b[0;36mfilter_traceback..error_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    151\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m    152\u001b[0m   filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n\u001b[0;32m--> 153\u001b[0m   \u001b[38;5;28;01mraise\u001b[39;00m e\u001b[38;5;241m.\u001b[39mwith_traceback(filtered_tb) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m    154\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m    155\u001b[0m   \u001b[38;5;28;01mdel\u001b[39;00m filtered_tb\n",
+            "File \u001b[0;32m~/NSCC/CodeSpace/LipNet/.venv/lib/python3.12/site-packages/tensorflow/python/ops/control_flow_assert.py:102\u001b[0m, in \u001b[0;36mAssert\u001b[0;34m(condition, data, summarize, name)\u001b[0m\n\u001b[1;32m    100\u001b[0m     xs \u001b[38;5;241m=\u001b[39m ops\u001b[38;5;241m.\u001b[39mconvert_n_to_tensor(data)\n\u001b[1;32m    101\u001b[0m     data_str \u001b[38;5;241m=\u001b[39m [_summarize_eager(x, summarize) \u001b[38;5;28;01mfor\u001b[39;00m x \u001b[38;5;129;01min\u001b[39;00m xs]\n\u001b[0;32m--> 102\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m errors\u001b[38;5;241m.\u001b[39mInvalidArgumentError(\n\u001b[1;32m    103\u001b[0m         node_def\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m    104\u001b[0m         op\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m    105\u001b[0m         message\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mExpected \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m to be true. Summarized data: \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m%\u001b[39m\n\u001b[1;32m    106\u001b[0m         (condition, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mjoin(data_str)))\n\u001b[1;32m    107\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m\n\u001b[1;32m    109\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m ops\u001b[38;5;241m.\u001b[39mname_scope(name, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAssert\u001b[39m\u001b[38;5;124m\"\u001b[39m, [condition, data]) \u001b[38;5;28;01mas\u001b[39;00m name:\n",
+            "\u001b[0;31mInvalidArgumentError\u001b[0m: Expected 'tf.Tensor(False, shape=(), dtype=bool)' to be true. Summarized data: b'No files matched pattern: .\\\\data\\\\s1\\\\*.mpg'"
+          ]
+        }
+      ],
       "source": [
         "data = tf.data.Dataset.list_files('.\\\\data\\\\s1\\\\*.mpg')\n",
         "data = data.shuffle(500, reshuffle_each_iteration=False)\n",
@@ -851,7 +781,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 25,
+      "execution_count": null,
       "id": "4de3b276",
       "metadata": {},
       "outputs": [
@@ -872,7 +802,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 26,
+      "execution_count": null,
       "id": "5281bde8-fdc8-4da1-bd55-5a7929a9e80c",
       "metadata": {
         "id": "5281bde8-fdc8-4da1-bd55-5a7929a9e80c"
@@ -884,7 +814,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 27,
+      "execution_count": null,
       "id": "cbebe683-6afd-47fd-bba4-c83b4b13bb32",
       "metadata": {
         "colab": {
@@ -911,7 +841,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 28,
+      "execution_count": null,
       "id": "5cf2d676-93a9-434c-b3c7-bdcc2577b2e7",
       "metadata": {
         "id": "5cf2d676-93a9-434c-b3c7-bdcc2577b2e7",
@@ -924,7 +854,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 29,
+      "execution_count": null,
       "id": "efa6cd46-7079-46c0-b45b-832f339f6cb0",
       "metadata": {
         "colab": {
@@ -1569,7 +1499,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 30,
+      "execution_count": null,
       "id": "acf5eb4f-a0da-4a9a-bf24-af13e9cc2fbe",
       "metadata": {
         "colab": {
@@ -1668,7 +1598,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 31,
+      "execution_count": null,
       "id": "c33a87a2-d5e0-4ec9-b174-73ebf41bf03a",
       "metadata": {
         "colab": {
@@ -1708,7 +1638,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 32,
+      "execution_count": null,
       "id": "84593332-133c-4205-b7a6-8e235d5e2b3b",
       "metadata": {
         "colab": {
@@ -1747,7 +1677,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 33,
+      "execution_count": null,
       "id": "d8e9a497-191b-4842-afbd-26f5e13c43ba",
       "metadata": {
         "id": "d8e9a497-191b-4842-afbd-26f5e13c43ba",
@@ -1771,7 +1701,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 34,
+      "execution_count": null,
       "id": "3f753ed2-70b9-4236-8c1c-08ca065dc8bf",
       "metadata": {
         "colab": {
@@ -1798,7 +1728,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 35,
+      "execution_count": null,
       "id": "f9171056-a352-491a-9ed9-92b28ced268e",
       "metadata": {
         "id": "f9171056-a352-491a-9ed9-92b28ced268e",
@@ -1850,7 +1780,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 36,
+      "execution_count": null,
       "id": "ab905c18",
       "metadata": {},
       "outputs": [
@@ -2008,7 +1938,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 37,
+      "execution_count": null,
       "id": "fae71bed",
       "metadata": {},
       "outputs": [
@@ -2029,7 +1959,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 38,
+      "execution_count": null,
       "id": "7a5815ab",
       "metadata": {},
       "outputs": [
@@ -2047,7 +1977,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 39,
+      "execution_count": null,
       "id": "65eccd93",
       "metadata": {},
       "outputs": [
@@ -2068,7 +1998,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 40,
+      "execution_count": null,
       "id": "6ed47531-8317-4255-9a12-b757642258e6",
       "metadata": {
         "colab": {
@@ -2096,7 +2026,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 41,
+      "execution_count": null,
       "id": "7c37b9b9-5298-4038-9c33-5031d1b457f0",
       "metadata": {
         "colab": {
@@ -2124,7 +2054,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 42,
+      "execution_count": null,
       "id": "98b316a4-5322-4782-8e36-4b3c1a696d85",
       "metadata": {
         "colab": {
@@ -2163,7 +2093,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 43,
+      "execution_count": null,
       "id": "ab015fd0-7fb4-4d5d-9fa2-30a05dbd515a",
       "metadata": {
         "id": "ab015fd0-7fb4-4d5d-9fa2-30a05dbd515a",
@@ -2180,7 +2110,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 44,
+      "execution_count": null,
       "id": "c564d5c9-db54-4e88-b311-9aeab7fb3e69",
       "metadata": {
         "id": "c564d5c9-db54-4e88-b311-9aeab7fb3e69",
@@ -2202,7 +2132,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 45,
+      "execution_count": null,
       "id": "a26dc3fc-a19c-4378-bd8c-e2b597a1d15c",
       "metadata": {
         "id": "a26dc3fc-a19c-4378-bd8c-e2b597a1d15c",
@@ -2226,7 +2156,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 46,
+      "execution_count": null,
       "id": "04be90d8-2482-46f9-b513-d5f4f8001c7e",
       "metadata": {
         "id": "04be90d8-2482-46f9-b513-d5f4f8001c7e",
@@ -2239,7 +2169,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 47,
+      "execution_count": null,
       "id": "eab49367-3f1e-4464-ae76-dbd07549d97e",
       "metadata": {
         "id": "eab49367-3f1e-4464-ae76-dbd07549d97e",
@@ -2260,7 +2190,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 48,
+      "execution_count": null,
       "id": "e085a632-d464-46ef-8777-959cad4adb2c",
       "metadata": {
         "id": "e085a632-d464-46ef-8777-959cad4adb2c",
@@ -2273,7 +2203,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 49,
+      "execution_count": null,
       "id": "48eca991-90ab-4592-8a79-b50e9ca015b6",
       "metadata": {
         "id": "48eca991-90ab-4592-8a79-b50e9ca015b6",
@@ -2286,7 +2216,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 50,
+      "execution_count": null,
       "id": "8ffba483-aa61-4bbe-a15f-a73e1ddf097c",
       "metadata": {
         "colab": {
@@ -2353,7 +2283,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 51,
+      "execution_count": null,
       "id": "247f664d-3c87-4e96-946e-930dad0e1c2c",
       "metadata": {
         "id": "247f664d-3c87-4e96-946e-930dad0e1c2c",

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zLl%N;A(3Px+`f+z2CWi;`f|ICbDR`seYa;+r_hz|mw;x2CLFKotB~eKG3|M^TC2LY
ze&h(R@gOCs;HfseZ5Ff161P{jlsQC}K!a}Ko
zc9nhwEAa3dCjT<&F^JO@0e{C@F4uP5-6_5862?%0iY?vKQyMq13D)73W2uCTdMovH
z+{Fni@w|}TsV3_69sX?{5hE3*fd{Y7bi`PD#`hy*M$A?TwiGmVn>-QU&@`Cjek!bl
z(W}48k@WI`*V0#QqqX8zTE*_U)yKU(z62WSyY12fINmkeUBI~Y>FN5njz26ft?eis
zxAx=(ZWof_?d6ylJhlLjGfyyiB4i@Y(G%A*31KFwB7NIe!Mjt%cMdY{HW^_*!4iql
zWB){Z8%q~oefeUF4?za#;%k=$(pZmuck~|jzu0kwb5Q#3$jgp{L5wP)q#2>kJS8Se
z3WHoijZAlmS{pwQFx&3%IXyHR1gf|t%oxiWzS&%qZe4R`qByV|PyZ6j`O>9ZMLwn$
zcvw^JYE-=m#{wR^&@397ymAN`VBxs+3AHQT+yKwWAp;?~u+0d|lUQU+VHE&W{gtDJHp{Z#n$K2oE;p1;Caq{M$Za0k0%p5mjv~&{BSr`(tJ#14-5zi8?Q&$Jg)Sb37BFE&dMT%j$WM9~F_+fyH$IbamRDS|-
zfp9#%AJAdla)>il-Z3`7Gb#n}nZx^SUKnM+zgBnHDbKc9D7&p<+r0Ns-vf%tt`rzW
z291^M-85|UjmT`uKAj%>m(d_m`F{mC0>}L*#+iIb)F_gaPU|$5MigO>!cK+hreY{2mhl`q{@U3C^+|7Fk7F&%|40c__%
zA0ul}*E$R*jiYP<9g+dFk#MLbk@F;^6VScJkj_9WqU;($+%|JQ=N5PGf
ztGY}A)k(~h@5CXsll1PCY
zKJqC>HVF6(K#o8N3%~*)b_9lC
z25Z0`8T1FCVL(VC39SK0DY7j@eK=V76tS673y3}gXoY$O&LNPApnVkiX^!V(fP;qt
z?pRYzH5L- Sequential:
@@ -24,29 +24,29 @@ def load_model() -> Sequential:
 
     model.add(TimeDistributed(Flatten()))
 
-    model.add(Bidirectional(LSTM(128, kernel_initializer='Orthogonal', return_sequences=True)))
-    # model.add(Bidirectional(LSTM(128, kernel_initializer=Orthogonal(), return_sequences=True)))
+    # model.add(Bidirectional(LSTM(128, kernel_initializer='Orthogonal', return_sequences=True)))
+    model.add(Bidirectional(LSTM(128, kernel_initializer=Orthogonal(), return_sequences=True)))
     model.add(Dropout(.5))
 
-    model.add(Bidirectional(LSTM(128, kernel_initializer='Orthogonal', return_sequences=True)))
-    # model.add(Bidirectional(LSTM(128, kernel_initializer=Orthogonal(), return_sequences=True)))
+    # model.add(Bidirectional(LSTM(128, kernel_initializer='Orthogonal', return_sequences=True)))
+    model.add(Bidirectional(LSTM(128, kernel_initializer=Orthogonal(), return_sequences=True)))
     model.add(Dropout(.5))
 
     model.add(Dense(41, kernel_initializer='he_normal', activation='softmax'))
 
-    model.load_weights(os.path.join('..','models','checkpoint'))
+    # model.load_weights(os.path.join('..','models','checkpoint'))
 
-    # # Ensure the checkpoint directory is correctly set relative to the script's execution directory
-    # checkpoint_dir = os.path.join('..', 'models')
+    # Ensure the checkpoint directory is correctly set relative to the script's execution directory
+    checkpoint_dir = os.path.join('..', 'models')
 
-    # # Create a checkpoint instance that points to the folder where the checkpoints are saved
-    # checkpoint = tf.train.Checkpoint(model=model)
+    # Create a checkpoint instance that points to the folder where the checkpoints are saved
+    checkpoint = tf.train.Checkpoint(model=model)
 
-    # # Restore the latest checkpoint
-    # latest = tf.train.latest_checkpoint(checkpoint_dir)
-    # if latest:
-    #     checkpoint.restore(latest)
-    # else:
-    #     raise FileNotFoundError(f"No checkpoint found in {checkpoint_dir}")
+    # Restore the latest checkpoint
+    latest = tf.train.latest_checkpoint(checkpoint_dir)
+    if latest:
+        checkpoint.restore(latest)
+    else:
+        raise FileNotFoundError(f"No checkpoint found in {checkpoint_dir}")
 
     return model
\ No newline at end of file
diff --git a/app/streamlitapp.py b/app/streamlitapp.py
index 811cb4e7..f07b5ee7 100644
--- a/app/streamlitapp.py
+++ b/app/streamlitapp.py
@@ -2,6 +2,7 @@
 import streamlit as st
 import os
 import imageio
+import keras
 
 import tensorflow as tf
 from utils import load_data, num_to_char
@@ -11,10 +12,10 @@
 st.set_page_config(layout='wide')
 
 # Setup the sidebar
-with st.sidebar:
-    st.image('https://www.onepointltd.com/wp-content/uploads/2020/03/inno2.png')
-    st.title('LipBuddy')
-    st.info('This application is originally developed from the LipNet deep learning model.')
+# with st.sidebar:
+#     st.image('https://www.onepointltd.com/wp-content/uploads/2020/03/inno2.png')
+#     st.title('LipBuddy')
+#     st.info('This application is originally developed from the LipNet deep learning model.')
 
 st.title('LipNet Full Stack App')
 # Generating a list of options or videos

From b2b9df5313a20a8a3013470e3ab8384a740ba8b6 Mon Sep 17 00:00:00 2001
From: "haidun.li" 
Date: Mon, 15 Apr 2024 00:28:21 -0300
Subject: [PATCH 07/17] Add new video loading functionality and update
 alignment loading in utils.py

---
 LipNet.ipynb | 1224 +++++++++++++++++++++++++-------------------------
 app/utils.py |   26 +-
 2 files changed, 626 insertions(+), 624 deletions(-)

diff --git a/LipNet.ipynb b/LipNet.ipynb
index bab88dcf..cedd532f 100644
--- a/LipNet.ipynb
+++ b/LipNet.ipynb
@@ -12,7 +12,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 158,
    "id": "ddfbccbe-41ae-4c23-98b1-a13868e2b499",
    "metadata": {
     "scrolled": true,
@@ -199,7 +199,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 159,
    "id": "02f907ea-f669-46c7-adcf-7f257e663448",
    "metadata": {
     "tags": []
@@ -273,7 +273,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 160,
    "id": "b24af50c-20b8-409d-ad78-30a933fdd669",
    "metadata": {
     "tags": []
@@ -291,7 +291,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 161,
    "id": "1e3db0b0-e559-4ad6-91fd-e7414b7d75e6",
    "metadata": {},
    "outputs": [
@@ -301,7 +301,7 @@
        "[]"
       ]
      },
-     "execution_count": 57,
+     "execution_count": 161,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -312,7 +312,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": 162,
    "id": "378d045a-3003-4f93-b7d2-a25a97774a68",
    "metadata": {
     "tags": []
@@ -338,7 +338,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": 163,
    "id": "8fb99c90-e05a-437f-839d-6e772f8c1dd5",
    "metadata": {
     "tags": []
@@ -350,7 +350,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": 164,
    "id": "c019e4c6-2af3-4160-99ea-5c8cb009f1a7",
    "metadata": {
     "tags": []
@@ -365,7 +365,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 165,
    "id": "8548cc59-6dfc-4acc-abc3-3e65212db02e",
    "metadata": {
     "tags": []
@@ -389,7 +389,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": 166,
    "id": "ec735e0b-ec98-4eb0-8f49-c35527d6670a",
    "metadata": {
     "tags": []
@@ -401,7 +401,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": 167,
    "id": "be04e972-d7a5-4a72-82d8-a6bdde1f3ce6",
    "metadata": {
     "tags": []
@@ -429,7 +429,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
+   "execution_count": 168,
    "id": "559f7420-6802-45fa-9ca0-b1ff209b461c",
    "metadata": {
     "tags": []
@@ -480,7 +480,7 @@
        " ' ']"
       ]
      },
-     "execution_count": 64,
+     "execution_count": 168,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -491,7 +491,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
+   "execution_count": 169,
    "id": "797ff78b-b48f-4e14-bb62-8cd0ebf9501a",
    "metadata": {
     "tags": []
@@ -503,7 +503,7 @@
        ""
       ]
      },
-     "execution_count": 65,
+     "execution_count": 169,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -514,7 +514,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 66,
+   "execution_count": 170,
    "id": "8cd7f4f4-ae77-4509-a4f4-c723787ebad1",
    "metadata": {},
    "outputs": [
@@ -524,7 +524,7 @@
        ""
       ]
      },
-     "execution_count": 66,
+     "execution_count": 170,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -535,7 +535,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
+   "execution_count": 171,
    "id": "9491bab5-6a3c-4f79-879a-8f9fbe73ae2e",
    "metadata": {
     "tags": []
@@ -555,7 +555,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
+   "execution_count": 172,
    "id": "dd01ca9f-77fb-4643-a2aa-47dd82c5d66b",
    "metadata": {
     "tags": []
@@ -577,7 +577,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
+   "execution_count": 173,
    "id": "8cb7cc58-31ae-4904-a805-1177a82717d2",
    "metadata": {
     "tags": []
@@ -589,7 +589,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 70,
+   "execution_count": 174,
    "id": "76aa964f-0c84-490d-897a-d00e3966e2c9",
    "metadata": {},
    "outputs": [
@@ -599,7 +599,7 @@
        "'bbal6n'"
       ]
      },
-     "execution_count": 70,
+     "execution_count": 174,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -610,7 +610,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 71,
+   "execution_count": 175,
    "id": "eb602c71-8560-4f9e-b26b-08202febb937",
    "metadata": {
     "scrolled": true,
@@ -623,17 +623,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 72,
+   "execution_count": 176,
    "id": "0e3184a1-6b02-4b4f-84a8-a0a65f951ea2",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       ""
+       ""
       ]
      },
-     "execution_count": 72,
+     "execution_count": 176,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -654,7 +654,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 73,
+   "execution_count": 177,
    "id": "d7ec0833-d54b-4073-84cf-92d011c60ec1",
    "metadata": {},
    "outputs": [
@@ -666,7 +666,7 @@
        "       39, 14, 15, 23], dtype=int64)>"
       ]
      },
-     "execution_count": 73,
+     "execution_count": 177,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -677,7 +677,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 74,
+   "execution_count": 178,
    "id": "fe1ad370-b287-4b46-85a2-7c45b0bd9b10",
    "metadata": {},
    "outputs": [
@@ -687,7 +687,7 @@
        ""
       ]
      },
-     "execution_count": 74,
+     "execution_count": 178,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -698,7 +698,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 75,
+   "execution_count": 179,
    "id": "6871031a-b0ba-4c76-a852-f6329b0f2606",
    "metadata": {
     "tags": []
@@ -722,7 +722,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 76,
+   "execution_count": 180,
    "id": "7686355d-45aa-4c85-ad9c-053e6a9b4d81",
    "metadata": {
     "tags": []
@@ -734,7 +734,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 77,
+   "execution_count": 181,
    "id": "f066fea2-91b1-42ed-a67d-00566a1a53ff",
    "metadata": {
     "tags": []
@@ -753,7 +753,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 78,
+   "execution_count": 182,
    "id": "6b1365bd-7742-41d1-95d4-247021751c3a",
    "metadata": {},
    "outputs": [
@@ -763,7 +763,7 @@
        "50"
       ]
      },
-     "execution_count": 78,
+     "execution_count": 182,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -774,7 +774,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 79,
+   "execution_count": 183,
    "id": "5281bde8-fdc8-4da1-bd55-5a7929a9e80c",
    "metadata": {},
    "outputs": [],
@@ -784,7 +784,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 80,
+   "execution_count": 184,
    "id": "cbebe683-6afd-47fd-bba4-c83b4b13bb32",
    "metadata": {},
    "outputs": [
@@ -794,7 +794,7 @@
        "2"
       ]
      },
-     "execution_count": 80,
+     "execution_count": 184,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -805,7 +805,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 81,
+   "execution_count": 185,
    "id": "5cf2d676-93a9-434c-b3c7-bdcc2577b2e7",
    "metadata": {
     "tags": []
@@ -817,7 +817,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 82,
+   "execution_count": 186,
    "id": "efa6cd46-7079-46c0-b45b-832f339f6cb0",
    "metadata": {
     "scrolled": true,
@@ -827,626 +827,626 @@
     {
      "data": {
       "text/plain": [
-       "array([[[[[1.5723375 ],\n",
-       "          [1.6097741 ],\n",
-       "          [1.5723375 ],\n",
+       "array([[[[[ 1.4638735 ],\n",
+       "          [ 1.4638735 ],\n",
+       "          [ 1.4638735 ],\n",
        "          ...,\n",
-       "          [0.33692947],\n",
-       "          [0.33692947],\n",
-       "          [0.33692947]],\n",
+       "          [ 0.2373849 ],\n",
+       "          [ 0.2373849 ],\n",
+       "          [ 0.15825659]],\n",
        "\n",
-       "         [[1.6097741 ],\n",
-       "          [1.6472107 ],\n",
-       "          [1.6097741 ],\n",
+       "         [[ 1.4638735 ],\n",
+       "          [ 1.4638735 ],\n",
+       "          [ 1.4638735 ],\n",
        "          ...,\n",
-       "          [0.33692947],\n",
-       "          [0.33692947],\n",
-       "          [0.33692947]],\n",
+       "          [ 0.2373849 ],\n",
+       "          [ 0.2373849 ],\n",
+       "          [ 0.15825659]],\n",
        "\n",
-       "         [[1.4974643 ],\n",
-       "          [1.4974643 ],\n",
-       "          [1.4974643 ],\n",
+       "         [[ 1.4638735 ],\n",
+       "          [ 1.4638735 ],\n",
+       "          [ 1.4243094 ],\n",
        "          ...,\n",
-       "          [0.37436607],\n",
-       "          [0.29949287],\n",
-       "          [0.29949287]],\n",
+       "          [ 0.2373849 ],\n",
+       "          [ 0.2373849 ],\n",
+       "          [ 0.2373849 ]],\n",
        "\n",
        "         ...,\n",
        "\n",
-       "         [[0.9733518 ],\n",
-       "          [0.9733518 ],\n",
-       "          [0.9733518 ],\n",
+       "         [[ 0.98910373],\n",
+       "          [ 0.98910373],\n",
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+       "          [ 0.        ],\n",
+       "          [ 0.        ],\n",
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        "\n",
        "\n",
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        "\n",
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        "          ...,\n",
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        "          ...,\n",
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        "\n",
-       "         [[0.9030205 ],\n",
-       "          [0.9030205 ],\n",
-       "          [0.9030205 ],\n",
+       "         [[ 0.8534356 ],\n",
+       "          [ 0.8534356 ],\n",
+       "          [ 0.8534356 ],\n",
        "          ...,\n",
-       "          [8.741239  ],\n",
-       "          [8.957963  ],\n",
-       "          [8.885722  ]],\n",
+       "          [ 9.775717  ],\n",
+       "          [ 9.814508  ],\n",
+       "          [ 9.853301  ]],\n",
        "\n",
-       "         [[0.93914133],\n",
-       "          [0.9030205 ],\n",
-       "          [0.9030205 ],\n",
+       "         [[ 0.89222807],\n",
+       "          [ 0.89222807],\n",
+       "          [ 0.8534356 ],\n",
        "          ...,\n",
-       "          [8.777359  ],\n",
-       "          [8.921843  ],\n",
-       "          [8.921843  ]]]]], dtype=float32)"
+       "          [ 9.736924  ],\n",
+       "          [ 9.814508  ],\n",
+       "          [ 9.814508  ]]]]], dtype=float32)"
       ]
      },
-     "execution_count": 82,
+     "execution_count": 186,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1457,7 +1457,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 83,
+   "execution_count": 187,
    "id": "acf5eb4f-a0da-4a9a-bf24-af13e9cc2fbe",
    "metadata": {
     "tags": []
@@ -1467,81 +1467,81 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Lossy conversion from float32 to uint8. Range [0.0, 9.546335220336914]. Convert image to uint8 prior to saving to suppress this warning.\n",
-      "Lossy conversion from float32 to uint8. Range [0.0, 9.546335220336914]. Convert image to uint8 prior to saving to suppress this warning.\n",
-      "Lossy conversion from float32 to uint8. Range [0.0, 9.546335220336914]. Convert image to uint8 prior to saving to suppress this warning.\n",
-      "Lossy conversion from float32 to uint8. Range [0.0, 9.546335220336914]. Convert image to uint8 prior to saving to suppress this warning.\n",
-      "Lossy conversion from float32 to uint8. Range [0.0, 9.546335220336914]. Convert image to uint8 prior to saving to suppress this warning.\n",
-      "Lossy conversion from float32 to uint8. Range [0.0, 9.546335220336914]. Convert image to uint8 prior to saving to suppress this warning.\n",
-      "Lossy conversion from float32 to uint8. Range [0.0, 9.546335220336914]. Convert image to uint8 prior to saving to suppress this warning.\n",
-      "Lossy conversion from float32 to uint8. Range [0.0, 9.546335220336914]. Convert image to uint8 prior to saving to suppress this warning.\n",
-      "Lossy conversion from float32 to uint8. Range [0.0, 9.546335220336914]. Convert image to uint8 prior to saving to suppress this warning.\n",
-      "Lossy conversion from float32 to uint8. Range [0.0, 9.546335220336914]. Convert image to uint8 prior to saving to suppress this warning.\n",
-      "Lossy conversion from float32 to uint8. Range [0.0, 9.546335220336914]. Convert image to uint8 prior to saving to suppress this warning.\n",
-      "Lossy conversion from float32 to uint8. Range [0.0, 9.546335220336914]. Convert image to uint8 prior to saving to suppress this warning.\n",
-      "Lossy conversion from float32 to uint8. Range [0.0, 9.546335220336914]. Convert image to uint8 prior to saving to suppress this warning.\n",
-      "Lossy conversion from float32 to uint8. Range [0.0, 9.546335220336914]. Convert image to uint8 prior to saving to suppress this warning.\n",
-      "Lossy conversion from float32 to uint8. Range [0.0, 9.546335220336914]. Convert image to uint8 prior to saving to suppress this warning.\n",
-      "Lossy conversion from float32 to uint8. Range [0.0, 9.546335220336914]. Convert image to uint8 prior to saving to suppress this warning.\n",
-      "Lossy conversion from float32 to uint8. Range [0.0, 9.546335220336914]. Convert image to uint8 prior to saving to suppress this warning.\n",
-      "Lossy conversion from float32 to uint8. Range [0.0, 9.546335220336914]. Convert image to uint8 prior to saving to suppress this warning.\n",
-      "Lossy conversion from float32 to uint8. Range [0.0, 9.546335220336914]. Convert image to uint8 prior to saving to suppress this warning.\n",
-      "Lossy conversion from float32 to uint8. Range [0.0, 9.546335220336914]. Convert image to uint8 prior to saving to suppress this warning.\n",
-      "Lossy conversion from float32 to uint8. Range [0.0, 9.546335220336914]. Convert image to uint8 prior to saving to suppress this warning.\n",
-      "Lossy conversion from float32 to uint8. Range [0.0, 9.546335220336914]. Convert image to uint8 prior to saving to suppress this warning.\n",
-      "Lossy conversion from float32 to uint8. Range [0.0, 9.546335220336914]. Convert image to uint8 prior to saving to suppress this warning.\n",
-      "Lossy conversion from float32 to uint8. Range [0.0, 9.546335220336914]. Convert image to uint8 prior to saving to suppress this warning.\n",
-      "Lossy conversion from float32 to uint8. Range [0.0, 9.546335220336914]. Convert image to uint8 prior to saving to suppress this warning.\n",
-      "Lossy conversion from float32 to uint8. Range [0.0, 9.546335220336914]. Convert image to uint8 prior to saving to suppress this warning.\n",
-      "Lossy conversion from float32 to uint8. Range [0.0, 9.546335220336914]. Convert image to uint8 prior to saving to suppress this warning.\n",
-      "Lossy conversion from float32 to uint8. Range [0.0, 9.546335220336914]. Convert image to uint8 prior to saving to suppress this warning.\n",
-      "Lossy conversion from float32 to uint8. Range [0.0, 9.546335220336914]. Convert image to uint8 prior to saving to suppress this warning.\n",
-      "Lossy conversion from float32 to uint8. Range [0.0, 9.546335220336914]. Convert image to uint8 prior to saving to suppress this warning.\n",
-      "Lossy conversion from float32 to uint8. Range [0.0, 9.546335220336914]. Convert image to uint8 prior to saving to suppress this warning.\n",
-      "Lossy conversion from float32 to uint8. Range [0.0, 9.546335220336914]. Convert image to uint8 prior to saving to suppress this warning.\n",
-      "Lossy conversion from float32 to uint8. Range [0.0, 9.546335220336914]. Convert image to uint8 prior to saving to suppress this warning.\n",
-      "Lossy conversion from float32 to uint8. Range [0.0, 9.546335220336914]. Convert image to uint8 prior to saving to suppress this warning.\n",
-      "Lossy conversion from float32 to uint8. Range [0.0, 9.546335220336914]. Convert image to uint8 prior to saving to suppress this warning.\n",
-      "Lossy conversion from float32 to uint8. Range [0.0, 9.546335220336914]. Convert image to uint8 prior to saving to suppress this warning.\n",
-      "Lossy conversion from float32 to uint8. Range [0.0, 9.546335220336914]. Convert image to uint8 prior to saving to suppress this warning.\n",
-      "Lossy conversion from float32 to uint8. Range [0.0, 9.546335220336914]. Convert image to uint8 prior to saving to suppress this warning.\n",
-      "Lossy conversion from float32 to uint8. Range [0.0, 9.546335220336914]. Convert image to uint8 prior to saving to suppress this warning.\n",
-      "Lossy conversion from float32 to uint8. Range [0.0, 9.546335220336914]. Convert image to uint8 prior to saving to suppress this warning.\n",
-      "Lossy conversion from float32 to uint8. Range [0.0, 9.546335220336914]. Convert image to uint8 prior to saving to suppress this warning.\n",
-      "Lossy conversion from float32 to uint8. Range [0.0, 9.546335220336914]. Convert image to uint8 prior to saving to suppress this warning.\n",
-      "Lossy conversion from float32 to uint8. Range [0.0, 9.546335220336914]. Convert image to uint8 prior to saving to suppress this warning.\n",
-      "Lossy conversion from float32 to uint8. Range [0.0, 9.546335220336914]. Convert image to uint8 prior to saving to suppress this warning.\n",
-      "Lossy conversion from float32 to uint8. Range [0.0, 9.546335220336914]. Convert image to uint8 prior to saving to suppress this warning.\n",
-      "Lossy conversion from float32 to uint8. Range [0.0, 9.546335220336914]. Convert image to uint8 prior to saving to suppress this warning.\n",
-      "Lossy conversion from float32 to uint8. Range [0.0, 9.546335220336914]. Convert image to uint8 prior to saving to suppress this warning.\n",
-      "Lossy conversion from float32 to uint8. Range [0.0, 9.546335220336914]. Convert image to uint8 prior to saving to suppress this warning.\n",
-      "Lossy conversion from float32 to uint8. Range [0.0, 9.546335220336914]. Convert image to uint8 prior to saving to suppress this warning.\n",
-      "Lossy conversion from float32 to uint8. Range [0.0, 9.546335220336914]. Convert image to uint8 prior to saving to suppress this warning.\n",
-      "Lossy conversion from float32 to uint8. Range [0.0, 9.546335220336914]. Convert image to uint8 prior to saving to suppress this warning.\n",
-      "Lossy conversion from float32 to uint8. Range [0.0, 9.546335220336914]. Convert image to uint8 prior to saving to suppress this warning.\n",
-      "Lossy conversion from float32 to uint8. Range [0.0, 9.546335220336914]. Convert image to uint8 prior to saving to suppress this warning.\n",
-      "Lossy conversion from float32 to uint8. Range [0.0, 9.546335220336914]. Convert image to uint8 prior to saving to suppress this warning.\n",
-      "Lossy conversion from float32 to uint8. Range [0.0, 9.546335220336914]. Convert image to uint8 prior to saving to suppress this warning.\n",
-      "Lossy conversion from float32 to uint8. Range [0.0, 9.546335220336914]. Convert image to uint8 prior to saving to suppress this warning.\n",
-      "Lossy conversion from float32 to uint8. Range [0.0, 9.546335220336914]. Convert image to uint8 prior to saving to suppress this warning.\n",
-      "Lossy conversion from float32 to uint8. Range [0.0, 9.546335220336914]. Convert image to uint8 prior to saving to suppress this warning.\n",
-      "Lossy conversion from float32 to uint8. Range [0.0, 9.546335220336914]. Convert image to uint8 prior to saving to suppress this warning.\n",
-      "Lossy conversion from float32 to uint8. Range [0.0, 9.546335220336914]. Convert image to uint8 prior to saving to suppress this warning.\n",
-      "Lossy conversion from float32 to uint8. Range [0.0, 9.546335220336914]. Convert image to uint8 prior to saving to suppress this warning.\n",
-      "Lossy conversion from float32 to uint8. Range [0.0, 9.546335220336914]. Convert image to uint8 prior to saving to suppress this warning.\n",
-      "Lossy conversion from float32 to uint8. Range [0.0, 9.546335220336914]. Convert image to uint8 prior to saving to suppress this warning.\n",
-      "Lossy conversion from float32 to uint8. Range [0.0, 9.546335220336914]. Convert image to uint8 prior to saving to suppress this warning.\n",
-      "Lossy conversion from float32 to uint8. Range [0.0, 9.546335220336914]. Convert image to uint8 prior to saving to suppress this warning.\n",
-      "Lossy conversion from float32 to uint8. Range [0.0, 9.546335220336914]. Convert image to uint8 prior to saving to suppress this warning.\n",
-      "Lossy conversion from float32 to uint8. Range [0.0, 9.546335220336914]. Convert image to uint8 prior to saving to suppress this warning.\n",
-      "Lossy conversion from float32 to uint8. Range [0.0, 9.546335220336914]. Convert image to uint8 prior to saving to suppress this warning.\n",
-      "Lossy conversion from float32 to uint8. Range [0.0, 9.546335220336914]. Convert image to uint8 prior to saving to suppress this warning.\n",
-      "Lossy conversion from float32 to uint8. Range [0.0, 9.546335220336914]. Convert image to uint8 prior to saving to suppress this warning.\n",
-      "Lossy conversion from float32 to uint8. Range [0.0, 9.546335220336914]. Convert image to uint8 prior to saving to suppress this warning.\n",
-      "Lossy conversion from float32 to uint8. Range [0.0, 9.546335220336914]. Convert image to uint8 prior to saving to suppress this warning.\n",
-      "Lossy conversion from float32 to uint8. Range [0.0, 9.546335220336914]. Convert image to uint8 prior to saving to suppress this warning.\n",
-      "Lossy conversion from float32 to uint8. Range [0.0, 9.546335220336914]. Convert image to uint8 prior to saving to suppress this warning.\n",
-      "Lossy conversion from float32 to uint8. Range [0.0, 9.546335220336914]. Convert image to uint8 prior to saving to suppress this warning.\n"
+      "Lossy conversion from float32 to uint8. Range [0.0, 10.088857650756836]. Convert image to uint8 prior to saving to suppress this warning.\n",
+      "Lossy conversion from float32 to uint8. Range [0.0, 10.088857650756836]. Convert image to uint8 prior to saving to suppress this warning.\n",
+      "Lossy conversion from float32 to uint8. Range [0.0, 10.088857650756836]. Convert image to uint8 prior to saving to suppress this warning.\n",
+      "Lossy conversion from float32 to uint8. Range [0.0, 10.088857650756836]. Convert image to uint8 prior to saving to suppress this warning.\n",
+      "Lossy conversion from float32 to uint8. Range [0.0, 10.088857650756836]. Convert image to uint8 prior to saving to suppress this warning.\n",
+      "Lossy conversion from float32 to uint8. Range [0.0, 10.088857650756836]. Convert image to uint8 prior to saving to suppress this warning.\n",
+      "Lossy conversion from float32 to uint8. Range [0.0, 10.088857650756836]. Convert image to uint8 prior to saving to suppress this warning.\n",
+      "Lossy conversion from float32 to uint8. Range [0.0, 10.088857650756836]. Convert image to uint8 prior to saving to suppress this warning.\n",
+      "Lossy conversion from float32 to uint8. Range [0.0, 10.088857650756836]. Convert image to uint8 prior to saving to suppress this warning.\n",
+      "Lossy conversion from float32 to uint8. Range [0.0, 10.088857650756836]. Convert image to uint8 prior to saving to suppress this warning.\n",
+      "Lossy conversion from float32 to uint8. Range [0.0, 10.088857650756836]. Convert image to uint8 prior to saving to suppress this warning.\n",
+      "Lossy conversion from float32 to uint8. Range [0.0, 10.088857650756836]. Convert image to uint8 prior to saving to suppress this warning.\n",
+      "Lossy conversion from float32 to uint8. Range [0.0, 10.088857650756836]. Convert image to uint8 prior to saving to suppress this warning.\n",
+      "Lossy conversion from float32 to uint8. Range [0.0, 10.088857650756836]. Convert image to uint8 prior to saving to suppress this warning.\n",
+      "Lossy conversion from float32 to uint8. Range [0.0, 10.088857650756836]. Convert image to uint8 prior to saving to suppress this warning.\n",
+      "Lossy conversion from float32 to uint8. Range [0.0, 10.088857650756836]. Convert image to uint8 prior to saving to suppress this warning.\n",
+      "Lossy conversion from float32 to uint8. Range [0.0, 10.088857650756836]. Convert image to uint8 prior to saving to suppress this warning.\n",
+      "Lossy conversion from float32 to uint8. Range [0.0, 10.088857650756836]. Convert image to uint8 prior to saving to suppress this warning.\n",
+      "Lossy conversion from float32 to uint8. Range [0.0, 10.088857650756836]. Convert image to uint8 prior to saving to suppress this warning.\n",
+      "Lossy conversion from float32 to uint8. Range [0.0, 10.088857650756836]. Convert image to uint8 prior to saving to suppress this warning.\n",
+      "Lossy conversion from float32 to uint8. Range [0.0, 10.088857650756836]. Convert image to uint8 prior to saving to suppress this warning.\n",
+      "Lossy conversion from float32 to uint8. Range [0.0, 10.088857650756836]. Convert image to uint8 prior to saving to suppress this warning.\n",
+      "Lossy conversion from float32 to uint8. Range [0.0, 10.088857650756836]. Convert image to uint8 prior to saving to suppress this warning.\n",
+      "Lossy conversion from float32 to uint8. Range [0.0, 10.088857650756836]. Convert image to uint8 prior to saving to suppress this warning.\n",
+      "Lossy conversion from float32 to uint8. Range [0.0, 10.088857650756836]. Convert image to uint8 prior to saving to suppress this warning.\n",
+      "Lossy conversion from float32 to uint8. Range [0.0, 10.088857650756836]. Convert image to uint8 prior to saving to suppress this warning.\n",
+      "Lossy conversion from float32 to uint8. Range [0.0, 10.088857650756836]. Convert image to uint8 prior to saving to suppress this warning.\n",
+      "Lossy conversion from float32 to uint8. Range [0.0, 10.088857650756836]. Convert image to uint8 prior to saving to suppress this warning.\n",
+      "Lossy conversion from float32 to uint8. Range [0.0, 10.088857650756836]. Convert image to uint8 prior to saving to suppress this warning.\n",
+      "Lossy conversion from float32 to uint8. Range [0.0, 10.088857650756836]. Convert image to uint8 prior to saving to suppress this warning.\n",
+      "Lossy conversion from float32 to uint8. Range [0.0, 10.088857650756836]. Convert image to uint8 prior to saving to suppress this warning.\n",
+      "Lossy conversion from float32 to uint8. Range [0.0, 10.088857650756836]. Convert image to uint8 prior to saving to suppress this warning.\n",
+      "Lossy conversion from float32 to uint8. Range [0.0, 10.088857650756836]. Convert image to uint8 prior to saving to suppress this warning.\n",
+      "Lossy conversion from float32 to uint8. Range [0.0, 10.088857650756836]. Convert image to uint8 prior to saving to suppress this warning.\n",
+      "Lossy conversion from float32 to uint8. Range [0.0, 10.088857650756836]. Convert image to uint8 prior to saving to suppress this warning.\n",
+      "Lossy conversion from float32 to uint8. Range [0.0, 10.088857650756836]. Convert image to uint8 prior to saving to suppress this warning.\n",
+      "Lossy conversion from float32 to uint8. Range [0.0, 10.088857650756836]. Convert image to uint8 prior to saving to suppress this warning.\n",
+      "Lossy conversion from float32 to uint8. Range [0.0, 10.088857650756836]. Convert image to uint8 prior to saving to suppress this warning.\n",
+      "Lossy conversion from float32 to uint8. Range [0.0, 10.088857650756836]. Convert image to uint8 prior to saving to suppress this warning.\n",
+      "Lossy conversion from float32 to uint8. Range [0.0, 10.088857650756836]. Convert image to uint8 prior to saving to suppress this warning.\n",
+      "Lossy conversion from float32 to uint8. Range [0.0, 10.088857650756836]. Convert image to uint8 prior to saving to suppress this warning.\n",
+      "Lossy conversion from float32 to uint8. Range [0.0, 10.088857650756836]. Convert image to uint8 prior to saving to suppress this warning.\n",
+      "Lossy conversion from float32 to uint8. Range [0.0, 10.088857650756836]. Convert image to uint8 prior to saving to suppress this warning.\n",
+      "Lossy conversion from float32 to uint8. Range [0.0, 10.088857650756836]. Convert image to uint8 prior to saving to suppress this warning.\n",
+      "Lossy conversion from float32 to uint8. Range [0.0, 10.088857650756836]. Convert image to uint8 prior to saving to suppress this warning.\n",
+      "Lossy conversion from float32 to uint8. Range [0.0, 10.088857650756836]. Convert image to uint8 prior to saving to suppress this warning.\n",
+      "Lossy conversion from float32 to uint8. Range [0.0, 10.088857650756836]. Convert image to uint8 prior to saving to suppress this warning.\n",
+      "Lossy conversion from float32 to uint8. Range [0.0, 10.088857650756836]. Convert image to uint8 prior to saving to suppress this warning.\n",
+      "Lossy conversion from float32 to uint8. Range [0.0, 10.088857650756836]. Convert image to uint8 prior to saving to suppress this warning.\n",
+      "Lossy conversion from float32 to uint8. Range [0.0, 10.088857650756836]. Convert image to uint8 prior to saving to suppress this warning.\n",
+      "Lossy conversion from float32 to uint8. Range [0.0, 10.088857650756836]. Convert image to uint8 prior to saving to suppress this warning.\n",
+      "Lossy conversion from float32 to uint8. Range [0.0, 10.088857650756836]. Convert image to uint8 prior to saving to suppress this warning.\n",
+      "Lossy conversion from float32 to uint8. Range [0.0, 10.088857650756836]. Convert image to uint8 prior to saving to suppress this warning.\n",
+      "Lossy conversion from float32 to uint8. Range [0.0, 10.088857650756836]. Convert image to uint8 prior to saving to suppress this warning.\n",
+      "Lossy conversion from float32 to uint8. Range [0.0, 10.088857650756836]. Convert image to uint8 prior to saving to suppress this warning.\n",
+      "Lossy conversion from float32 to uint8. Range [0.0, 10.088857650756836]. Convert image to uint8 prior to saving to suppress this warning.\n",
+      "Lossy conversion from float32 to uint8. Range [0.0, 10.088857650756836]. Convert image to uint8 prior to saving to suppress this warning.\n",
+      "Lossy conversion from float32 to uint8. Range [0.0, 10.088857650756836]. Convert image to uint8 prior to saving to suppress this warning.\n",
+      "Lossy conversion from float32 to uint8. Range [0.0, 10.088857650756836]. Convert image to uint8 prior to saving to suppress this warning.\n",
+      "Lossy conversion from float32 to uint8. Range [0.0, 10.088857650756836]. Convert image to uint8 prior to saving to suppress this warning.\n",
+      "Lossy conversion from float32 to uint8. Range [0.0, 10.088857650756836]. Convert image to uint8 prior to saving to suppress this warning.\n",
+      "Lossy conversion from float32 to uint8. Range [0.0, 10.088857650756836]. Convert image to uint8 prior to saving to suppress this warning.\n",
+      "Lossy conversion from float32 to uint8. Range [0.0, 10.088857650756836]. Convert image to uint8 prior to saving to suppress this warning.\n",
+      "Lossy conversion from float32 to uint8. Range [0.0, 10.088857650756836]. Convert image to uint8 prior to saving to suppress this warning.\n",
+      "Lossy conversion from float32 to uint8. Range [0.0, 10.088857650756836]. Convert image to uint8 prior to saving to suppress this warning.\n",
+      "Lossy conversion from float32 to uint8. Range [0.0, 10.088857650756836]. Convert image to uint8 prior to saving to suppress this warning.\n",
+      "Lossy conversion from float32 to uint8. Range [0.0, 10.088857650756836]. Convert image to uint8 prior to saving to suppress this warning.\n",
+      "Lossy conversion from float32 to uint8. Range [0.0, 10.088857650756836]. Convert image to uint8 prior to saving to suppress this warning.\n",
+      "Lossy conversion from float32 to uint8. Range [0.0, 10.088857650756836]. Convert image to uint8 prior to saving to suppress this warning.\n",
+      "Lossy conversion from float32 to uint8. Range [0.0, 10.088857650756836]. Convert image to uint8 prior to saving to suppress this warning.\n",
+      "Lossy conversion from float32 to uint8. Range [0.0, 10.088857650756836]. Convert image to uint8 prior to saving to suppress this warning.\n",
+      "Lossy conversion from float32 to uint8. Range [0.0, 10.088857650756836]. Convert image to uint8 prior to saving to suppress this warning.\n",
+      "Lossy conversion from float32 to uint8. Range [0.0, 10.088857650756836]. Convert image to uint8 prior to saving to suppress this warning.\n",
+      "Lossy conversion from float32 to uint8. Range [0.0, 10.088857650756836]. Convert image to uint8 prior to saving to suppress this warning.\n",
+      "Lossy conversion from float32 to uint8. Range [0.0, 10.088857650756836]. Convert image to uint8 prior to saving to suppress this warning.\n"
      ]
     }
    ],
@@ -1551,7 +1551,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 84,
+   "execution_count": 188,
    "id": "c33a87a2-d5e0-4ec9-b174-73ebf41bf03a",
    "metadata": {
     "tags": []
@@ -1560,16 +1560,16 @@
     {
      "data": {
       "text/plain": [
-       ""
+       ""
       ]
      },
-     "execution_count": 84,
+     "execution_count": 188,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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W941LdtFvhLfhqmNUyutiGVtnAcL/hlwu2Hj4Lhkuvp4Wy7Tv4XhX68fhDOkMZTw7ayjDcHUOQT7Yq/M8cy3sy/0O0P/DJbXlWW4bILy674p9zzgOcu0hsN8eZn4XR8CPo78ow3hzuGR1uCh9jObFrc/He1OHRVk9+CCIvhXrIssGA1nmCkXPfSCaYlL6yH1O3ig0i7JfZ2zm2Ddz0j5/vEH6gGTqDpQ+n554S5S5svV+69fbWV/kjtw/YAh8jBJs3/kg+y2y50QAN6YPzlkyUzKUsc8Z8P/hvisoMxd9gfvpMh5SPORz4fiN6wp3XoGc1Dj8DngZPs6dbTgks8Gg9MdJuOTDwlcmWgKPGJePy0xCzO/yfVF05UNRFEVRlJqiLx+KoiiKotSUmWt2CaiSFZxxEZFKK4mwycwnBmVjEA01SLA2IPpqSJbLy7iMKgFtsGU6bI/L9gzItvxhlp20PVrm+/bO0UVcRYfnJMAxLdd8gnU6JV3RRU5c5hoXGKUWzB6Bo0MY1ZTDJaR+aBk8eomcZ3zFoLFs6lEGytDsw80XI5AR9VCuzfYF2h8t2nnqg7ySyzl/RotF2WnJN0qfuZSYKGyS4VJcNLO4zC4JFg01AZFRuSlpz+BpouwXx2xk4FxB1n+ooVVsv1hnz2tp3TFRNi9hzU4L4zLC7EIWGXVxbECUvSthxzENS/t5NheKcE6LWZ39cA3TDjkxwmt1CD9DppUY6+uli98vykRSXbyfQ7JYVq8r420FeI7Iy8Ls4ZK6Yn9gX88hEf6DrtWlz7FF8tp4/H8ImplEJm55n0hzzSStE2A9DnO053ieiyuqUltFURRFUWYD+vKhKIqiKEpN0ZcPRVEURVFqysz1+ZhGvByzn4LdK+SfwfYN+XywzLJ4nD/GJJV5yB4pbH0Y4jf6fdEfsjboWEZKCD0MnRuLtpF6QZkSN+wLlzaHsuq6/DocZXG0kfKwvjg20dmIQ1k4Red4mTx3l49Hte4omNW1yefh1aNrxQyoXPYbioIP/hmdMeuTkEjJcxxiGWgPZtpEWTawdu+GuPTV4P4hxwtSosrh/h/jwf01eHtYhr4yvAzlwlxOe2SkRZRxP49UQvpKFAI5T14ftTLd4by05Tcnrbx3SbpflLU1Wilme0zKp+u8evZZFJFv7Bj7hNfJ9q0NstgG4CDX5PPsywT7EiuThVmy4xGD58Kli1ewjsL9xHwZQlmj0W9OhPSGfX3HvyThHyGPc0pt+b5Fx/MEQJ8Hk7FjHjpH13GiPUdYeoSfxwn6P1aDM7z6JFDxysczzzxDV1xxBXV2dpLnefTQQw+JcmMMffnLX6ZTTjmF6urqaM2aNfTKK69MVn8VRVEURZnlVPzyMTIyQu973/toy5Yt45b/7d/+LX3zm9+ku+++m3bt2kUNDQ20du1aymQy4+6vKIqiKMrcomKzyyWXXEKXXHLJuGXGGPr6179Of/mXf0lXXXUVERH9y7/8C7W3t9NDDz1EH//4x8tuxwsC8hxL0BEdqGz/KuoUJhncFSOcMlODVyxfCocmGgE3Q+DSJ4vq6cNaazENl5onoSyAnDRp9/Vd54umpEy0uUosk2JEVZdk17EsGipj52/ycqnbGcmQLQM/dqhHFA0bef4B6ysuZ3MwGy2XTSbASJJk87wehg2Xvjn8DNGqVQ9mlyFHVuGOuJWC5lPymo4WrakB5axNMfuDIuXL8ebmk1DmWJYN9+167VihDJdnb0VzFW/jGJh9uES3s0FKXRfWWZMImpKQgbyNuNqSkFFUT01b6e176mSE1+XM1NQWk+aaS5dY80UoiqhrTjHZNQpGXYZSV5Di0HI+uxcSmNGZRwrFZx03J4Qy1aIZhvWo3CzVRCDXh67xLLNo2mD9MRm43i6zB5iLgjH2AzoU6ZqbneD8XefI+5rA6KcV/P9zma6dUaLZtfDxuvEIq46+zIQIp/v376fe3l5as2ZN6buWlhZavXo17dixYzKbUhRFURRlljKpDqe9vb1ERNTe3i6+b29vL5Uh2WyWsln7Njo4ODjufoqiKIqinBxMu9R28+bN1NLSUvrr6uqa7i4piqIoijKFTOrKR0dHBxER9fX10SmnnFL6vq+vj84555xxj9m0aRNt3LixtD04OKgvINVqOJltNXlM2qezC2S2UM/lHsOjCIdCvzO7L4R+5/JaL1+mXJfInQHXIS2uKHNulWGbA7DtFplRPoFuLTw0NvhYpJm/BGZD5SOcgHOIsYuRCMmFbZ2Y4DcH/ebS2ySh74adKwuNDBOeY2UBnFPR8duF+4BgiHT0AXHB/UywfU59LFoGvCAhM5C6QsYjXenjpc/LUlIyPD9m6+2IyRXbVjY0cUfG07WLzxXbjxx6rvQ5BnOWZ5xNeLLON4vyfueEkj+zU8YryH2MfCzlcxPmopew/0pMvnz/topw+TWwMlMAfy/WVw8vRQXZYre++uPS58u57BipxFfDtS/3HfFncFbbKpnUlY9ly5ZRR0cHbdu2rfTd4OAg7dq1i7q7u8c9JpVKUXNzs/hTFEVRFOXkpeKVj+HhYfrVr35V2t6/fz/t3buX2traaOnSpXTzzTfT1772NTr99NNp2bJl9KUvfYk6Ozvp6quvnsx+K4qiKIoyS6n45WPPnj30kY98pLT9W5PJ+vXr6dvf/jZ9/vOfp5GREfrMZz5D/f399MEPfpAef/xxSqcnyLKqKIqiKMqcoOKXjw9/+MPOsKue59FXv/pV+upXv3pCHSNjKo/b4bL5T3Go2HFxGLUMGmI5XEMf0pPzkO3y8nlxu40+F2jadpjPRSh2DL1u4iyEOcZA4W1i4AmuE59I289s3aHQ7yLksOskyrcoGna+AUX7eBDJcNQheznbF8Obu3rD7ewxh04e7fzYN04GYjSIuB8xGfCPhy1Pe9JePhRYX6EMhD7PGLvtg6NSwM4Yyxoglgcvx1ge3OcDfUc4OSjj/R4NZPyERnb+MRhDDOHeGhstfT4tIX0+0iI+ifRzSLDrOGzk+XqYVp3B/UPicL15fBDuG/J2+3j+dtzSHvrqRM8bPk9dMWYQHm7ci8Fsx/OtNj08j9cRei7avmLslItPXRXZt1BMDodv2PHAzhsvJWO3uNLPy2e4I8YR1sH7iv2shAr8WmrJtKtdFEVRFEWZW+jLh6IoiqIoNUWz2s50cCmOhzjGpUdeNiqX1mOj0uem0IQBmlk1bOnRz4OclpmEUGrrRJzHBMuAznDEPKutIyx7qM7y3rMxiy1m/SyKsuh6sLUGx3IuX6L34RwSbBl+NJAmEb587jLXELn7Kuo0suc89HkOzABNnp1jRWh/NLDL0gGMRr0XLYuNYeZaNuJ5kKyOsDZiFfyO4qYllNo2+1KyysO9N4BJKiHMVTjAdjzyIKfc+tqu0ufLl6wUZZd2nWc34Dgvbu/ZKxefJ8oeBDNMmo8pXBtnRHHHPOLmDJQIc1OSwSy2rtDcSOHEZbqhvrH/chVlaoWw8EPMfOE0s0wFaMo6ETPMDEFXPhRFURRFqSn68qEoiqIoSk3Rlw9FURRFUWrK3PH5cNnoUBbKDzsBiS7KRMvG0R+3XwMrK0h7pQ/S2yBu5YdcPvt2IdvOQT0Ztu3wOTkhyq3HIU3z4tLubPI85Xa0vbRYwTlgePXqw6Sz9sEfhvug4HE+2zcL/gEZgz4Y9lbPOX5zJD15vUeFX0X02GCZSyJ7rNgYuS/3MSGSkmX0R+FtjkIb3K8jEZPnxKW+KO1dGJdh0nk9KRibJp/7fETLWdFviEs2Hz20R5RdcZqNBO0lpJxThC0HaW29L+XECTYeYT+m8lIf4L2QJTsWjx3qEWWXLn6/7Vp8gn8rYq46fv86JKIhnwu2beA+Efc+SvfRP8WbhH+Jofs7KK+skjorCbeuUltFURRFURR9+VAURVEUpcbMHbPLTIO99nloZik3iqdLagqSNX9gVG63WemtV5D1cDMMz1RLRMIkVLVJaoIMs0KqB80bZtzwcDmRyc8MmJ1E1kvo9xNsCfl4UY5bBprIM/NJK66Ess9oIkk7lnMzxraJWXTTvj0Ol8u5KaeSXxFJWOotCqkvlDFTB8pgeRmaVrhpA6OPYlZbbvrA6Kd81ibA7JEQEUZBviuyD0fLNzGia5OfgXKWKRrOP8nut1G4blzaXO+Y7ii1FbJQkHpyc8UTYPbAucG3Uzj3WH9cZkY014jbrRKLMmam5vfGFEhGPXi+GFc4Z0e0WQPP0JzR3+qTiY6moiiKoig1RV8+FEVRFEWpKfryoSiKoihKTTm5fD5cPghod5QHRpdMIJet1u9BhCZH/wReJ2SuFf4S2DcuL8WMt2PSJi7MoHAOXKoW8kfhGXdd5tqQrwarMw82eLDR8jHF7L9ivF1jD9JT4ecBviJFtm8+JHWVcF8CHJoUkz/We1L6yO3nYft8dChsvq8Pg5plviIZGAu0ZLfF8mzfaBkuSk+5Lwf6TiRYJteYkfNNZqp1/8bhbbzlkOFi6HPen+aQr4b05eAUhd+OrDMNfiUZ7q8Ctxs/K5TaptlmAg5MyXjfokz4eeSjJbFZE31+4X3ldeNz0ZXh1hVqHX1FHjv8fOnzZe/oFmUVBRwwDlkq3/YdDx84TmQFCJVF+3ygPwoPWU+J6PQU+Owpu8zl/1KJtHYqcP7/ZFTgF6MrH4qiKIqi1BR9+VAURVEUpaacXGaX2QRftsSMhdws4TItYKROvg3LiWZESm3jIlKprIabWngW29+U2o+uyLDYN75sOIHUVtSD58/rdZ1/BRRY5tSJIpzyKxVzZKBFUp5dpg2bXWybQWhp37DPEr5kPtGZ9zPTylAgTUIZY/uGcta0b5f3MfppEzNZFKF3Q4GVcmNG3QDMPscK1tSCct5630anLIKclvcb++aLrK4QxRTqcYFmGM4AM99htFtuhnkDzHz1nj2n7x3aAcfZ64RmNp/de5gpeNTkxDaXbI/AUj+fpXh2vCwNJgpXZF4e4dSLy1pNTvbN48+7MrNNh4AxFWZVfL4EvqMM7hyenTeL2Zcr7uXJQ7nPVpdZCdCVD0VRFEVRaoq+fCiKoiiKUlP05UNRFEVRlJoyc30+AqpQozWB7BVDFTOqzj47wbGu/vAQ4qH9UkmKxCE1NUmH/AuIH7f2+sK8uujm0EbKTavQvpezviqhsOw86yRmkqxErsxsvaHw6q7rmGfSRLBLcts6ChgxpDLPZDrPT4syaZOXfRsI7HhjCHWeATftybHhfQtLJvl+khGQvr6aX1D6nAnkPGmNWX8g9Pk4Lf4WRTHCfC6wzjxr/41Ckyjry7eI7VHmg9IUk5JZLsMdKsrx5v4o4ay6hXH3e7vf3P9F1tngSf+EenYszgWecTcB7efYNcbw6vy65WEu8nrimBmZ+UcE4Dc0EMjtEebnkHRKhGUZ92MKyXBZX+shq6649/D+xiy33M8D/d24DBnCm/NnOPqRiDJ4Lshw69C3tMwcLPoG16bJ4Z8SyrIbRQXhAURfapGZ1tUGSm2r9K8TVZ5wDYqiKIqiKBWgLx+KoiiKotSUmWt2qSFVZ2edCEe9YpGukvbL3Rf3A7OTN2aXLb0mufQcJFg0UqjWFWFUmFpwGVJsY0RVRxTTSpb3yhwbXJblER/xbdyVyfTyxStEGY/y6AKlj5z6kLzRLhOjtJfLgo+B1LIfzAn9xfrS5xyYZHhG2IVgdvCZacFVJ0o/eSZbLoklIsoGsn0uhUXzDd/Ow3J+kaSJhsPNLhjFNMGuaR4ioWJfuY0kDRFeW9ixLoMnmvKEXBvuMD4XQ2Y2HokX5hCq3nHelourby6pLSdkgsAle+MwGPKykMmXbUOdeE/LDkX320BUUS/JpLYj5UuyBVOQqXfKcEljJ8G04kJXPhRFURRFqSkVvXxs3ryZzjvvPGpqaqJFixbR1VdfTfv27RP7ZDIZ2rBhA82fP58aGxtp3bp11NfXN6mdVhRFURRl9lLRy8f27dtpw4YNtHPnTnryyScpn8/T7//+79PIyEhpn1tuuYW2bt1KDzzwAG3fvp2OHDlC11xzzaR3XFEURVGU2UlFPh+PP/642P72t79NixYtop6eHvrd3/1dGhgYoHvuuYfuu+8+uvDCC4mI6N5776UzzjiDdu7cSeeff/7k9fw3TJm/xlTj6rdLtuUq86P9MULyt5y1RHtgowzq7L4+KNpEllunX4eEh2n3DNpywQckcMj2WEBolAF73A6M2SuTVs6J58tDnyfB5wH9U+qZD4aXkjI9HmL60cM9ooyHXg8gqDUPxZ0AqS33K3niyF5Rlg15E1gCkIVyvw6MxY6hyTkoYRVtsN8u9R7ax+3YYF+4fBZphNDnQZl6+5FAXgue1TZj8DFnfSlQWuw6XyTDzgvDwrt+1cnrLffk8tYiYd+ixwIls/xItNzzFhMo52Vt1PvSk4XLvoMJA/qXCfpH8Hva5ceBzwXM/s1x1OOSyBqQ+vJr5YUkwuVmfY0et5D/CfO3wvOtmgrCnzsz2U631HZgYICIiNra2oiIqKenh/L5PK1Zs6a0z/Lly2np0qW0Y8eOcevIZrM0ODgo/hRFURRFOXmp+uUjCAK6+eab6YILLqAzzzyTiIh6e3spmUxSa2ur2Le9vZ16e3vHrWfz5s3U0tJS+uvq6qq2S4qiKIqizAKqltpu2LCBXnrpJXr22WdPqAObNm2ijRs3lrYHBwedLyCz1sxSCyoZm4JdmI0NQlTJZhZh1RWQzyGRDUV+5at0WOaSJOPynkvGxpZCPYj2algESK8QPe0xqy1mZOUSR1yy5bvisnSeLYSjLLPR41ll5fIqN7UMBxj9k8syZZ0JkIXOjw2P+5mIqMm39WIW13q+DZJVHg0Uj8sw00oOzCw+mCiyTE6b8qNNSbyfREQNjuy0TTHb13D0UxapE80HriC58Fsty84rBvOmiS2ZuxbMUTLrgkfNzcM5ueS8OIdT3LIBx/G+YvZlbswJzdPDL5Q+X7x0pSjzMMO2KItswm0uQbMHz0aL0az5GAdwHJprWJv/+7XnRNEYt2qjiYSfR7WZeqeKSkwt5cJNMlWaYKp6+bjhhhvo0UcfpWeeeYaWLFlS+r6jo4NyuRz19/eL1Y++vj7q6OgYt65UKkUpsJ0riqIoinLyUtErmjGGbrjhBnrwwQfpqaeeomXLlonyFStWUCKRoG3btpW+27dvHx04cIC6u7snp8eKoiiKosxqKlr52LBhA91333308MMPU1NTU8mPo6Wlherq6qilpYWuv/562rhxI7W1tVFzczPdeOON1N3dPSVKF0VRFEVRZh8VvXzcddddRET04Q9/WHx/77330qc+9SkiIrrjjjvI931at24dZbNZWrt2Ld15550Vd8wzZnL9O1x2qRPIals11Z6bMyuiw0aKmXKZzc4blbb0IGazjnKJLJGUV8byaFtlGWexa6weIaUlkMgSlW1DRMmsAOvkWThDYcpZeO8JLovPjg1y0f4JGQiNPcp8TjAjKLefZ42sE31QRBmznuMSZiv4QyS8aB8I7gOQhrHhfWuDNvKGZ5WVx42wcO9FelOUoa9Gf7GBfa4XZVwKy/04iIhafZuNFzPXcn+UNtCLt7K5kfLkIxB9dfgYB2A7H2Lb6MfT5Nt6MTtsVsy38u3lo0yiPgoh6lMoGWY+J2kveg65lr4r8UfhcvUQmNqB+4A4/DoMZs32ypThIjyDOEp00zJlgGExq5A6j8n1Q2EGauznMRV+HDWmopcPnAzjkU6nacuWLbRly5aqO6UoiqIoysnLDHPLVRRFURTlZOfkzmqrstwSoSVMZtowo6OiLJZbaPfDdJmy0snpnAtsw9Wm0wwTbVqLsSVTjBQZbVghevSglOLxJftjsNTM63FFqkQzC89kW+/JJeJG9nkBSBbXdp4jtjE6qugbk1SivBIllZyASVZHAzlSXN45H8wsuN0fs4EFX8m1Q/u2P60xuSTewSTDCZDvtrGfVU2+VNNduWRV6TOPfEtElS3nszkVkl2zubj11xhg0ZqBfEek0EwF99coRHHl0t8YmKQSrKuj0AbO/yjQyMJNlyETRAXROflzyhV9tGrQpIvXLWPnZgDmMr/MaLsnC3z8y7F6VIqufCiKoiiKUlP05UNRFEVRlJqiLx+KoiiKotSUk8vnYxIy7c0IqrWvObIQemmIIsttnVJpC/vBtoiNDPZSnrkWfUW4SRjPD66bCKkONniPhYV3Xm+X7Nhhg47O7/o23F8jJIsVGUmjGQqkDLfIshGjPwj387jkHRArJ2Et714cbP7z5K4XL1td+myy0ufifx3aGdm3UTaMORMdXnzQSIksz2RbDz4eDRD6ndOZOC62eXZaPI6Hfk/APOX9Hi3K9v/1gE0JUQ8+LT78HuNS3Eu7zpP7Juw9hRmOiWVEveI0CLDI/CMeP7BHFHGfm1iA4ePZfPflWPQH0ndFZPINJY61g5PANADMdyYFY8pl2DhO3G8q5F/lyo5aCS7fkZz1ozF5OTY+S7UQ8l2Iyzq5fP7yJTJMPPdliTXjObJtDO9eJiEfF4efWq0J+TRNgg+IrnwoiqIoilJT9OVDURRFUZSaoi8fiqIoiqLUlJPL56NapiMeiEvDPhX9KYAdkqeSBhtpbJRtYxRh3reQZp7ZT/EcKtHs82NdcT5ccT3wuDLtsJiaHv0cfLJj0wRh0odYCHWX9xGGcE/wMN0QpprH64jNq5MHsuNMToYQNwV5Tf+//9xe+nwM/DoOFuw5DhnpuzAUWJ8THgadiGiE+RlkjPQ5CFgocB/Ce2O8jiTz3WhjsTuIiJLEQ5HjeNs2izBR+wPrg1I06J9g6+Q+JURE830Z8yblWYeoe369XZTx6/+xZR8SZX6DvVZec6Mo435Ml7z7d0QRv46P798l+xKwaxzI690vW6CRwF7HEZLXlI+3D/FReFj6VghLnyzzFp6S+BxEMqQ4xBIRPggYepz7nMCzDp8Tjxy0/k88Hsy0E8N1AtguRPtROWFzsSI/Dv7s5+Nryl/P0JUPRVEURVFqir58KIqiKIpSU2af2aVaOa1riT60pDXNVLtsGThMCyApM/Fo2ZqfsUt4+XkypLefY0u2ocyx9qOXgWXARPkhlgUOs4vJ5qAs+hqbMbt8jlLTouO4LCz1J9m+eTDl8FrQDMDBrLL8uJjj2j/40v8R20cK9jyOgdQyD/3+cabJ7luUZgC+RF+E3yOByKQql+9d5yjrkHW+mW8S26Os7y1xCPXPxioTSJNUlslJG2MuvbikhWXHTYJ89zDNw91LBDA2PPT7X+yTofYXMvNNZ1yOU6Nnx/Gy06R82mPPorVLVkAHbHuPHN4tio7ArT8UWLPPCMwNnvE2BgbCNkfI+hYmIMd5unbxuXYDTFmh7NNlPm8DV1bbRAJ3Ln184vALouiS0y+wdSTgXx48w3h6A5cknxIQlp8/QyoIJy/A7MNcajuRWYWboSrJeMtTBFRwmJkE+fQM+6+rKIqiKMrJjr58KIqiKIpSU/TlQ1EURVGUmjL7fD6UScHjEisI4xvrt3bfYoO0rXqOlONenhme0XehWmlxtbJjsDNz6akJ+WqwcNMTuDGg9JYzZDDR+Pigz8cbRWsjRunj/2Byy2czMoT54fwp0W2A/fiNgvWzOJprFmXZwD4GUhi2u2DbHCvK8/PZeQTg/5Fk9dTFpA8AtjFSsD4Qw0XpV1II7NhgG0Xmj1IXkzJk3gb3zXi7DenHxHH5juQD+bjk/TmSbxVlaeZL0gLS4mbWxt++8owoa/J4v6F9dklfyMrz/Wm2S2wfzdtrPFCQ+9bHrK/UvLjsG59/zYEciwwb01ggr+ljh3psGchghT8IERH3R4J9PYc/CJeCevl85H6h41jI9JDPB4QgGOYpE6BvwpeiEr8Kjkvyj+3xZ+1EfiR8X1e/XWA4d9eznl0n4/KndDVX1VGKoiiKoihVoi8fiqIoiqLUFDW7zEQqidxZLpiVkEus0rAMzZYiubSWiMhwySxmo+Vml8nKZBmS89ptD+XChi8FggyXLyHCGCZYZNb0BDJnLr0VmUOJ6GjRmja41JFIykQzYJ7hsthsEF0WkpoyMwCaFlBqO8LMGf056FvR1jOSl2aPo8NWlpvNy/P1mGlnUbOMTNpeP1T6jFJbjLjJl/rzRZT62m0018R9e845MInwbW6ewbJsUR6HJiLeBpYFJnqu+ELOOl+UNcatRPrV2CJRxq8jSpl52UBBmuCOgnz5WM6WowmuKW7NKaF5w7IjowmQUwTTYYHncYZHFDfJEMnswB7YlsqNshnaj5kWUKLsN7Dsw2jWwWcBG/OwCYjLWSuI4MzLIPu0eNaj2UOYaCYwu7iy6rpkuA7TiqgT9qvW1CKqP+EaFEVRFEVRKkBfPhRFURRFqSn68qEoiqIoSk1Rn4+5gsMmiRlQ+Z6xfhnuurDQ2pa9ItgLuV2wiHXaqWZiKOmCDjn8M6oOPY/21CoZYf4a6NfRX7R2dgxFzsGQ1jzLasyX58vt/ujHcSxns8weBz8O9IHg0lfMMnssY+t5c1hmrh0bsWGkgwLKIm09R0mSKdj2m5MynL0H7RvmO4Fy2nkpFqa8bkCUNcZsvYMF6bc0xLbfyMhw8kdH7PZYTvrR1CWlX0c6bsetEc4jyfxBkjHwR2HXNBfI64bjH1W2ON0vyhYlrR9N2pf9PCUpx2ZRgvncOHxH6iFzbYMvz7Fc8obL7GWZX+VvXMyOK/w80AfB5bvgwIzI51ueOaygX4noz1Rl7uVUG6Z9sqhyTMulollx11130dlnn03Nzc3U3NxM3d3d9IMf/KBUnslkaMOGDTR//nxqbGykdevWUV9f36R3WlEURVGU2UtFLx9Lliyh22+/nXp6emjPnj104YUX0lVXXUU/+9nPiIjolltuoa1bt9IDDzxA27dvpyNHjtA111wzJR1XFEVRFGV2UpHZ5YorrhDbt912G9111120c+dOWrJkCd1zzz1033330YUXXkhERPfeey+dccYZtHPnTjr//PPHqzIaY6qXlc4UnDImx3HVnrfrOFgmFBFOcV8mB/Mgc2yQYHJWMJ94TA7mjcroiIbL1jxYTqzkFdiV1ZgvU8bQRGDLDIwFXzLOmGizx9vldpn+LcgOy2mCSJkLY4Olz60xudTbxLKA4hK5qy87k+8ofX5pZLEoGwIzREvCZnJFyWpLwvY1BeaDMRbhNleU160Y2DHGqZ5h5oxcATIDx6UUkJs2hrPSXBVzyD3TTPqKZqZMwbY/kpcZSIuBHcciSHt5GZGU06K5hJtaUHYbeI7jmLkGr0Vb0kYcXdHwa1G2MG7n0EjgHqdWllUXzYMyi7Hsd5rNRYzEyyOsJkPRV237Cbi/cZvfi2hacUr0uYQUJKM8cqnJg8mXy1tB6mpGZITXRs/OGw9lseJAh8m5EhMvPw8MfjrdZhfXeaDJvZrqqz2wWCzS/fffTyMjI9Td3U09PT2Uz+dpzZo1pX2WL19OS5cupR07dkTWk81maXBwUPwpiqIoinLyUvHLx4svvkiNjY2USqXos5/9LD344IP0nve8h3p7eymZTFJra6vYv729nXp7eyPr27x5M7W0tJT+urq6IvdVFEVRFGX2U/HLx7vf/W7au3cv7dq1iz73uc/R+vXr6ec//3nVHdi0aRMNDAyU/g4ePFh1XYqiKIqizHwqltomk0l617veRUREK1asoN27d9M3vvEN+tjHPka5XI76+/vF6kdfXx91dHRE1pdKpSiVipYlKlME951w+IqYYch6mW078bbxlbeSSL3l+sNgZkdHHVmWyXIiU6a0icuOp5ktvxUymS5k261g55ch3aM7kEDJZN2vSp8XJ46Jsl/nFoptHrYdw2YvYLLM1oT0R+nL2uyowxB6nUtI0edhFPwsOPUJ6UfUnLQ+J3VxDG9u+zqYk34sA8b6MqCvDPeBaID2+Ha+XtrVU3F5bbhEOQ3+MA0sTDqX/RLJUOzo/8NpT0iJbGf8eOnzOxPHRRn3z+gP5Piif0Y9y47LM+y+vW37NmKirxNKu8ulGPKbghtc+GtgJtfyHgaYmZr7kYRCiPPtPPiwjcr5HmP3ogc+F9hmJJXIcHkbobDorJ7p8H+c6VltgyCgbDZLK1asoEQiQdu2bSuV7du3jw4cOEDd3d0n2oyiKIqiKCcJFa18bNq0iS655BJaunQpDQ0N0X333UdPP/00PfHEE9TS0kLXX389bdy4kdra2qi5uZluvPFG6u7urlzpoiiKoijKSUtFLx9Hjx6lT37yk/T6669TS0sLnX322fTEE0/Q7/3e7xER0R133EG+79O6desom83S2rVr6c4775ySjs96XEtzoWyKjIJj6c8lUwuij/MSCfgiOkOin7NLuCYJ04eVhc7Bcb5eKEOl3fYc52swq66IgAjZeNl5PHFkrygbZtWkoZtpR7bYrsRbsK89/1aIHJniCSJlE0Lei1l1+RI2Kt86mNQ55sm+NPlyqb+30GLbg+y4PBprLAUmKbYvNzMQEY0U7HEFyFy7qM6achJg5snDvtxEMS8pl8E5mB2XtzlWlOfkMzNEU0KOBe/PYCEVWUbkzmq7IGEz+S6ID4myehYpFKOR8vFvBpMMl2GPQNZkblrpBDMPGkj6mQwaTTJRdRIR+Wx2onmOb6Fppd63PciDZjQBvcOIyrJD0aYeIaF1SV3R5MrM+mhGRq5cbDPuxpplmZdkJqqQRNhZbXTfeDUgESZHRNcQaGqKKpukqKWTkdW2opePe+65x1meTqdpy5YttGXLlhPqlKIoiqIoJy+aWE5RFEVRlJqiLx+KoiiKotQUzWo7XdRaOuWQcYWyNzJfCrTtxY5bm2mxRWZA5T4nHhzH7bXeRNNO2DrBtsh9QDCzJX+VhvPl7YsMnESUYiGVM+DjgT4fxHw5fLCXNzBZaBPYdn023lmwz46w8yhCnVz6lw9l+LUfE3BcK/h88CE/5ggLXw9ZTRsbbT3HC/J6DxSs1DUL4c25bwyWpUD62Rq3fg6jQbT0E0ORc7ANLoNGXw0hw427s7i2xe18T4HvBverwZD5XBbLpa3YpssfA+dePQvT3gDzKw/1JNk5ZkEyiyHVRd/Y9W+CfovbC47LsHsqBvWjDwj3uVrbeY4o8/h5OSSzjx1+XhRdumQFRcLurwDCqaP/F/ZH4Dukr/x5k4A57PLHcCGyhE8g8+V9myy/DtdzmJcJSXD556orH4qiKIqi1BR9+VAURVEUpabMPrNLJdHjOHGQcEUtGyGhpe4K2hfrlLAcNQlZAUO4TDkow+WR/NIQYZZnnUyCDJct/6FENkizbLijkD2S1eOhpMyZPRHMN7l8xI5ExMpMBpbTHVJjHuEUTRu4RM230exSz8Y0AcviPLNn3kgZLpf3JmB+pdlxGVi+HmLLqxnj/h2RZMeiGaDI5gbP2ktEFGPLqPUgH+ZPj6GijD7Ku1rALMZAf6He9hNMK7xNzA7LzScoZ+XgdeLRUPG4BJg6uNwUz9/VJpcFZ0iOaYNn52YCTFB8/I9hxl1mEsmCDDbhuIVQPtzg2fPIoVmRkcffpnwVnlCGawvrMeMtzFt+vz1yeLco41JX8qPnTdbIcXvsUE/p82WnrpI7s3v//oM/FkUXL/uI2PbYozBkjuam6jEwa3KTM8F9wjOIY50scy6WEZMke67wC1OEaBOeS6G+lgrK76eufCiKoiiKUlP05UNRFEVRlJqiLx+KoiiKotSUmevzYcz4PgzV+nxMVT2zBIM2Q26jdGWSRMkq86XwB2SdJt5kN6BOfzQTWWZS0fJKJxhCPWdtrZE2SQrbq7lMMAOHpT2sp7zMlgnwc/CJh7tGKWJ0X1F6K9uwn7HfCPddaPLHRBn3AXkLZLgJlsm1AWS43D+hEcKEc6ntaFH6FGEGWg7KWbkPRigUOPf5QD8WMd6Yfdjui6Hm0QeDhz/3HemX8xAKnY9NmuCcypxD6G+EkllJ+RLHenb+afCd4H41LhlwSK7L7rfwfAb/EBPtK/PoYeu7cflih3wWuOwdNnmpCeQ5mREesh5SMiTls4c/38L+bgErg2cWf05iiHjWH+7jEToOEO2jn5wD17OvEjzHfToZ6MqHoiiKoig1RV8+FEVRFEWpKfryoSiKoihKTZm5Ph9RVBt3A/fj9VSUHrhKe1ol/eb7Vnsc4GEocu7LgDFAeD1oo2TaczMo04jTApuDOhTLg7ePcT3Q54T3B+Oz5B256R3nz8MoHy9Km/MoOy40E6BKlx18lBUlwJbO/TxcfhwZOAfenmuWxsA3JV/BNMXU6VHkDPqx2OMwvDj3pUB/jHD7rpTv0X3j/hmumBsI7w/2LQZjwc8xgN9q/BxxbLivCsYOybMU867zQzKm/Mc1nw95gzFngnE/E7n9cUQZXLK8I/h6E97u7GD89ctTH2AI9RiLnTMMz4ytr9r4HZg+YZj5mCSwRfS54M8meIYZHuMIYyOxaxp6RjPfDZOVMUB47BAPj5sopHq5uHz68NkvjouOTxJZp4ZXVxRFURRlpqIvH4qiKIqi1JTZZ3aplmrlR64lKwSXsCo5llODjLeYdbbs9vlS3JiUbPqDY7j3hHVMiGspEuvhEjdYMsWlWA4X1GGYalzMdwcKt4w6wrmPQre5bLHeYYIohkwy/LN7THm47yL85uBlKMN9o2hNaWh2kH2R58tluWiSSDpCemN4d5e8NemQ4XLzSRHMDq7zwH35WA0FMoT8SGCX3vNwjjzjLZcrYxtoLuNh8NEcJkK2gwkmZD4xLA0AtIHh5qNAcw2vB6W2uK/sjLze/B5Lw/0dCDMX9JMt6aPp0mf9eSkv63w5e2rp8znpQ7JKMG34dfYaBxBC3ePPF3z28HrQrOwwn7hksfySTpZ8thLKzmpbzvfjoCsfiqIoiqLUFH35UBRFURSlpujLh6IoiqIoNWXm+nx4nrX3u+xIk2ELc6V0NxWEmMV6+LHlSpUmwmUjrNaPwyFDxnDAJsbqKUhbtsdDqCdgavnsuJz0pAj1u8D9OqBMpKqvRNYVne7eJX11+XgkwO6dYTZp9OvIMJ+A/gBlepaFMQx9Xt38doUij8GcjgnppbymQ4G9VkWDdv44Kyv/dwyGIudtYrhz7meQhL7l2Cmir8hUgH0bDaLTAvD+JCDcdzoWLfVt8q0UE30zso7ZiD4Ygeu5xYqS4CvC56lbAg3S7og6iMbzMWHzDcqKrM1RA6npeQ3wPDtu7H3zP3sviTzuUMN8sf3Vl58V2+cm7Ty+8vTfkW3y0OuusOj4XOTPUJfvBIRld/p5VPL/w1UPPw/8P8B9GFVqqyiKoijKbEdfPhRFURRFqSkz1+zC8RzmC05FkUoZmPG12nqqPa5KMGqpe2fXMiyGIHSYdti+fBmSiMgMsYinC9rkcRm7MGtGRmQZLuHx5Udc3mTLeqZQfqZHjg/v3Gke1BCWDXGhm5toPt71AVH2yOHd9rhALhln2GmMgNmFR7zEaJj1nh3jcF8sqdDljV4WHgrJSe3B2DcOSlS5DBWX+UdZPWgSmEgWzOHmorxXrtCZKBtEm2F4f/KBfARihE+eZRf3zTLzEV43vo3jlnREP0XJrCgjLjV1j2kgzIwwp5lpo94xb3IOUxqOU9ohn8Z9eb3YtwSb5Zh9eiiIvt9fK9SVPv/irQ5R1lZno+8GcE7vTr8ONb1l9x2VUXv9puis3SbLnoVoqub/szAcQ9FhYq41VboGGPZ/rxJJsK58KIqiKIpSU07o5eP2228nz/Po5ptvLn2XyWRow4YNNH/+fGpsbKR169ZRX1/fifZTURRFUZSThKpfPnbv3k3/+I//SGeffbb4/pZbbqGtW7fSAw88QNu3b6cjR47QNddcc8IdVRRFURTl5KAqn4/h4WG67rrr6Fvf+hZ97WtfK30/MDBA99xzD91333104YUXEhHRvffeS2eccQbt3LmTzj///Mnp9WxhikPiGpD2OpOTVhQmntWLWW25DDcJdnW+L9o2ufQWpK6mAPZi3I5o35kN1yGfrvejJZKj4KuRhey0aS86e+WVi88rfcaMnD5ZKeBQIMM2vxU0RPaHg6He+SiiNwT+quByRy7nxDL0HegttJQ+oz8Il5q6/CHygexdOLw790+AcNfMPwEzznJcbaA/Cg+FnoN+xx2+Gy7paUscJdL2amHG37bYcOlzUwXZeH2RDRbCsqPvhBctZ+U+Tk2+PP8YS0OQMSjDjc5qK+qYQB7O5xh65uSZzwnKaTM8azTc3qey8feg/bGCbeX10WZR9uOhd4ntjtTC0ucvvrpXlL0/ae/b/+edHxJlhvnJoQzXsNACPHw7EZFx+TPGyvdxclLusx/bdzxrI/3tplpqu2HDBrrssstozZo14vuenh7K5/Pi++XLl9PSpUtpx44d49aVzWZpcHBQ/CmKoiiKcvJS8crH/fffT88//zzt3r07VNbb20vJZJJaW1vF9+3t7dTb2ztufZs3b6avfOUrlXZDURRFUZRZSkUvHwcPHqSbbrqJnnzySUqn0xMfUAabNm2ijRs3lrYHBwepq6tL7lSu+aJayWwlkUEd0UCdTENWQgGeIx8r17JcHJb+XCYRvkwIxwVpu/TpjUL2W1zC89mxuIznkhez8/DgOl26ZIXdALMPz7r5xJG9oigF0Thj7FgPl0VZmWiPiL538Melz/Ww1J4ju5yLUsA8D4AIU4+3noDzxX0TIvqqyyQl++aShfK+DhelSWaQSR/HinJxPQumjhwzmST96PmFWV758j6aXQoOmSg3WbQm5VzEc+SkfDlP65n5qj0xIMoWJ47bNnxpdmliJhk0ieQp+tpIiSyaUqKPQ6lrvcel3RCN1JH9WZjkKgj8jJFx0yzacdpDE5wt64fnEt9aGJPz7arTLih9Ng/K9kZydv7FwMz0eqZFbHOJdtrDzNg2I+7Dr8rIqCnPHnfJ6ReIMv7sRXMFj34akqly881kmWCq/D9k4P+HiXgOT5nUtqenh44ePUrvf//7KR6PUzwep+3bt9M3v/lNisfj1N7eTrlcjvr7+8VxfX191NHRMW6dqVSKmpubxZ+iKIqiKCcvFa18XHTRRfTiiy+K7z796U/T8uXL6dZbb6Wuri5KJBK0bds2WrduHRER7du3jw4cOEDd3d2T12tFURRFUWYtFb18NDU10Zlnnim+a2hooPnz55e+v/7662njxo3U1tZGzc3NdOONN1J3d/fcU7ooiqIoijIukx5e/Y477iDf92ndunWUzWZp7dq1dOedd1ZeUaFI5LA/lkW5mVwrsYO55EiuNsAm67l8J1yh0KsNwevy3cAy3v6YlIVyOasHsjGvob702fS9KcqK/8X68SRAhosjaFhYYw+y45q8tZl6SSmZ5eGQTS46Iyb6kTx6uIdtga8K9O7SznPsRsg/wG4/dqhHlAwyG2kDyjmZTwDKFFFSGAXOihzMy6xLGkncBi/7dnrSBggcCuT1fivWWPr8RqxJlA3E7VzAbLDo88G3xyBTLPcXQFks9/NIgK9IHfOrqIvJubAoadMAzIvLUP8oi+U0+DKdAJfTNvkotY0OBZ4UWYRlGfqARNeB4dVdEyW6ztFA+vjk2bxx2eRd/UR/I4I5zetFOa/oC5xTGzvw6uUXyiZYpuBkXI798WE7F31f9uW1oXliO1Nv51gc5tR/ZqwM9yepY6LsfXWvlT4H8Mz0mOz/kVd+JMou71pl+5aOTm3gDCsw0b78WeD63wLPDP4MDfl48OvGj6vgf/YJv3w8/fTTYjudTtOWLVtoy5YtJ1q1oiiKoignIZrbRVEURVGUmjI7stpyXGaHCpaUquZETDSTTSWyX9yXmzNcY4p1MhON58ll+OCIjeXyr69sE2XXffRztsrGelFGx+XSL48IiMuLwgyDEV7ZORqU0zqWd7nc8OKlK0XZ4wf2iG0uxV27+FzZPpPDodT20UO2niLJZVnMpCv75pJQsv1giToc4NVeRzQl8ZEahcvdxuSkmIG0npkh5rOonUREORZFdCioE2WYkbW/aCO8DhflnOImGx6ZlChsvuHwaKRNMTneC+LW7IJmFjSfcFMLRn/lppUUypDZOeL5lguaVjgT/WpMsPmP0VB5Vmc0++TZvkUTPU/QtFI0438eF3YsCkj5/MuDXHqeb80SXgpMrpBhm5PL2Xni+3IOD/vS1IHRcDmuCLdcon3n/u2i7MazLi19vuqMj4iyJw49U/p8ybtklmzxXHaZWZBq/+/UOKuurnwoiqIoilJT9OVDURRFUZSaoi8fiqIoiqLUlNnn84G2L5d9q9yyE/AV8coN4V5hvZH7CVnTBKHfOWjPc0mN2bbJSpki92soHpVy2v++zyYP3D52ijwuy7JVtkifj1hGtmGO9dvPI9ImH2tkGWDj8nzLDu0L+611ymclxSiJGRGZYvSxly+2PiCPHJZ5kbhtPQbXkNvns0b6xmSYrK04gUST+w+gLZ/jyoBKJCWMDczngYcMf7uvdp6gRBWz40qpr5wLeRbe3pUNF+H2+Wbw4+B+HgmQxDb50j9EZGB1po2W8H196GfK4fPArz/68bjA6y8zFct6gpAwe3ywb/wKYzZa7juCeXrRB0T4h8C+I+x6t0E4+8tP/2DpswduYx6TqeK8KGbZmYCadWxM+o5kczzcOYSFZxLe4bysaChvfZXqYb7/zU+eLH3+wn/5HVHGnz1eCkaDPU88zC7uwuW7gf8/2b4YQt2J8Knjx3nOjMeiK+W3piiKoiiKcuLoy4eiKIqiKDVl5ppdYr7NvFqMXuoWVCsxchznubKoTtSmywxSranHdZyLPERc5JJVl2QXTQlMavv//udToujHmcWlz/uzi0SZP2SXuottjaIsmCe3Y6M202TxLRlJMMjaJU0fop+KTLagNTUFe06YuVYsfUL2SGGSITCZYNTaOFuMBvksN8nwDJhvH8jaA4kuz7iL5ppRRzTB8JI9i1wJ/eayTN4ekoYqMyLjrmPZf4L7kktR85BFGE0tHC5vTEM2Xlf0UW52QflsPZhh+HnhvrKfGHGUm9LkcUmHaSXlRT+S8yEjhSXhGCcfyng9QQXPzHKj7eYreAxnQD7NI+y2x6RE20sxUweYCDyWpbs+IeeCmO5gSinkIKtvwtY7nJGmlRHPmmjqkhAegNV7oG6+KMsYe79/f7/Mhrvu3Ux6C8/aULgATrWy2ACfS9H1RGWu/U1hde0zdOVDURRFUZSaoi8fiqIoiqLUlBlndvmtYqFQZB7DU212ceBNVGe1EVddTEVk1ADMLo7ldaF2MaB2Cex1GRqS5z6asXVmcnJZkl/PYkFOO78o++YFts0iKDw8FvXQD0Alw/pqwCRhjG1jEPpdYG3g9ebH4bGFUN/4gbC8yfqD7Uf15e0Do48bcsw9XM53qWG4aGisguVcbnYpQHsZtgyNdY7A3Btl22MwT11mlxjzsjegjAiYacGHBGGJGFOiwLgEoGiZDLNLCD/a7JJ1KGryDpWKK7EcjiCvBxVirginfBRRCcMZq8jsgiZA27fBON6n5SWLLIxItYlI9JaXdQYFGB1WHnr2sLlQLEQ/37JpWTaWYM8ez3FOoWeWbc9zKLtOBJdCEJ+hsnD8ufjb51c5ykPPlK1PrA2HDh2irq6uiXdUFEVRFGXGcfDgQVqyZIlznxn38hEEAR05coSMMbR06VI6ePAgNTc3T3e3ZhSDg4PU1dWlYzMOOjbR6NhEo2MzPjou0ejYhDHG0NDQEHV2dpI/QT6aGWd28X2flixZQoODg0RE1NzcrBc2Ah2baHRsotGxiUbHZnx0XKLRsZG0tLRMvBOpw6miKIqiKDVGXz4URVEURakpM/blI5VK0V/91V9RKpWaeOc5ho5NNDo20ejYRKNjMz46LtHo2JwYM87hVFEURVGUk5sZu/KhKIqiKMrJib58KIqiKIpSU/TlQ1EURVGUmqIvH4qiKIqi1JQZ+/KxZcsWOu200yidTtPq1avpueeem+4u1ZTNmzfTeeedR01NTbRo0SK6+uqrad++fWKfTCZDGzZsoPnz51NjYyOtW7eO+vr6pqnH08ftt99OnufRzTffXPpuLo/N4cOH6ROf+ATNnz+f6urq6KyzzqI9e/aUyo0x9OUvf5lOOeUUqqurozVr1tArr7wyjT2uDcVikb70pS/RsmXLqK6ujt75znfSX//1X4s8FHNlbJ555hm64oorqLOzkzzPo4ceekiUlzMOx44do+uuu46am5uptbWVrr/+ehoeHq7hWUwNrrHJ5/N066230llnnUUNDQ3U2dlJn/zkJ+nIkSOijpN1bCYVMwO5//77TTKZNP/8z/9sfvazn5k/+ZM/Ma2traavr2+6u1Yz1q5da+69917z0ksvmb1795pLL73ULF261AwPD5f2+exnP2u6urrMtm3bzJ49e8z5559vPvCBD0xjr2vPc889Z0477TRz9tlnm5tuuqn0/Vwdm2PHjplTTz3VfOpTnzK7du0yr776qnniiSfMr371q9I+t99+u2lpaTEPPfSQ+clPfmKuvPJKs2zZMjM2NjaNPZ96brvtNjN//nzz6KOPmv3795sHHnjANDY2mm984xulfebK2Dz22GPmi1/8ovn+979viMg8+OCDoryccbj44ovN+973PrNz507zox/9yLzrXe8y1157bY3PZPJxjU1/f79Zs2aN+e53v2tefvlls2PHDrNq1SqzYsUKUcfJOjaTyYx8+Vi1apXZsGFDabtYLJrOzk6zefPmaezV9HL06FFDRGb79u3GmLdvgkQiYR544IHSPr/4xS8MEZkdO3ZMVzdrytDQkDn99NPNk08+aT70oQ+VXj7m8tjceuut5oMf/GBkeRAEpqOjw/zd3/1d6bv+/n6TSqXMv//7v9eii9PGZZddZv74j/9YfHfNNdeY6667zhgzd8cG/8GWMw4///nPDRGZ3bt3l/b5wQ9+YDzPM4cPH65Z36ea8V7MkOeee84QkXnttdeMMXNnbE6UGWd2yeVy1NPTQ2vWrCl95/s+rVmzhnbs2DGNPZteBgYGiIiora2NiIh6enoon8+LcVq+fDktXbp0zozThg0b6LLLLhNjQDS3x+aRRx6hlStX0kc/+lFatGgRnXvuufStb32rVL5//37q7e0VY9PS0kKrV68+6cfmAx/4AG3bto1++ctfEhHRT37yE3r22WfpkksuIaK5PTaccsZhx44d1NraSitXrizts2bNGvJ9n3bt2lXzPk8nAwMD5Hketba2EpGOTbnMuMRyb775JhWLRWpvbxfft7e308svvzxNvZpegiCgm2++mS644AI688wziYiot7eXkslkacL/lvb2durt7Z2GXtaW+++/n55//nnavXt3qGwuj82rr75Kd911F23cuJH+4i/+gnbv3k1/9md/RslkktavX186//Hur5N9bL7whS/Q4OAgLV++nGKxGBWLRbrtttvouuuuIyKa02PDKWccent7adGiRaI8Ho9TW1vbnBqrTCZDt956K1177bWl5HI6NuUx414+lDAbNmygl156iZ599tnp7sqM4ODBg3TTTTfRk08+Sel0erq7M6MIgoBWrlxJf/M3f0NEROeeey699NJLdPfdd9P69eunuXfTy/e+9z36zne+Q/fddx+9973vpb1799LNN99MnZ2dc35slMrJ5/P0h3/4h2SMobvuumu6uzPrmHFmlwULFlAsFgspE/r6+qijo2OaejV93HDDDfToo4/SD3/4Q1qyZEnp+46ODsrlctTf3y/2nwvj1NPTQ0ePHqX3v//9FI/HKR6P0/bt2+mb3/wmxeNxam9vn7Njc8opp9B73vMe8d0ZZ5xBBw4cICIqnf9cvL/+/M//nL7whS/Qxz/+cTrrrLPoj/7oj+iWW26hzZs3E9HcHhtOOePQ0dFBR48eFeWFQoGOHTs2J8bqty8er732Gj355JOlVQ8iHZtymXEvH8lkklasWEHbtm0rfRcEAW3bto26u7unsWe1xRhDN9xwAz344IP01FNP0bJly0T5ihUrKJFIiHHat28fHThw4KQfp4suuohefPFF2rt3b+lv5cqVdN1115U+z9WxueCCC0KS7F/+8pd06qmnEhHRsmXLqKOjQ4zN4OAg7dq166Qfm9HRUfJ9+ciLxWIUBAERze2x4ZQzDt3d3dTf3089PT2lfZ566ikKgoBWr15d8z7Xkt++eLzyyiv0H//xHzR//nxRPpfHpiKm2+N1PO6//36TSqXMt7/9bfPzn//cfOYznzGtra2mt7d3urtWMz73uc+ZlpYW8/TTT5vXX3+99Dc6Olra57Of/axZunSpeeqpp8yePXtMd3e36e7unsZeTx9c7WLM3B2b5557zsTjcXPbbbeZV155xXznO98x9fX15t/+7d9K+9x+++2mtbXVPPzww+anP/2pueqqq05KOSmyfv16s3jx4pLU9vvf/75ZsGCB+fznP1/aZ66MzdDQkHnhhRfMCy+8YIjI/P3f/7154YUXSoqNcsbh4osvNueee67ZtWuXefbZZ83pp59+UshJXWOTy+XMlVdeaZYsWWL27t0rns3ZbLZUx8k6NpPJjHz5MMaYf/iHfzBLly41yWTSrFq1yuzcuXO6u1RTiGjcv3vvvbe0z9jYmPnTP/1TM2/ePFNfX2/+4A/+wLz++uvT1+lpBF8+5vLYbN261Zx55pkmlUqZ5cuXm3/6p38S5UEQmC996Uumvb3dpFIpc9FFF5l9+/ZNU29rx+DgoLnpppvM0qVLTTqdNu94xzvMF7/4RfFPY66MzQ9/+MNxny/r1683xpQ3Dm+99Za59tprTWNjo2lubjaf/vSnzdDQ0DSczeTiGpv9+/dHPpt/+MMfluo4WcdmMvGMYeH9FEVRFEVRppgZ5/OhKIqiKMrJjb58KIqiKIpSU/TlQ1EURVGUmqIvH4qiKIqi1BR9+VAURVEUpaboy4eiKIqiKDVFXz4URVEURakp+vKhKIqiKEpN0ZcPRVEURVFqir58KIqiKIpSU/TlQ1EURVGUmqIvH4qiKIqi1JT/CxoCjq9JSrGOAAAAAElFTkSuQmCC",
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