projectkeempat_rere.py

# -*- coding: utf-8 -*-
"""projectkeempat_rere.ipynb

Automatically generated by Colaboratory.

Original file is located at
    https://colab.research.google.com/drive/1rI_mgNeuYPNlDkMt2zi9lZl4gNyPihLX
"""

# install kaggle package
!pip install -q kaggle

# upload kaggle.json
from google.colab import files
files.upload()

!mkdir -p ~/.kaggle
!cp kaggle.json ~/.kaggle/
!chmod 600 ~/.kaggle/kaggle.json
!ls ~/.kaggle

!kaggle datasets download -d alessiocorrado99/animals10

# unzip
!mkdir animals
!unzip -qq animals10.zip -d animals
!ls animals

!ls animals/animals10/raw-img/

import os

animals = os.path.join('/content/animals/animals10/raw-img/')

print(os.listdir(animals))

import shutil

ignore_animals = ['squirrel', 'cat', 'butterfly', 'elephant', 'sheep', 'cow', ]

for x in ignore_animals:
  path = os.path.join(animals, x)
  shutil.rmtree(path)

list_animals = os.listdir(animals)
print(list_animals)

from PIL import Image
total = 0

for x in list_animals:
  dir = os.path.join(animals, x)
  y = len(os.listdir(dir))
  print(x+':', y)
  total = total + y
  
  img_name = os.listdir(dir)
  for z in range(4):
    img_path = os.path.join(dir, img_name[z])
    img = Image.open(img_path)
    print('-',img.size)
  print('---------------')

print('\nTotal :', total)

import numpy as np
import matplotlib.pyplot as plt

fig, ax = plt.subplots(2, 2, figsize=(15,15))
fig.suptitle("Randomly displays images.", fontsize=24)
animals_sorted = sorted(list_animals)
animals_id = 0
for i in range(2):
  for j in range(2):
    try:
      animals_selected = animals_sorted[animals_id] 
      animals_id += 1
    except:
      break
    if animals_selected == '.TEMP':
        continue
    animals_selected_images = os.listdir(os.path.join(animals, animals_selected))
    animals_selected_random = np.random.choice(animals_selected_images)
    img = plt.imread(os.path.join(animals, animals_selected, animals_selected_random))
    ax[i][j].imshow(img)
    ax[i][j].set_title(animals_selected, pad=10, fontsize=22)
    
plt.setp(ax, xticks=[],yticks=[])
plt.show

from tensorflow.keras.preprocessing.image import ImageDataGenerator
 
train_datagen = ImageDataGenerator(
    rotation_range=40,
    width_shift_range=0.2,
    height_shift_range=0.2,
    rescale=1/255,
    shear_range=0.2,
    zoom_range=0.2,
    horizontal_flip=True,
    fill_mode='nearest',
    validation_split=0.2   
)

batch_size = 256

data_train = train_datagen.flow_from_directory(
    animals,
    target_size=(150, 150),
    batch_size=batch_size,
    class_mode='categorical',
    subset='training')

data_val = train_datagen.flow_from_directory(
    animals, 
    target_size=(150, 150),
    batch_size=batch_size,
    class_mode='categorical',
    subset='validation')

import tensorflow as tf
# from tensorflow.keras import applications, optimizers

tf.device('/device:GPU:0')

model = tf.keras.models.Sequential([
    tf.keras.layers.Conv2D(64, (3,3), activation='relu', input_shape=(150, 150, 3)),
    tf.keras.layers.MaxPooling2D(2, 2),
    tf.keras.layers.Conv2D(64, (3,3), activation='relu'),
    tf.keras.layers.MaxPooling2D(2,2),
    tf.keras.layers.Conv2D(128, (3,3), activation='relu'),
    tf.keras.layers.MaxPooling2D(2,2),
    tf.keras.layers.Conv2D(128, (3,3), activation='relu'),
    tf.keras.layers.MaxPooling2D(2,2),
    tf.keras.layers.Flatten(),
    tf.keras.layers.Dropout(0.5), 
    tf.keras.layers.Dense(512, activation='relu'),
    tf.keras.layers.Dense(4, activation='softmax')
])

model.compile(optimizer='adam',
              loss='categorical_crossentropy',
              metrics = ['accuracy'])

model.summary()

class myCallback(tf.keras.callbacks.Callback):
  def on_epoch_end(self, epoch, logs={}):
    if(logs.get('accuracy')>0.92 and logs.get('val_accuracy')>0.92):
      print("\nAccuracy above 92%, finish training!")
      self.model.stop_training = True

callbacks = myCallback()

history = model.fit(data_train, 
                    epochs = 64, 
                    steps_per_epoch = data_train.samples // batch_size,
                    validation_data = data_val, 
                    validation_steps = data_val.samples // batch_size,
                    verbose = 2,
                    callbacks = [callbacks])

plt.plot(history.history['accuracy'])
plt.plot(history.history['val_accuracy'])
plt.title('Accuracy')
plt.ylabel('accuracy')
plt.xlabel('epoch')
plt.legend(['train', 'val'], loc='upper left')
plt.show()

plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.title('Loss')
plt.ylabel('loss')
plt.xlabel('epoch')
plt.legend(['train', 'val'], loc='upper left')
plt.show()

converter = tf.lite.TFLiteConverter.from_keras_model(model)
tflite_model = converter.convert()

with tf.io.gfile.GFile('model.tflite', 'wb') as f:
  f.write(tflite_model)

!ls -la | grep 'model'