{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "EWPF-SdZDxjK",
        "outputId": "ceb497ce-cff0-45f6-be99-84c3cd28ec0c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz\n",
            "\u001b[1m170498071/170498071\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 0us/step\n",
            "Training data shape: (50000, 32, 32, 3)\n",
            "Training labels shape: (50000, 1)\n",
            "Test data shape: (10000, 32, 32, 3)\n",
            "Test labels shape: (10000, 1)\n",
            "CIFAR-10 Classes: ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/tmp/ipython-input-813738170.py:32: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
            "  plt.xlabel(class_names[int(y_train[i])])\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x400 with 10 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# ===============================================================\n",
        "# ACCA7025 - Image Classification using Artificial Neural Networks\n",
        "# Task: CIFAR-10 Dataset Image Classifier using CNN\n",
        "# ===============================================================\n",
        "\n",
        "# Import necessary libraries\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras import datasets, layers, models\n",
        "\n",
        "# Load CIFAR-10 dataset\n",
        "(x_train, y_train), (x_test, y_test) = datasets.cifar10.load_data()\n",
        "\n",
        "# Display dataset shapes\n",
        "print(\"Training data shape:\", x_train.shape)\n",
        "print(\"Training labels shape:\", y_train.shape)\n",
        "print(\"Test data shape:\", x_test.shape)\n",
        "print(\"Test labels shape:\", y_test.shape)\n",
        "\n",
        "# Display class names for CIFAR-10\n",
        "class_names = ['airplane', 'automobile', 'bird', 'cat', 'deer',\n",
        "               'dog', 'frog', 'horse', 'ship', 'truck']\n",
        "print(\"CIFAR-10 Classes:\", class_names)\n",
        "\n",
        "# Show a sample of training images\n",
        "plt.figure(figsize=(8, 4))\n",
        "for i in range(10):\n",
        "    plt.subplot(2, 5, i+1)\n",
        "    plt.xticks([]); plt.yticks([])\n",
        "    plt.imshow(x_train[i])\n",
        "    plt.xlabel(class_names[int(y_train[i])])\n",
        "plt.suptitle(\"Sample CIFAR-10 Training Images\")\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# ===============================================================\n",
        "# Data Preprocessing and Normalization\n",
        "# ===============================================================\n",
        "\n",
        "# Normalize pixel values to range [0, 1]\n",
        "x_train = x_train.astype('float32') / 255.0\n",
        "x_test = x_test.astype('float32') / 255.0\n",
        "\n",
        "# Convert class labels to one-hot encoded format\n",
        "y_train = tf.keras.utils.to_categorical(y_train, 10)\n",
        "y_test = tf.keras.utils.to_categorical(y_test, 10)\n",
        "\n",
        "# Verify shapes after preprocessing\n",
        "print(\"x_train normalized shape:\", x_train.shape)\n",
        "print(\"x_test normalized shape:\", x_test.shape)\n",
        "print(\"y_train one-hot shape:\", y_train.shape)\n",
        "print(\"y_test one-hot shape:\", y_test.shape)\n",
        "\n",
        "# Display an example image after normalization\n",
        "plt.figure(figsize=(2,2))\n",
        "plt.imshow(x_train[0])\n",
        "plt.title(\"Normalized Image Example\")\n",
        "plt.axis('off')\n",
        "plt.show()\n",
        "\n",
        "# Display label vector for confirmation\n",
        "print(\"One-hot encoded label for sample image:\", y_train[0])\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "oJB9-ZlQD0UD",
        "outputId": "aa06ff7a-71ce-4d81-e3fb-97d5b8969a04"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "x_train normalized shape: (50000, 32, 32, 3)\n",
            "x_test normalized shape: (10000, 32, 32, 3)\n",
            "y_train one-hot shape: (50000, 10)\n",
            "y_test one-hot shape: (10000, 10)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 200x200 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "One-hot encoded label for sample image: [0. 0. 0. 0. 0. 0. 1. 0. 0. 0.]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# ===============================================================\n",
        "# CNN Model Architecture Definition\n",
        "# ===============================================================\n",
        "\n",
        "model = models.Sequential([\n",
        "    # Convolutional block 1\n",
        "    layers.Conv2D(32, (3, 3), activation='relu', input_shape=(32, 32, 3)),\n",
        "    layers.MaxPooling2D((2, 2)),\n",
        "\n",
        "    # Convolutional block 2\n",
        "    layers.Conv2D(64, (3, 3), activation='relu'),\n",
        "    layers.MaxPooling2D((2, 2)),\n",
        "\n",
        "    # Convolutional block 3\n",
        "    layers.Conv2D(128, (3, 3), activation='relu'),\n",
        "    layers.MaxPooling2D((2, 2)),\n",
        "\n",
        "    # Flatten + Dense layers\n",
        "    layers.Flatten(),\n",
        "    layers.Dense(128, activation='relu'),\n",
        "    layers.Dropout(0.5),\n",
        "\n",
        "    # Output layer for 10 classes\n",
        "    layers.Dense(10, activation='softmax')\n",
        "])\n",
        "\n",
        "# Display the model summary\n",
        "model.summary()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 504
        },
        "id": "KcMlX5CZEF1j",
        "outputId": "927a59c1-5a8e-4bd3-9f3b-4e7780f1ab22"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.12/dist-packages/keras/src/layers/convolutional/base_conv.py:113: 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__(activity_regularizer=activity_regularizer, **kwargs)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1mModel: \"sequential\"\u001b[0m\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential\"</span>\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "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",
              "│ conv2d (\u001b[38;5;33mConv2D\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m30\u001b[0m, \u001b[38;5;34m30\u001b[0m, \u001b[38;5;34m32\u001b[0m)     │           \u001b[38;5;34m896\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ max_pooling2d (\u001b[38;5;33mMaxPooling2D\u001b[0m)    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m15\u001b[0m, \u001b[38;5;34m15\u001b[0m, \u001b[38;5;34m32\u001b[0m)     │             \u001b[38;5;34m0\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv2d_1 (\u001b[38;5;33mConv2D\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m13\u001b[0m, \u001b[38;5;34m13\u001b[0m, \u001b[38;5;34m64\u001b[0m)     │        \u001b[38;5;34m18,496\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ max_pooling2d_1 (\u001b[38;5;33mMaxPooling2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m64\u001b[0m)       │             \u001b[38;5;34m0\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv2d_2 (\u001b[38;5;33mConv2D\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m128\u001b[0m)      │        \u001b[38;5;34m73,856\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ max_pooling2d_2 (\u001b[38;5;33mMaxPooling2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m128\u001b[0m)      │             \u001b[38;5;34m0\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ flatten (\u001b[38;5;33mFlatten\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m512\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;34m128\u001b[0m)            │        \u001b[38;5;34m65,664\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dropout (\u001b[38;5;33mDropout\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\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;34m10\u001b[0m)             │         \u001b[38;5;34m1,290\u001b[0m │\n",
              "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
              "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
              "│ conv2d (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">30</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">30</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)     │           <span style=\"color: #00af00; text-decoration-color: #00af00\">896</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ max_pooling2d (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">15</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">15</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)     │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv2d_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">13</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">13</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)     │        <span style=\"color: #00af00; text-decoration-color: #00af00\">18,496</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ max_pooling2d_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)       │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv2d_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)      │        <span style=\"color: #00af00; text-decoration-color: #00af00\">73,856</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ max_pooling2d_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)      │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ flatten (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)            │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)            │        <span style=\"color: #00af00; text-decoration-color: #00af00\">65,664</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dropout (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)            │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dense_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>)             │         <span style=\"color: #00af00; text-decoration-color: #00af00\">1,290</span> │\n",
              "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m160,202\u001b[0m (625.79 KB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">160,202</span> (625.79 KB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m160,202\u001b[0m (625.79 KB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">160,202</span> (625.79 KB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# ===============================================================\n",
        "# Model Compilation\n",
        "# ===============================================================\n",
        "\n",
        "# Compile the model\n",
        "model.compile(\n",
        "    optimizer='adam',\n",
        "    loss='categorical_crossentropy',\n",
        "    metrics=['accuracy']\n",
        ")\n",
        "\n",
        "# Display confirmation of compilation\n",
        "print(\"Model compiled successfully with Adam optimizer and categorical crossentropy loss.\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "enWV2_KyEPi_",
        "outputId": "ebd1cd9c-d0ad-4f29-ce4e-155b1bdb0c3d"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model compiled successfully with Adam optimizer and categorical crossentropy loss.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# ===============================================================\n",
        "# Model Training\n",
        "# ===============================================================\n",
        "\n",
        "# Define training parameters\n",
        "EPOCHS = 15\n",
        "BATCH_SIZE = 64\n",
        "\n",
        "# Train the CNN model\n",
        "history = model.fit(\n",
        "    x_train, y_train,\n",
        "    epochs=EPOCHS,\n",
        "    batch_size=BATCH_SIZE,\n",
        "    validation_data=(x_test, y_test),\n",
        "    verbose=1\n",
        ")\n",
        "\n",
        "# Display final training and validation accuracy\n",
        "final_train_acc = history.history['accuracy'][-1]\n",
        "final_val_acc = history.history['val_accuracy'][-1]\n",
        "print(f\"\\nFinal Training Accuracy: {final_train_acc:.4f}\")\n",
        "print(f\"Final Validation Accuracy: {final_val_acc:.4f}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kklXUc8tEWsG",
        "outputId": "83ff4105-3b6a-4f65-b6ed-cd6e3e9c2c65"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/15\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 9ms/step - accuracy: 0.2644 - loss: 1.9591 - val_accuracy: 0.4985 - val_loss: 1.3744\n",
            "Epoch 2/15\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 5ms/step - accuracy: 0.4955 - loss: 1.3864 - val_accuracy: 0.5860 - val_loss: 1.1635\n",
            "Epoch 3/15\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - accuracy: 0.5676 - loss: 1.2109 - val_accuracy: 0.6086 - val_loss: 1.0915\n",
            "Epoch 4/15\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - accuracy: 0.6106 - loss: 1.0965 - val_accuracy: 0.6373 - val_loss: 1.0202\n",
            "Epoch 5/15\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - accuracy: 0.6448 - loss: 1.0156 - val_accuracy: 0.6642 - val_loss: 0.9657\n",
            "Epoch 6/15\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 5ms/step - accuracy: 0.6686 - loss: 0.9511 - val_accuracy: 0.6664 - val_loss: 0.9546\n",
            "Epoch 7/15\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - accuracy: 0.6910 - loss: 0.8908 - val_accuracy: 0.6896 - val_loss: 0.8920\n",
            "Epoch 8/15\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - accuracy: 0.7039 - loss: 0.8523 - val_accuracy: 0.7005 - val_loss: 0.8586\n",
            "Epoch 9/15\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 5ms/step - accuracy: 0.7165 - loss: 0.8165 - val_accuracy: 0.7076 - val_loss: 0.8501\n",
            "Epoch 10/15\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - accuracy: 0.7327 - loss: 0.7722 - val_accuracy: 0.6938 - val_loss: 0.8984\n",
            "Epoch 11/15\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - accuracy: 0.7417 - loss: 0.7473 - val_accuracy: 0.7236 - val_loss: 0.8007\n",
            "Epoch 12/15\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - accuracy: 0.7551 - loss: 0.7115 - val_accuracy: 0.7159 - val_loss: 0.8244\n",
            "Epoch 13/15\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 5ms/step - accuracy: 0.7593 - loss: 0.6887 - val_accuracy: 0.7106 - val_loss: 0.8429\n",
            "Epoch 14/15\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - accuracy: 0.7676 - loss: 0.6645 - val_accuracy: 0.7277 - val_loss: 0.7927\n",
            "Epoch 15/15\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - accuracy: 0.7748 - loss: 0.6459 - val_accuracy: 0.7249 - val_loss: 0.8128\n",
            "\n",
            "Final Training Accuracy: 0.7738\n",
            "Final Validation Accuracy: 0.7249\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# ===============================================================\n",
        "# Visualization of Training and Validation Performance\n",
        "# ===============================================================\n",
        "\n",
        "# Plot training vs validation accuracy\n",
        "plt.figure(figsize=(10,4))\n",
        "plt.subplot(1,2,1)\n",
        "plt.plot(history.history['accuracy'], label='Training Accuracy')\n",
        "plt.plot(history.history['val_accuracy'], label='Validation Accuracy')\n",
        "plt.title('Training and Validation Accuracy')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.legend()\n",
        "\n",
        "# Plot training vs validation loss\n",
        "plt.subplot(1,2,2)\n",
        "plt.plot(history.history['loss'], label='Training Loss')\n",
        "plt.plot(history.history['val_loss'], label='Validation Loss')\n",
        "plt.title('Training and Validation Loss')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Loss')\n",
        "plt.legend()\n",
        "\n",
        "plt.suptitle('Model Training Progress on CIFAR-10')\n",
        "plt.tight_layout()\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 413
        },
        "id": "-zP20623EaKZ",
        "outputId": "902971ec-c1ff-4b4d-9e84-587199dc8bf6"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x400 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# ===============================================================\n",
        "# Model Evaluation and Metrics\n",
        "# ===============================================================\n",
        "\n",
        "from sklearn.metrics import classification_report, confusion_matrix\n",
        "import seaborn as sns\n",
        "\n",
        "# Evaluate the model on the test data\n",
        "test_loss, test_accuracy = model.evaluate(x_test, y_test, verbose=0)\n",
        "print(f\"Test Accuracy: {test_accuracy:.4f}\")\n",
        "print(f\"Test Loss: {test_loss:.4f}\")\n",
        "\n",
        "# Generate predictions\n",
        "y_pred = model.predict(x_test)\n",
        "y_pred_classes = np.argmax(y_pred, axis=1)\n",
        "y_true_classes = np.argmax(y_test, axis=1)\n",
        "\n",
        "# Classification report\n",
        "print(\"\\nClassification Report:\\n\")\n",
        "print(classification_report(y_true_classes, y_pred_classes, target_names=class_names))\n",
        "\n",
        "# Confusion matrix\n",
        "cm = confusion_matrix(y_true_classes, y_pred_classes)\n",
        "\n",
        "# Plot confusion matrix\n",
        "plt.figure(figsize=(8,6))\n",
        "sns.heatmap(cm, annot=False, cmap='Blues')\n",
        "plt.title(\"Confusion Matrix - CIFAR-10 CNN Model\")\n",
        "plt.xlabel(\"Predicted Labels\")\n",
        "plt.ylabel(\"True Labels\")\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 963
        },
        "id": "cLE2kBuvEvw1",
        "outputId": "07e0af3c-986e-47a7-b68b-2825f5cf46d0"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Accuracy: 0.7249\n",
            "Test Loss: 0.8128\n",
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 6ms/step\n",
            "\n",
            "Classification Report:\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "    airplane       0.74      0.76      0.75      1000\n",
            "  automobile       0.87      0.83      0.85      1000\n",
            "        bird       0.72      0.52      0.61      1000\n",
            "         cat       0.58      0.49      0.53      1000\n",
            "        deer       0.61      0.77      0.68      1000\n",
            "         dog       0.65      0.62      0.64      1000\n",
            "        frog       0.77      0.80      0.79      1000\n",
            "       horse       0.73      0.76      0.74      1000\n",
            "        ship       0.80      0.84      0.82      1000\n",
            "       truck       0.77      0.85      0.81      1000\n",
            "\n",
            "    accuracy                           0.72     10000\n",
            "   macro avg       0.72      0.72      0.72     10000\n",
            "weighted avg       0.72      0.72      0.72     10000\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# ===============================================================\n",
        "# Visualize Predictions and Model Summary\n",
        "# ===============================================================\n",
        "\n",
        "# Select random samples from the test set\n",
        "num_images = 10\n",
        "indices = np.random.choice(len(x_test), num_images, replace=False)\n",
        "sample_images = x_test[indices]\n",
        "sample_labels = y_true_classes[indices]\n",
        "sample_preds = y_pred_classes[indices]\n",
        "\n",
        "# Display sample predictions\n",
        "plt.figure(figsize=(12, 5))\n",
        "for i in range(num_images):\n",
        "    plt.subplot(2, 5, i + 1)\n",
        "    plt.imshow(sample_images[i])\n",
        "    plt.axis('off')\n",
        "    true_label = class_names[sample_labels[i]]\n",
        "    pred_label = class_names[sample_preds[i]]\n",
        "    color = \"green\" if true_label == pred_label else \"red\"\n",
        "    plt.title(f\"T: {true_label}\\nP: {pred_label}\", color=color)\n",
        "plt.suptitle(\"Sample Predictions from CNN on CIFAR-10\")\n",
        "plt.tight_layout()\n",
        "plt.show()\n",
        "\n",
        "# Summary of model evaluation\n",
        "print(\"==============================================================\")\n",
        "print(\"Model Evaluation Summary\")\n",
        "print(\"==============================================================\")\n",
        "print(f\"Final Test Accuracy : {test_accuracy:.4f}\")\n",
        "print(f\"Final Test Loss      : {test_loss:.4f}\")\n",
        "print(\"Model performed best on 'automobile' and 'ship' classes,\")\n",
        "print(\"and showed relatively lower performance on 'cat' and 'bird' classes.\")\n",
        "print(\"The model achieves a balanced generalization and is suitable for baseline CIFAR-10 classification.\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 651
        },
        "id": "iALBm7rnE6Dv",
        "outputId": "56b3777f-f9ce-4f57-f29b-bb8072e78b97"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x500 with 10 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "==============================================================\n",
            "Model Evaluation Summary\n",
            "==============================================================\n",
            "Final Test Accuracy : 0.7249\n",
            "Final Test Loss      : 0.8128\n",
            "Model performed best on 'automobile' and 'ship' classes,\n",
            "and showed relatively lower performance on 'cat' and 'bird' classes.\n",
            "The model achieves a balanced generalization and is suitable for baseline CIFAR-10 classification.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# ===============================================================\n",
        "# Save Model and Weights\n",
        "# ===============================================================\n",
        "\n",
        "model.save(\"cifar10_cnn_model.h5\")\n",
        "print(\"Model saved successfully as 'cifar10_cnn_model.h5'\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P8j2TqDNFJsS",
        "outputId": "24b8eafa-9bfb-4e09-a40d-24ad90d1ab63"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model saved successfully as 'cifar10_cnn_model.h5'\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "l_N4Zy6DFhvc"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}