{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "V28"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "TPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "!pip install tensorflow"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fYkLMyMqFrfT",
        "outputId": "5691c5a9-2754-48f6-fceb-090b76baf3a2"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Successfully installed astunparse-1.6.3 flatbuffers-25.2.10 google-pasta-0.2.0 libclang-18.1.1 tensorboard-2.19.0 tensorboard-data-server-0.7.2 tensorflow-2.19.0 tensorflow-io-gcs-filesystem-0.37.1 werkzeug-3.1.3 wheel-0.45.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow.keras.applications import MobileNetV2\n",
        "from tensorflow.keras.layers import Input, Lambda, Dense, GlobalAveragePooling2D\n",
        "from tensorflow.keras.models import Model\n",
        "from tensorflow.keras.datasets import mnist\n",
        "from tensorflow.keras.utils import to_categorical\n",
        "import numpy as np\n",
        "\n",
        "# Load MNIST data\n",
        "(train_images, train_labels), (test_images, test_labels) = mnist.load_data()\n",
        "\n",
        "# Preprocess the data: Reshape and scale\n",
        "train_images = np.stack([train_images]*3, axis=-1) / 255.0\n",
        "test_images = np.stack([test_images]*3, axis=-1) / 255.0\n",
        "\n",
        "# Resize images to 32x32 to match the input size of MobileNetV2\n",
        "train_images = tf.image.resize(train_images, [32, 32])\n",
        "test_images = tf.image.resize(test_images, [32, 32])\n",
        "\n",
        "# Convert labels to one-hot encoding\n",
        "train_labels = to_categorical(train_labels, 10)\n",
        "test_labels = to_categorical(test_labels, 10)\n",
        "\n",
        "# Load the MobileNetV2 model, pre-trained on ImageNet\n",
        "base_model = MobileNetV2(weights='imagenet', include_top=False, input_shape=(32, 32, 3))\n",
        "\n",
        "# Freeze the base model\n",
        "base_model.trainable = False\n",
        "\n",
        "# Create new model on top\n",
        "inputs = Input(shape=(32, 32, 3))\n",
        "x = base_model(inputs, training=False)\n",
        "x = GlobalAveragePooling2D()(x)\n",
        "outputs = Dense(10, activation='softmax')(x)\n",
        "model = Model(inputs, outputs)\n",
        "\n",
        "# Compile the model\n",
        "model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])\n",
        "\n",
        "# Train the model\n",
        "model.fit(train_images, train_labels, epochs=10, validation_split=0.2)\n",
        "\n",
        "# Unfreeze some layers of the base model for fine-tuning\n",
        "base_model.trainable = True\n",
        "for layer in base_model.layers[:100]:\n",
        "    layer.trainable = False\n",
        "\n",
        "# Re-compile the model (necessary for the changes to take effect)\n",
        "model.compile(optimizer=tf.keras.optimizers.Adam(1e-5),  # Lower lr for fine-tuning\n",
        "              loss='categorical_crossentropy',\n",
        "              metrics=['accuracy'])\n",
        "\n",
        "# Fine-tune the model\n",
        "model.fit(train_images, train_labels, epochs=5, validation_split=0.2)\n",
        "\n",
        "# Evaluate the model on the test set\n",
        "loss, accuracy = model.evaluate(test_images, test_labels)\n",
        "\n",
        "print(f\"Test loss: {loss}\")\n",
        "print(f\"Test accuracy: {accuracy}\")\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "from sklearn.metrics import confusion_matrix\n",
        "import seaborn as sns\n",
        "\n",
        "# Predict the values from the test dataset\n",
        "test_predictions = model.predict(test_images)\n",
        "test_predictions_classes = np.argmax(test_predictions, axis=1)\n",
        "test_true_classes = np.argmax(test_labels, axis=1)\n",
        "\n",
        "# Compute the confusion matrix\n",
        "cm = confusion_matrix(test_true_classes, test_predictions_classes)\n",
        "\n",
        "# Plotting the confusion matrix\n",
        "plt.figure(figsize=(10, 8))\n",
        "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', cbar=False)\n",
        "plt.xlabel('Predicted Label')\n",
        "plt.ylabel('True Label')\n",
        "plt.title('Confusion Matrix')\n",
        "plt.show()\n",
        "\n",
        "def display_sample(sample_images, sample_labels, sample_predictions):\n",
        "    fig, axes = plt.subplots(3, 3, figsize=(12, 12))\n",
        "    fig.subplots_adjust(hspace=0.5, wspace=0.5)\n",
        "\n",
        "    for i, ax in enumerate(axes.flat):\n",
        "        ax.imshow(sample_images[i].reshape(32, 32), cmap='gray')  # Change to 32x32\n",
        "        ax.set_xlabel(f\"True: {sample_labels[i]}\\nPredicted: {sample_predictions[i]}\")\n",
        "        ax.set_xticks([])\n",
        "        ax.set_yticks([])\n",
        "\n",
        "    plt.show()\n",
        "\n",
        "# Convert RGB to Grayscale for visualization; this assumes the images were resized to 32x32\n",
        "test_images_gray = np.dot(test_images[...,:3], [0.2989, 0.5870, 0.1140])\n",
        "\n",
        "# Selecting a few images from the test set\n",
        "random_indices = np.random.choice(len(test_images_gray), 9, replace=False)\n",
        "sample_images = test_images_gray[random_indices]\n",
        "sample_labels = test_true_classes[random_indices]\n",
        "sample_predictions = test_predictions_classes[random_indices]\n",
        "\n",
        "# Display the selected images and their labels\n",
        "display_sample(sample_images, sample_labels, sample_predictions)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "3b6sO1khE9_a",
        "outputId": "717b83fb-7298-4829-a592-83b88071fbe5"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n",
            "\u001b[1m11490434/11490434\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0us/step\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-5-a67f4e39aa93>:25: UserWarning: `input_shape` is undefined or non-square, or `rows` is not in [96, 128, 160, 192, 224]. Weights for input shape (224, 224) will be loaded as the default.\n",
            "  base_model = MobileNetV2(weights='imagenet', include_top=False, input_shape=(32, 32, 3))\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/mobilenet_v2/mobilenet_v2_weights_tf_dim_ordering_tf_kernels_1.0_224_no_top.h5\n",
            "\u001b[1m9406464/9406464\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0us/step\n",
            "Epoch 1/10\n",
            "\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m42s\u001b[0m 25ms/step - accuracy: 0.4394 - loss: 1.8491 - val_accuracy: 0.5979 - val_loss: 1.3019\n",
            "Epoch 2/10\n",
            "\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m35s\u001b[0m 23ms/step - accuracy: 0.6050 - loss: 1.2659 - val_accuracy: 0.6388 - val_loss: 1.1406\n",
            "Epoch 3/10\n",
            "\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m35s\u001b[0m 23ms/step - accuracy: 0.6389 - loss: 1.1300 - val_accuracy: 0.6528 - val_loss: 1.0739\n",
            "Epoch 4/10\n",
            "\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m35s\u001b[0m 23ms/step - accuracy: 0.6448 - loss: 1.0845 - val_accuracy: 0.6605 - val_loss: 1.0375\n",
            "Epoch 5/10\n",
            "\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m35s\u001b[0m 23ms/step - accuracy: 0.6542 - loss: 1.0477 - val_accuracy: 0.6642 - val_loss: 1.0150\n",
            "Epoch 6/10\n",
            "\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m35s\u001b[0m 23ms/step - accuracy: 0.6594 - loss: 1.0212 - val_accuracy: 0.6674 - val_loss: 1.0002\n",
            "Epoch 7/10\n",
            "\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m35s\u001b[0m 23ms/step - accuracy: 0.6589 - loss: 1.0203 - val_accuracy: 0.6687 - val_loss: 0.9903\n",
            "Epoch 8/10\n",
            "\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m35s\u001b[0m 23ms/step - accuracy: 0.6642 - loss: 0.9998 - val_accuracy: 0.6719 - val_loss: 0.9825\n",
            "Epoch 9/10\n",
            "\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m35s\u001b[0m 23ms/step - accuracy: 0.6716 - loss: 0.9859 - val_accuracy: 0.6718 - val_loss: 0.9771\n",
            "Epoch 10/10\n",
            "\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m35s\u001b[0m 23ms/step - accuracy: 0.6671 - loss: 0.9842 - val_accuracy: 0.6732 - val_loss: 0.9721\n",
            "Epoch 1/5\n",
            "\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m113s\u001b[0m 69ms/step - accuracy: 0.2328 - loss: 10.6508 - val_accuracy: 0.1437 - val_loss: 10.7704\n",
            "Epoch 2/5\n",
            "\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m100s\u001b[0m 67ms/step - accuracy: 0.4827 - loss: 2.4533 - val_accuracy: 0.2733 - val_loss: 2.3262\n",
            "Epoch 3/5\n",
            "\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m100s\u001b[0m 67ms/step - accuracy: 0.6174 - loss: 1.4362 - val_accuracy: 0.6418 - val_loss: 1.1562\n",
            "Epoch 4/5\n",
            "\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m100s\u001b[0m 67ms/step - accuracy: 0.7038 - loss: 1.0777 - val_accuracy: 0.8116 - val_loss: 0.6566\n",
            "Epoch 5/5\n",
            "\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m101s\u001b[0m 67ms/step - accuracy: 0.7732 - loss: 0.7957 - val_accuracy: 0.8488 - val_loss: 0.5217\n",
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 19ms/step - accuracy: 0.8246 - loss: 0.5952\n",
            "Test loss: 0.5533193349838257\n",
            "Test accuracy: 0.8414000272750854\n",
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 21ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x1200 with 9 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "7mXagMOUE-bw"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}