C3_W1_Lab_2_Transfer_Learning_CIFAR_10.ipynb

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  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "provenance": [],
      "machine_shape": "hm",
      "gpuType": "T4",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
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        "name": "ipython",
        "version": 3
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      "file_extension": ".py",
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      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.7.4"
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    "gpuClass": "standard"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Khaled-Mohammed-Abdelgaber/advanced-computer-vision-with-tensorflow/blob/main/C3_W1_Lab_2_Transfer_Learning_CIFAR_10.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FStp_vbUkRz5"
      },
      "source": [
        "\n",
        "\n",
        "\n",
        "# Transfer Learning\n",
        "In this notebook, you will perform transfer learning to train CIFAR-10 dataset on ResNet50 model available in Keras.\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qpiJj8ym0v0-"
      },
      "source": [
        "## Imports"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AoilhmYe1b5t",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2902996f-7acb-4e3e-f4d7-c14beafe4824"
      },
      "source": [
        "import os, re, time, json\n",
        "import PIL.Image, PIL.ImageFont, PIL.ImageDraw\n",
        "import numpy as np\n",
        "try:\n",
        "  # %tensorflow_version only exists in Colab.\n",
        "  %tensorflow_version 2.x\n",
        "except Exception:\n",
        "  pass\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras.applications.resnet50 import ResNet50\n",
        "from matplotlib import pyplot as plt\n",
        "import tensorflow_datasets as tfds\n",
        "\n",
        "print(\"Tensorflow version \" + tf.__version__)"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Colab only includes TensorFlow 2.x; %tensorflow_version has no effect.\n",
            "Tensorflow version 2.12.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HuG_q_1jkaZ6"
      },
      "source": [
        "## Parameters"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "v4ocPhg6J_xw"
      },
      "source": [
        "- Define the batch size\n",
        "- Define the class (category) names"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cCpkS9C_H7Tl"
      },
      "source": [
        "BATCH_SIZE = 32 \n",
        "classes = ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O-o96NnyJ_xx"
      },
      "source": [
        "Define some functions that will help you to create some visualizations. (These will be used later)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CfFqJxrzoj5Q"
      },
      "source": [
        "#@title Visualization Utilities[RUN ME]\n",
        "#Matplotlib config\n",
        "plt.rc('image', cmap='gray')\n",
        "plt.rc('grid', linewidth=0)\n",
        "plt.rc('xtick', top=False, bottom=False, labelsize='large')\n",
        "plt.rc('ytick', left=False, right=False, labelsize='large')\n",
        "plt.rc('axes', facecolor='F8F8F8', titlesize=\"large\", edgecolor='white')\n",
        "plt.rc('text', color='a8151a')\n",
        "plt.rc('figure', facecolor='F0F0F0')# Matplotlib fonts\n",
        "MATPLOTLIB_FONT_DIR = os.path.join(os.path.dirname(plt.__file__), \"mpl-data/fonts/ttf\")\n",
        "# utility to display a row of digits with their predictions\n",
        "def display_images(digits, predictions, labels, title):\n",
        "\n",
        "  n = 10\n",
        "\n",
        "  indexes = np.random.choice(len(predictions), size=n) #will choose random 10 indexes from our data\n",
        "  n_digits = digits[indexes]#store them in n_digits\n",
        "  n_predictions = predictions[indexes] #store corresponding predictions\n",
        "  n_predictions = n_predictions.reshape((n,))#make prediction 1D \n",
        "  n_labels = labels[indexes] #store corresponding actual labels\n",
        " \n",
        "  fig = plt.figure(figsize=(20, 4))\n",
        "  plt.title(title)\n",
        "  plt.yticks([])\n",
        "  plt.xticks([])\n",
        "  #plot them\n",
        "  for i in range(10):\n",
        "    ax = fig.add_subplot(1, 10, i+1)\n",
        "    class_index = n_predictions[i]\n",
        "    \n",
        "    plt.xlabel(classes[class_index])\n",
        "    plt.xticks([])\n",
        "    plt.yticks([])\n",
        "    plt.imshow(n_digits[i])\n",
        "\n",
        "# utility to display training and validation curves\n",
        "def plot_metrics(metric_name, title, ylim=5):\n",
        "  plt.title(title)\n",
        "  plt.ylim(0,ylim)\n",
        "  plt.plot(history.history[metric_name],color='blue',label=metric_name)\n",
        "  plt.plot(history.history['val_' + metric_name],color='green',label='val_' + metric_name)"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wPq4Sw5akosT"
      },
      "source": [
        "## Loading and Preprocessing Data\n",
        "[CIFAR-10](https://www.cs.toronto.edu/~kriz/cifar.html) dataset has 32 x 32 RGB images belonging to 10 classes. You will load the dataset from Keras."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E103YDdQ8NNq",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4a603640-b46c-489d-a14a-e69f3c055f5d"
      },
      "source": [
        "(training_images, training_labels) , (validation_images, validation_labels) = tf.keras.datasets.cifar10.load_data()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz\n",
            "170498071/170498071 [==============================] - 2s 0us/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "prd944ThNavt"
      },
      "source": [
        "### Visualize Dataset\n",
        "\n",
        "Use the `display_image` to view some of the images and their class labels."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UiokWTuKo88c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 294
        },
        "outputId": "0b2a5139-d38c-4d85-f8fa-bd5796102634"
      },
      "source": [
        "display_images(training_images, training_labels, training_labels, \"Training Data\" )"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x400 with 11 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-q35q41KNfxH",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 294
        },
        "outputId": "82ba5eb4-4dc9-4799-8331-043131d95b28"
      },
      "source": [
        "display_images(validation_images, validation_labels, validation_labels, \"Training Data\" )"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x400 with 11 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "validation_images[0].astype('float32').shape"
      ],
      "metadata": {
        "id": "6iIw-0ousl1C",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4b49a34d-cb41-4b43-fb2c-e4ec65a28565"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(32, 32, 3)"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ltKfwrCVNuIu"
      },
      "source": [
        "### Preprocess Dataset\n",
        "Here, you'll perform normalization on images in training and validation set. \n",
        "- You'll use the function [preprocess_input](https://github.com/keras-team/keras-applications/blob/master/keras_applications/resnet50.py) from the ResNet50 model in Keras."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JIxdiJVKArC6"
      },
      "source": [
        "#The images are converted from RGB to BGR, then each color channel is zero-centered with respect to the ImageNet dataset, without scaling.\n",
        "def preprocess_image_input(input_images):\n",
        "  input_images = input_images.astype('float32')\n",
        "  output_ims = tf.keras.applications.resnet50.preprocess_input(input_images)  #process_input take image with datatype float32\n",
        "  return output_ims\n"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QOqjKzgAEU-Z"
      },
      "source": [
        "train_X = preprocess_image_input(training_images)\n",
        "valid_X = preprocess_image_input(validation_images)"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2fooPL9Gkuox"
      },
      "source": [
        "## Define the Network\n",
        "You will be performing transfer learning on **ResNet50** available in Keras.\n",
        "- You'll load pre-trained **imagenet weights** to the model.\n",
        "- You'll choose to retain all layers of **ResNet50** along with the final classification layers."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "56y8UNFQIVwj",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "087fafab-8ea6-4339-d6c8-38a1c3f36aab"
      },
      "source": [
        "'''\n",
        "Feature Extraction is performed by ResNet50 pretrained on imagenet weights. \n",
        "Input size is 224 x 224.\n",
        "'''\n",
        "def feature_extractor(inputs):\n",
        "  #instantiat resnet model except the top classifier, weights is imagenet weights [after model trained on imagenet dataset]\n",
        "  feature_extractor = tf.keras.applications.resnet.ResNet50(input_shape=(224, 224, 3),\n",
        "                                                            include_top=False,\n",
        "                                                            weights='imagenet')(inputs) \n",
        "  return feature_extractor\n",
        "\n",
        "\n",
        "'''\n",
        "Defines final dense layers and subsequent softmax layer for classification.\n",
        "ours new classifier\n",
        "'''\n",
        "def classifier(inputs):\n",
        "    x = tf.keras.layers.GlobalAveragePooling2D()(inputs)\n",
        "    x = tf.keras.layers.Flatten()(x)\n",
        "    x = tf.keras.layers.Dense(1024, activation=\"relu\")(x)\n",
        "    x = tf.keras.layers.Dense(512, activation=\"relu\")(x)\n",
        "    x = tf.keras.layers.Dense(10, activation=\"softmax\", name=\"classification\")(x)\n",
        "    return x\n",
        "\n",
        "'''\n",
        "Since input image size is (32 x 32), first upsample the image by factor of (7x7) to transform it to (224 x 224)\n",
        "Connect the feature extraction and \"classifier\" layers to build the model.\n",
        "'''\n",
        "def final_model(inputs):\n",
        "    # we will resize the image to 224 x 224 by using upsampling layer with 7x7 upsampling\n",
        "    resize = tf.keras.layers.UpSampling2D(size=(7,7))(inputs)\n",
        "\n",
        "    resnet_feature_extractor = feature_extractor(resize)\n",
        "    classification_output = classifier(resnet_feature_extractor)\n",
        "\n",
        "    return classification_output\n",
        "\n",
        "'''\n",
        "Define the model and compile it. \n",
        "Use Stochastic Gradient Descent as the optimizer.\n",
        "Use Sparse Categorical CrossEntropy as the loss function.\n",
        "'''\n",
        "def define_compile_model():\n",
        "  inputs = tf.keras.layers.Input(shape=(32,32,3))\n",
        "  \n",
        "  classification_output = final_model(inputs) \n",
        "  model = tf.keras.Model(inputs=inputs, outputs = classification_output)\n",
        " \n",
        "  model.compile(optimizer='SGD', \n",
        "                loss='sparse_categorical_crossentropy',\n",
        "                metrics = ['accuracy'])\n",
        "  \n",
        "  return model\n",
        "\n",
        "\n",
        "model = define_compile_model()\n",
        "\n",
        "model.summary()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/resnet/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
            "94765736/94765736 [==============================] - 0s 0us/step\n",
            "Model: \"model\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_1 (InputLayer)        [(None, 32, 32, 3)]       0         \n",
            "                                                                 \n",
            " up_sampling2d (UpSampling2D  (None, 224, 224, 3)      0         \n",
            " )                                                               \n",
            "                                                                 \n",
            " resnet50 (Functional)       (None, 7, 7, 2048)        23587712  \n",
            "                                                                 \n",
            " global_average_pooling2d (G  (None, 2048)             0         \n",
            " lobalAveragePooling2D)                                          \n",
            "                                                                 \n",
            " flatten (Flatten)           (None, 2048)              0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 1024)              2098176   \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 512)               524800    \n",
            "                                                                 \n",
            " classification (Dense)      (None, 10)                5130      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 26,215,818\n",
            "Trainable params: 26,162,698\n",
            "Non-trainable params: 53,120\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CuhDh8ao8VyB"
      },
      "source": [
        "## Train the model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2K6RNDqtJ_xx",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ca87e9ff-8428-4a75-ca6a-636a747fa14f"
      },
      "source": [
        "# this will take around 20 minutes to complete\n",
        "EPOCHS = 2\n",
        "history = model.fit(train_X, training_labels, epochs=EPOCHS, validation_data = (valid_X, validation_labels), batch_size=64)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/2\n",
            "782/782 [==============================] - 562s 680ms/step - loss: 0.3933 - accuracy: 0.8717 - val_loss: 0.2115 - val_accuracy: 0.9271\n",
            "Epoch 2/2\n",
            "782/782 [==============================] - 530s 678ms/step - loss: 0.0995 - accuracy: 0.9671 - val_loss: 0.1750 - val_accuracy: 0.9413\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CYb5sAEmk4ut"
      },
      "source": [
        "## Evaluate the Model\n",
        "\n",
        "Calculate the loss and accuracy metrics using the model's `.evaluate` function."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "io7Fuu-w3PZi",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "69bdedf1-b498-4e9b-be11-f37858c1a7b3"
      },
      "source": [
        "loss, accuracy = model.evaluate(valid_X, validation_labels, batch_size=64)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "157/157 [==============================] - 28s 175ms/step - loss: 0.1750 - accuracy: 0.9413\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yml-phRfPeOj"
      },
      "source": [
        "### Plot Loss and Accuracy Curves\n",
        "\n",
        "Plot the loss (in blue) and validation loss (in green)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b1ZMMJ6T921A",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 456
        },
        "outputId": "541a2df6-5b7e-42d4-a386-6356fa0b23df"
      },
      "source": [
        "plot_metrics(\"loss\", \"Loss\")"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QbnWIbeJJ_xx"
      },
      "source": [
        "Plot the training accuracy (blue) as well as the validation accuracy (green)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P0YpFs3J99eO",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 456
        },
        "outputId": "cbb097ab-5078-46ca-aac7-ae0b7573bcfb"
      },
      "source": [
        "plot_metrics(\"accuracy\", \"Accuracy\")"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9jFVovcUUVs1"
      },
      "source": [
        "### Visualize predictions\n",
        "You can take a look at the predictions on the validation set."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NIQAqkMV9adq",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        },
        "outputId": "8ea3392c-e871-4d27-c4db-e2ac327ccf6c"
      },
      "source": [
        "probabilities = model.predict(valid_X, batch_size=64)\n",
        "probabilities = np.argmax(probabilities, axis = 1)\n",
        "\n",
        "display_images(validation_images, probabilities, validation_labels, \"Bad predictions indicated in red.\")"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "157/157 [==============================] - 28s 172ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x400 with 11 Axes>"
            ],
            "image/png": 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    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "C-noZC5rLTi9"
      },
      "execution_count": 15,
      "outputs": []
    }
  ]
}