Chapter 11 - residual network in tensorflow(with transfer learning)

Chapter11/residualNeuralNetwork/withTransferLearning/README.md

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+What it does : 
+
+    1. This Python program predict the category of the image using ResNet50 pretrained model.
+
+Dependancies :
+
+    1. Keras module is needed to be installed in the local machine to run this program. 
+    2. gdown module is needed to be installed in the local machine to run this program. 
+    3. zipfile module is needed to be installed in the local machine to run this program. 
+
+Things to check before running :
+
+    1. Check whether you have given the correct location of your dataset file.
+    2. You should have access to the file in the Google Drive.
+
+
+

Chapter11/residualNeuralNetwork/withTransferLearning/residual_network_with_transfer_learning.ipynb

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+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "residual_network_with_transfer_learning.ipynb",
+      "provenance": [],
+      "collapsed_sections": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Fm15BsTFkiR2"
+      },
+      "source": [
+        "# **Problem: Residual Neural Network in tensorflow. Train on cats and dogs dataset.**\n",
+        "\n",
+        "Python program to train cats and dogs dataset using Residual Neural Network in tensorflow (with transfer learning).\n",
+        "\n",
+        "Run all the cells. After executing the last cell, you will get the predictions.\n",
+        "\n",
+        "**Notes:**\n",
+        "\n",
+        "Following things are needed to be checked before running the program.\n",
+        "  1. Keras module is needed to be installed.\n",
+        "  2. gdown module is needed to be installed.\n",
+        "  3. Check whether you have given the correct location of your dataset file.\n",
+        "  5. You should have access to the file in the Google Drive.\n",
+        "  "
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ypdiISGNBNBX"
+      },
+      "source": [
+        "# **Import Modules**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "rvTOp-uUAqtl"
+      },
+      "source": [
+        "# Import resnet50 pretrained model\n",
+        "from keras.applications.resnet import ResNet50\n",
+        "\n",
+        "# Import image module\n",
+        "from keras.preprocessing import image\n",
+        "\n",
+        "# Import preprocess_input and decode_predictions modules to evaluate the input\n",
+        "from keras.applications.resnet import preprocess_input, decode_predictions\n",
+        "\n",
+        "# Import numpy module\n",
+        "import numpy as np\n",
+        "\n",
+        "# Import gdown module to download files from google drive\n",
+        "import gdown\n"
+      ],
+      "execution_count": 1,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "J9sgnIraBU86"
+      },
+      "source": [
+        "# **Get the file location from google drive and download**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "lkTPUtD5BaBr"
+      },
+      "source": [
+        "# Please change the URL as needed (make sure you have the access to the file)\n",
+        "\n",
+        "url = 'https://drive.google.com/file/d/1-FTayQ4HNMpWg9BSUE17-gbreaSsdtCf/view?usp=sharing'\n",
+        "\n",
+        "# Derive the file id from the URL\n",
+        "file_id = url.split('/')[-2]\n",
+        "\n",
+        "# Derive the download url of the the file\n",
+        "download_url = 'https://drive.google.com/uc?id=' + file_id\n",
+        "\n",
+        "# Give the location you want to save it in your local machine\n",
+        "file_location = 'cats_and_dogs.zip'\n",
+        "\n",
+        "#--------------------------------------------- Download and extract the zip file -----------------------------------------------------------\n",
+        "\n",
+        "# Download the file from drive to your local machine\n",
+        "gdown.download(download_url, file_location, quiet=False)\n"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "KrMC9ovZBdmr"
+      },
+      "source": [
+        "# **Unzip the zip dataset**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "EGCAPKdvBg9x"
+      },
+      "source": [
+        "!unzip /content/cats_and_dogs.zip -d \"/content/unzipped_folder/\""
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ALN2RKuXB1nQ"
+      },
+      "source": [
+        "# **Start the prediction operation**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "1ULa2X0GB27Z"
+      },
+      "source": [
+        "# Use ResNet50 pretrained model with imagenet training\n",
+        "model = ResNet50(weights='imagenet')\n",
+        "\n",
+        "def evaluate(img_fname):\n",
+        "    # Load the image\n",
+        "    img = image.load_img(img_fname, target_size=(224, 224))\n",
+        "\n",
+        "    # Conver image into a Numpy array\n",
+        "    x = image.img_to_array(img)\n",
+        "\n",
+        "    # Expand the array \n",
+        "    x = np.expand_dims(x, axis=0)\n",
+        "\n",
+        "    # Adequate your image to the format the model requires\n",
+        "    x = preprocess_input(x)\n",
+        "\n",
+        "    # Get the predictions\n",
+        "    preds = model.predict(x)\n",
+        "\n",
+        "    # Print the probability and category name for the 5 categories \n",
+        "    # With highest probability: \n",
+        "    print('Predicted:', decode_predictions(preds, top=5)[0])"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "x8d84ch6B6jV"
+      },
+      "source": [
+        "# **Get the predictions for one image**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "ZK9nKtGUB94t"
+      },
+      "source": [
+        "# Give the path of the image you want to predict to the evaluate function as a input parameter\n",
+        "evaluate('/content/unzipped_folder/Cats and Dogs (New)/Cats/Cat_1.jpg')"
+      ],
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}

Chapter11/residualNeuralNetwork/withTransferLearning/residual_network_with_transfer_learning.py

@@ -1,2 +1,86 @@
-# TODO: Create a Residual Neural Network in tensorflow. Train on cats and dogs dataset. (with transfer learning)
-# TODO: Code should be well commented.
+'''Copyright (c) 2021 AIClub
+
+Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated 
+documentation files (the "Software"), to deal in the Software without restriction, including without 
+limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of 
+the Software, and to permit persons to whom the Software is furnished to do so, subject to the following 
+conditions:
+
+The above copyright notice and this permission notice shall be included in all copies or substantial
+portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT 
+LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO 
+EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN 
+AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE 
+OR OTHER DEALINGS IN THE SOFTWARE.'''
+
+# Import resnet50 pretrained model
+from keras.applications.resnet import ResNet50
+
+# Import image module
+from keras.preprocessing import image
+
+# Import preprocess_input and decode_predictions modules to evaluate the input
+from keras.applications.resnet import preprocess_input, decode_predictions
+
+# Import numpy module
+import numpy as np
+
+# Import gdown module to download files from google drive
+import gdown
+
+# Import zip file module to open the zip file
+from zipfile import ZipFile
+
+#--------------------------------------------- Get the file location from google drive  ----------------------------------------------------
+
+# Please change the URL as needed (make sure you have the access to the file)
+
+url = 'https://drive.google.com/file/d/1-FTayQ4HNMpWg9BSUE17-gbreaSsdtCf/view?usp=sharing'
+
+# Derive the file id from the URL
+file_id = url.split('/')[-2]
+
+# Derive the download url of the the file
+download_url = 'https://drive.google.com/uc?id=' + file_id
+
+# Give the location you want to save it in your local machine
+file_location = 'cats_and_dogs.zip'
+
+#--------------------------------------------- Download and extract the zip file -----------------------------------------------------------
+
+# Download the file from drive to your local machine
+gdown.download(download_url, file_location)
+
+# Open the downloaded zip file and extract its contents
+with ZipFile(file_location, "r") as zip_file:
+    filepath = zip_file.extractall()
+
+#--------------------------------------------- Start the prediction operation --------------------------------------------------------------
+
+# Use ResNet50 pretrained model with imagenet training
+model = ResNet50(weights='imagenet')
+
+def evaluate(img_fname):
+    # Load the image
+    img = image.load_img(img_fname, target_size=(224, 224))
+
+    # Conver image into a Numpy array
+    x = image.img_to_array(img)
+
+    # Expand the array 
+    x = np.expand_dims(x, axis=0)
+
+    # Adequate your image to the format the model requires
+    x = preprocess_input(x)
+
+    # Get the predictions
+    preds = model.predict(x)
+
+    # Print the probability and category name for the 5 categories 
+    # With highest probability: 
+    print('Predicted:', decode_predictions(preds, top=5)[0])
+
+# Give the path of the image you want to predict to the evaluate function as a input parameter
+evaluate('Cats and Dogs (New)\Cats\Cat_1.jpg')