{
  "cells": [
    {
      "cell_type": "markdown",
      "id": "3458975f",
      "metadata": {
        "id": "3458975f"
      },
      "source": [
        "# CIFAR-10 CNN Classification with Inline Comments\n",
        "\n",
        "Full CNN pipeline using TensorFlow/Keras.\n",
        "- Includes dataset overview, preprocessing, CNN model, hyperparameter tuning, final training (20 epochs), evaluation, extra visuals, data augmentation, and result export.\n",
        "- Inline explanations provided in every cell for report readiness."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "b4500a58",
      "metadata": {
        "id": "b4500a58",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4879e514-b659-4ee9-a8de-07445174e9ec"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "TensorFlow version: 2.19.0\n"
          ]
        }
      ],
      "source": [
        "# Cell 1: Import Libraries\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras import datasets, layers, models\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn.metrics import confusion_matrix, classification_report\n",
        "import os\n",
        "\n",
        "print(\"TensorFlow version:\", tf.__version__)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "3b7d8b53",
      "metadata": {
        "id": "3b7d8b53",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9d9dbaa2-3bd4-4beb-f777-225bb804d14f"
      },
      "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",
            "Testing data shape: (10000, 32, 32, 3)\n",
            "Number of classes: 10\n"
          ]
        }
      ],
      "source": [
        "# Cell 2: Load CIFAR-10 dataset\n",
        "(X_train, y_train), (X_test, y_test) = datasets.cifar10.load_data()\n",
        "\n",
        "print(\"Training data shape:\", X_train.shape)\n",
        "print(\"Testing data shape:\", X_test.shape)\n",
        "print(\"Number of classes:\", len(np.unique(y_train)))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "79bd29d9",
      "metadata": {
        "id": "79bd29d9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 598
        },
        "outputId": "406b42c0-45c4-4391-9050-2fa013680d4c"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 10 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== Dataset Summary Table =====\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "        Dataset  Number of Samples Image Size  Number of Classes\n",
              "0  Training Set              50000    32x32x3                 10\n",
              "1      Test Set              10000    32x32x3                 10"
            ],
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              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "      <script>\n",
              "        async function quickchart(key) {\n",
              "          const quickchartButtonEl =\n",
              "            document.querySelector('#' + key + ' button');\n",
              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "          try {\n",
              "            const charts = await google.colab.kernel.invokeFunction(\n",
              "                'suggestCharts', [key], {});\n",
              "          } catch (error) {\n",
              "            console.error('Error during call to suggestCharts:', error);\n",
              "          }\n",
              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "        }\n",
              "        (() => {\n",
              "          let quickchartButtonEl =\n",
              "            document.querySelector('#df-d3c5dcff-c01a-43f0-a7e4-c783aaeada3f button');\n",
              "          quickchartButtonEl.style.display =\n",
              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "        })();\n",
              "      </script>\n",
              "    </div>\n",
              "\n",
              "  <div id=\"id_411a36a0-699b-43a1-ac70-41f3c248461a\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df_summary')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_411a36a0-699b-43a1-ac70-41f3c248461a button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df_summary');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_summary",
              "summary": "{\n  \"name\": \"df_summary\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": \"Dataset\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Test Set\",\n          \"Training Set\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Number of Samples\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 28284,\n        \"min\": 10000,\n        \"max\": 50000,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          10000,\n          50000\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Image Size\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"32x32x3\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Number of Classes\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 10,\n        \"max\": 10,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          10\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Cell 3: Visualize sample images and show summary table\n",
        "plt.figure(figsize=(10,5))\n",
        "for i in range(10):\n",
        "    plt.subplot(2,5,i+1)\n",
        "    plt.imshow(X_train[i])\n",
        "    plt.title(f\"Label: {y_train[i][0]}\")\n",
        "    plt.axis('off')\n",
        "plt.tight_layout()\n",
        "plt.show()\n",
        "\n",
        "data_summary = {\n",
        "    \"Dataset\": [\"Training Set\", \"Test Set\"],\n",
        "    \"Number of Samples\": [len(X_train), len(X_test)],\n",
        "    \"Image Size\": [\"32x32x3\", \"32x32x3\"],\n",
        "    \"Number of Classes\": [10, 10]\n",
        "}\n",
        "df_summary = pd.DataFrame(data_summary)\n",
        "print(\"===== Dataset Summary Table =====\")\n",
        "display(df_summary)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "b2b37b42",
      "metadata": {
        "id": "b2b37b42",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2a36990c-1f00-4372-8bf4-9665bfa05a9f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Preprocessing complete. Shapes:\n",
            "X_train (50000, 32, 32, 3) y_train (50000, 10)\n"
          ]
        }
      ],
      "source": [
        "# Cell 4: Normalize images to [0,1] and one-hot encode labels\n",
        "X_train = X_train.astype('float32') / 255.0\n",
        "X_test = X_test.astype('float32') / 255.0\n",
        "\n",
        "y_train = tf.keras.utils.to_categorical(y_train, 10)\n",
        "y_test = tf.keras.utils.to_categorical(y_test, 10)\n",
        "\n",
        "print('Preprocessing complete. Shapes:')\n",
        "print('X_train', X_train.shape, 'y_train', y_train.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "a26fa878",
      "metadata": {
        "id": "a26fa878",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ff12f29e-a880-4659-f089-7401e2681ede"
      },
      "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;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m)     │           \u001b[38;5;34m896\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ batch_normalization             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m)     │           \u001b[38;5;34m128\u001b[0m │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)            │                        │               │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ max_pooling2d (\u001b[38;5;33mMaxPooling2D\u001b[0m)    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\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;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m64\u001b[0m)     │        \u001b[38;5;34m18,496\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ batch_normalization_1           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m64\u001b[0m)     │           \u001b[38;5;34m256\u001b[0m │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)            │                        │               │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ max_pooling2d_1 (\u001b[38;5;33mMaxPooling2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\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;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m128\u001b[0m)      │        \u001b[38;5;34m73,856\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ batch_normalization_2           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m128\u001b[0m)      │           \u001b[38;5;34m512\u001b[0m │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)            │                        │               │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ max_pooling2d_2 (\u001b[38;5;33mMaxPooling2D\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;34m0\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ flatten (\u001b[38;5;33mFlatten\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2048\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;34m262,272\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\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)     │           <span style=\"color: #00af00; text-decoration-color: #00af00\">896</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ batch_normalization             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)     │           <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</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\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</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\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)     │        <span style=\"color: #00af00; text-decoration-color: #00af00\">18,496</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ batch_normalization_1           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)     │           <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</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\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</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\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)      │        <span style=\"color: #00af00; text-decoration-color: #00af00\">73,856</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ batch_normalization_2           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)      │           <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</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\">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\">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\">2048</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\">262,272</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;34m357,706\u001b[0m (1.36 MB)\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\">357,706</span> (1.36 MB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m357,258\u001b[0m (1.36 MB)\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\">357,258</span> (1.36 MB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m448\u001b[0m (1.75 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\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">448</span> (1.75 KB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Cell 5: Define CNN model function\n",
        "def create_cnn_model():\n",
        "    model = models.Sequential([\n",
        "        layers.Conv2D(32, (3,3), activation='relu', padding='same', input_shape=(32,32,3)),\n",
        "        layers.BatchNormalization(),\n",
        "        layers.MaxPooling2D((2,2)),\n",
        "        layers.Conv2D(64, (3,3), activation='relu', padding='same'),\n",
        "        layers.BatchNormalization(),\n",
        "        layers.MaxPooling2D((2,2)),\n",
        "        layers.Conv2D(128, (3,3), activation='relu', padding='same'),\n",
        "        layers.BatchNormalization(),\n",
        "        layers.MaxPooling2D((2,2)),\n",
        "        layers.Flatten(),\n",
        "        layers.Dense(128, activation='relu'),\n",
        "        layers.Dropout(0.5),\n",
        "        layers.Dense(10, activation='softmax')\n",
        "    ])\n",
        "    return model\n",
        "\n",
        "# Display model summary\n",
        "temp_model = create_cnn_model()\n",
        "temp_model.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "edf52429",
      "metadata": {
        "id": "edf52429",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 689
        },
        "outputId": "56347ec4-9d1a-4451-83ab-c0b72b507f59"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Batch=32, LR=0.001, Val_Acc=0.713\n",
            "Batch=32, LR=0.0005, Val_Acc=0.716\n",
            "Batch=64, LR=0.001, Val_Acc=0.652\n",
            "Batch=64, LR=0.0005, Val_Acc=0.636\n",
            "\n",
            "===== Hyperparameter Tuning Results =====\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "   Batch Size  Learning Rate  Validation Accuracy\n",
              "0          32         0.0010               0.7130\n",
              "1          32         0.0005               0.7162\n",
              "2          64         0.0010               0.6517\n",
              "3          64         0.0005               0.6361"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d04f7363-6e4d-4299-aa51-d6bad21c9538\" class=\"colab-df-container\">\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>Batch Size</th>\n",
              "      <th>Learning Rate</th>\n",
              "      <th>Validation Accuracy</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>32</td>\n",
              "      <td>0.0010</td>\n",
              "      <td>0.7130</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>32</td>\n",
              "      <td>0.0005</td>\n",
              "      <td>0.7162</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>64</td>\n",
              "      <td>0.0010</td>\n",
              "      <td>0.6517</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>64</td>\n",
              "      <td>0.0005</td>\n",
              "      <td>0.6361</td>\n",
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              "    <div class=\"colab-df-buttons\">\n",
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              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "    .colab-df-convert:hover {\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
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              "\n",
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              "      const buttonEl =\n",
              "        document.querySelector('#df-d04f7363-6e4d-4299-aa51-d6bad21c9538 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-d04f7363-6e4d-4299-aa51-d6bad21c9538');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
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              "  </div>\n",
              "\n",
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              "                title=\"Suggest charts\"\n",
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              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
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              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "      <script>\n",
              "        async function quickchart(key) {\n",
              "          const quickchartButtonEl =\n",
              "            document.querySelector('#' + key + ' button');\n",
              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "          try {\n",
              "            const charts = await google.colab.kernel.invokeFunction(\n",
              "                'suggestCharts', [key], {});\n",
              "          } catch (error) {\n",
              "            console.error('Error during call to suggestCharts:', error);\n",
              "          }\n",
              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "        }\n",
              "        (() => {\n",
              "          let quickchartButtonEl =\n",
              "            document.querySelector('#df-5a843c0a-ee38-4ad6-8ab9-72bbdcb52a27 button');\n",
              "          quickchartButtonEl.style.display =\n",
              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "        })();\n",
              "      </script>\n",
              "    </div>\n",
              "\n",
              "  <div id=\"id_707079c3-be10-460c-b6b8-4ac208e71610\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
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              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
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              "        fill: #D2E3FC;\n",
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              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
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              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df_results')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
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              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_707079c3-be10-460c-b6b8-4ac208e71610 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df_results');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_results",
              "summary": "{\n  \"name\": \"df_results\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"Batch Size\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 18,\n        \"min\": 32,\n        \"max\": 64,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          64,\n          32\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Learning Rate\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.00028867513459481284,\n        \"min\": 0.0005,\n        \"max\": 0.001,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.0005,\n          0.001\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Validation Accuracy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.041333154327227235,\n        \"min\": 0.6360999941825867,\n        \"max\": 0.7161999940872192,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.7161999940872192,\n          0.6360999941825867\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best params (batch_size, learning_rate): (32, 0.0005)\n"
          ]
        }
      ],
      "source": [
        "# Cell 6: Hyperparameter tuning (short epochs for speed)\n",
        "batch_sizes = [32, 64]\n",
        "learning_rates = [0.001, 0.0005]\n",
        "\n",
        "results = []\n",
        "for batch in batch_sizes:\n",
        "    for lr in learning_rates:\n",
        "        model = create_cnn_model()\n",
        "        model.compile(optimizer=Adam(learning_rate=lr),\n",
        "                      loss='categorical_crossentropy',\n",
        "                      metrics=['accuracy'])\n",
        "        history = model.fit(X_train, y_train, epochs=5, batch_size=batch, # short epochs for tuning\n",
        "                            validation_data=(X_test, y_test), verbose=0)\n",
        "        val_acc = history.history['val_accuracy'][-1]\n",
        "        results.append((batch, lr, val_acc))\n",
        "        print(f\"Batch={batch}, LR={lr}, Val_Acc={val_acc:.3f}\")\n",
        "\n",
        "df_results = pd.DataFrame(results, columns=['Batch Size', 'Learning Rate', 'Validation Accuracy'])\n",
        "print('\\n===== Hyperparameter Tuning Results =====')\n",
        "display(df_results)\n",
        "\n",
        "# Bar plot\n",
        "plt.figure(figsize=(8,4))\n",
        "sns.barplot(x='Batch Size', y='Validation Accuracy', hue='Learning Rate', data=df_results)\n",
        "plt.title('Hyperparameter Tuning Results')\n",
        "plt.show()\n",
        "\n",
        "# Choose best parameters\n",
        "best_row = df_results.loc[df_results['Validation Accuracy'].idxmax()]\n",
        "best_params = (int(best_row['Batch Size']), float(best_row['Learning Rate']))\n",
        "print('Best params (batch_size, learning_rate):', best_params)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "96ebeaf3",
      "metadata": {
        "id": "96ebeaf3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "09587dd1-51e0-4b46-d0c2-0cd9e7d1f971"
      },
      "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": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 7ms/step - accuracy: 0.3445 - loss: 1.8842 - val_accuracy: 0.5603 - val_loss: 1.2188\n",
            "Epoch 2/20\n",
            "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 4ms/step - accuracy: 0.5370 - loss: 1.2821 - val_accuracy: 0.5705 - val_loss: 1.2111\n",
            "Epoch 3/20\n",
            "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 4ms/step - accuracy: 0.6269 - loss: 1.0620 - val_accuracy: 0.6794 - val_loss: 0.9232\n",
            "Epoch 4/20\n",
            "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 4ms/step - accuracy: 0.6743 - loss: 0.9253 - val_accuracy: 0.7041 - val_loss: 0.8703\n",
            "Epoch 5/20\n",
            "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 4ms/step - accuracy: 0.7092 - loss: 0.8281 - val_accuracy: 0.6876 - val_loss: 0.8830\n",
            "Epoch 6/20\n",
            "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 5ms/step - accuracy: 0.7368 - loss: 0.7537 - val_accuracy: 0.6750 - val_loss: 0.9815\n",
            "Epoch 7/20\n",
            "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 4ms/step - accuracy: 0.7621 - loss: 0.6834 - val_accuracy: 0.7163 - val_loss: 0.8337\n",
            "Epoch 8/20\n",
            "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 4ms/step - accuracy: 0.7830 - loss: 0.6142 - val_accuracy: 0.7539 - val_loss: 0.7550\n",
            "Epoch 9/20\n",
            "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 4ms/step - accuracy: 0.7961 - loss: 0.5809 - val_accuracy: 0.7392 - val_loss: 0.8081\n",
            "Epoch 10/20\n",
            "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 4ms/step - accuracy: 0.8133 - loss: 0.5306 - val_accuracy: 0.7671 - val_loss: 0.7188\n",
            "Epoch 11/20\n",
            "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 4ms/step - accuracy: 0.8311 - loss: 0.4824 - val_accuracy: 0.7526 - val_loss: 0.7654\n",
            "Epoch 12/20\n",
            "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 4ms/step - accuracy: 0.8377 - loss: 0.4524 - val_accuracy: 0.7475 - val_loss: 0.8222\n",
            "Epoch 13/20\n",
            "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 4ms/step - accuracy: 0.8484 - loss: 0.4211 - val_accuracy: 0.7467 - val_loss: 0.8306\n",
            "Epoch 14/20\n",
            "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 4ms/step - accuracy: 0.8582 - loss: 0.3976 - val_accuracy: 0.7617 - val_loss: 0.8339\n",
            "Epoch 15/20\n",
            "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 4ms/step - accuracy: 0.8701 - loss: 0.3690 - val_accuracy: 0.6910 - val_loss: 1.2118\n",
            "Epoch 16/20\n",
            "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 4ms/step - accuracy: 0.8764 - loss: 0.3469 - val_accuracy: 0.7636 - val_loss: 0.8521\n",
            "Epoch 17/20\n",
            "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 4ms/step - accuracy: 0.8806 - loss: 0.3350 - val_accuracy: 0.7711 - val_loss: 0.8085\n",
            "Epoch 18/20\n",
            "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 4ms/step - accuracy: 0.8890 - loss: 0.3084 - val_accuracy: 0.7410 - val_loss: 1.0013\n",
            "Epoch 19/20\n",
            "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 4ms/step - accuracy: 0.8949 - loss: 0.2943 - val_accuracy: 0.7560 - val_loss: 0.9104\n",
            "Epoch 20/20\n",
            "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 4ms/step - accuracy: 0.8986 - loss: 0.2801 - val_accuracy: 0.7761 - val_loss: 0.8194\n"
          ]
        }
      ],
      "source": [
        "# Cell 7: Train final CNN with best hyperparameters (20 epochs)\n",
        "batch_size, learning_rate = best_params\n",
        "model = create_cnn_model()\n",
        "model.compile(optimizer=Adam(learning_rate=learning_rate),\n",
        "              loss='categorical_crossentropy',\n",
        "              metrics=['accuracy'])\n",
        "\n",
        "history = model.fit(X_train, y_train, epochs=20, batch_size=batch_size, # 20 epochs\n",
        "                    validation_data=(X_test, y_test))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "40939134",
      "metadata": {
        "id": "40939134",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "44bdb58b-dee5-4978-cb83-777323b86214"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.7743 - loss: 0.8149\n",
            "Test Accuracy: 0.7761\n",
            "Test Loss: 0.8194\n"
          ]
        }
      ],
      "source": [
        "# Cell 8: Evaluate final model\n",
        "test_loss, test_acc = model.evaluate(X_test, y_test)\n",
        "print(f\"Test Accuracy: {test_acc:.4f}\")\n",
        "print(f\"Test Loss: {test_loss:.4f}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "e89da96d",
      "metadata": {
        "id": "e89da96d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e894f1a8-346d-464a-b4bf-acfc2ec54cd0"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x500 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Cell 9: Plot training & validation accuracy and loss\n",
        "plt.figure(figsize=(12,5))\n",
        "plt.subplot(1,2,1)\n",
        "plt.plot(history.history['accuracy'], label='Train Accuracy')\n",
        "plt.plot(history.history['val_accuracy'], label='Validation Accuracy')\n",
        "plt.title('Model Accuracy')\n",
        "plt.xlabel('Epochs'); plt.ylabel('Accuracy')\n",
        "plt.legend()\n",
        "\n",
        "plt.subplot(1,2,2)\n",
        "plt.plot(history.history['loss'], label='Train Loss')\n",
        "plt.plot(history.history['val_loss'], label='Validation Loss')\n",
        "plt.title('Model Loss')\n",
        "plt.xlabel('Epochs'); plt.ylabel('Loss')\n",
        "plt.legend()\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "be7b4cf9",
      "metadata": {
        "id": "be7b4cf9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d107cd2b-9fb0-4dd4-c70a-506cda8bd5f2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Classification Report:\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.78      0.79      0.79      1000\n",
            "           1       0.90      0.88      0.89      1000\n",
            "           2       0.68      0.72      0.70      1000\n",
            "           3       0.59      0.65      0.62      1000\n",
            "           4       0.72      0.78      0.75      1000\n",
            "           5       0.75      0.62      0.68      1000\n",
            "           6       0.88      0.78      0.83      1000\n",
            "           7       0.76      0.82      0.79      1000\n",
            "           8       0.89      0.85      0.87      1000\n",
            "           9       0.86      0.87      0.87      1000\n",
            "\n",
            "    accuracy                           0.78     10000\n",
            "   macro avg       0.78      0.78      0.78     10000\n",
            "weighted avg       0.78      0.78      0.78     10000\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# Cell 10: Confusion matrix and classification report\n",
        "y_pred = model.predict(X_test)\n",
        "y_pred_classes = np.argmax(y_pred, axis=1)\n",
        "y_true = np.argmax(y_test, axis=1)\n",
        "\n",
        "cm = confusion_matrix(y_true, y_pred_classes)\n",
        "plt.figure(figsize=(10,8))\n",
        "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues')\n",
        "plt.title('Confusion Matrix')\n",
        "plt.xlabel('Predicted Label')\n",
        "plt.ylabel('True Label')\n",
        "plt.show()\n",
        "\n",
        "print(\"\\nClassification Report:\\n\")\n",
        "print(classification_report(y_true, y_pred_classes))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "7bf0e803",
      "metadata": {
        "id": "7bf0e803",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2725cd85-412d-4531-d982-c44b0f4e4bdf"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Cell 11: Class distribution bar chart\n",
        "class_names = ['Airplane','Automobile','Bird','Cat','Deer','Dog','Frog','Horse','Ship','Truck']\n",
        "unique, counts = np.unique(np.argmax(y_train, axis=1), return_counts=True)\n",
        "\n",
        "plt.figure(figsize=(10,5))\n",
        "ax = sns.barplot(x=class_names, y=counts)\n",
        "plt.title('CIFAR-10 Training Data Distribution')\n",
        "plt.xlabel('Class')\n",
        "plt.ylabel('Number of Images')\n",
        "plt.xticks(rotation=45)\n",
        "for p in ax.patches:\n",
        "    ax.annotate(f\"{int(p.get_height())}\", (p.get_x()+0.2, p.get_height()+50))\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "14242f19",
      "metadata": {
        "id": "14242f19",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 497
        },
        "outputId": "bff2e49f-8315-4c82-bfa0-612d3eadc22a"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 10 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ],
      "source": [
        "# Cell 12: Display misclassified test images\n",
        "misclassified_indices = np.where(y_pred_classes != y_true)[0]\n",
        "\n",
        "plt.figure(figsize=(12,6))\n",
        "for i, idx in enumerate(misclassified_indices[:10]):\n",
        "    plt.subplot(2,5,i+1)\n",
        "    plt.imshow(X_test[idx])\n",
        "    plt.title(f\"True: {class_names[y_true[idx]]}\\nPred: {class_names[y_pred_classes[idx]]}\", fontsize=9)\n",
        "    plt.axis('off')\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "L2k5rOyvMcXv",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 794
        },
        "outputId": "9e5f7b0a-59ff-45ba-c9ae-112ebea3bd4f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===== FINAL MODEL SUMMARY =====\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1mModel: \"sequential_5\"\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_5\"</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_15 (\u001b[38;5;33mConv2D\u001b[0m)              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m)     │           \u001b[38;5;34m896\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ batch_normalization_15          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m)     │           \u001b[38;5;34m128\u001b[0m │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)            │                        │               │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ max_pooling2d_15 (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m32\u001b[0m)     │             \u001b[38;5;34m0\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv2d_16 (\u001b[38;5;33mConv2D\u001b[0m)              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m64\u001b[0m)     │        \u001b[38;5;34m18,496\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ batch_normalization_16          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m64\u001b[0m)     │           \u001b[38;5;34m256\u001b[0m │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)            │                        │               │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ max_pooling2d_16 (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m64\u001b[0m)       │             \u001b[38;5;34m0\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv2d_17 (\u001b[38;5;33mConv2D\u001b[0m)              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m128\u001b[0m)      │        \u001b[38;5;34m73,856\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ batch_normalization_17          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m128\u001b[0m)      │           \u001b[38;5;34m512\u001b[0m │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)            │                        │               │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ max_pooling2d_17 (\u001b[38;5;33mMaxPooling2D\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;34m0\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ flatten_5 (\u001b[38;5;33mFlatten\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2048\u001b[0m)           │             \u001b[38;5;34m0\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dense_10 (\u001b[38;5;33mDense\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)            │       \u001b[38;5;34m262,272\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dropout_5 (\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_11 (\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_15 (<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\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)     │           <span style=\"color: #00af00; text-decoration-color: #00af00\">896</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ batch_normalization_15          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)     │           <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)            │                        │               │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ max_pooling2d_15 (<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\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)     │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv2d_16 (<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\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)     │        <span style=\"color: #00af00; text-decoration-color: #00af00\">18,496</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ batch_normalization_16          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)     │           <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)            │                        │               │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ max_pooling2d_16 (<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\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)       │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv2d_17 (<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\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)      │        <span style=\"color: #00af00; text-decoration-color: #00af00\">73,856</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ batch_normalization_17          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)      │           <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)            │                        │               │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ max_pooling2d_17 (<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\">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\">0</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ flatten_5 (<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\">2048</span>)           │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dense_10 (<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\">262,272</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dropout_5 (<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_11 (<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;34m1,072,224\u001b[0m (4.09 MB)\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\">1,072,224</span> (4.09 MB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m357,258\u001b[0m (1.36 MB)\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\">357,258</span> (1.36 MB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m448\u001b[0m (1.75 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\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">448</span> (1.75 KB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Optimizer params: \u001b[0m\u001b[38;5;34m714,518\u001b[0m (2.73 MB)\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\"> Optimizer params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">714,518</span> (2.73 MB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "===== BEST HYPERPARAMETERS =====\n",
            "Batch Size: 32, Learning Rate: 0.0005\n",
            "\n",
            "===== FINAL MODEL PERFORMANCE =====\n",
            "Test Accuracy: 0.7761\n",
            "Test Loss: 0.8194\n",
            "\n",
            "===== PERFORMANCE INTERPRETATION =====\n",
            "Moderate performance – model learns patterns but could improve.\n"
          ]
        }
      ],
      "source": [
        "# Cell 13: Model summary, best params, performance interpretation\n",
        "print(\"===== FINAL MODEL SUMMARY =====\")\n",
        "model.summary()\n",
        "\n",
        "print(\"\\n===== BEST HYPERPARAMETERS =====\")\n",
        "print(f\"Batch Size: {batch_size}, Learning Rate: {learning_rate}\")\n",
        "\n",
        "print(\"\\n===== FINAL MODEL PERFORMANCE =====\")\n",
        "print(f\"Test Accuracy: {test_acc:.4f}\")\n",
        "print(f\"Test Loss: {test_loss:.4f}\")\n",
        "\n",
        "# Interpretation\n",
        "if test_acc > 0.8:\n",
        "    performance_comment = \"Excellent performance – model generalizes well.\"\n",
        "elif test_acc > 0.6:\n",
        "    performance_comment = \"Moderate performance – model learns patterns but could improve.\"\n",
        "else:\n",
        "    performance_comment = \"Low performance – may need architectural changes or more data.\"\n",
        "\n",
        "print(\"\\n===== PERFORMANCE INTERPRETATION =====\")\n",
        "print(performance_comment)"
      ],
      "id": "L2k5rOyvMcXv"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "d66093fe",
      "metadata": {
        "id": "d66093fe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 794
        },
        "outputId": "44141403-2e32-4be5-edcf-5933c152e9d3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "===== FINAL MODEL PERFORMANCE =====\n",
            "Test Accuracy: 0.7761\n",
            "Test Loss: 0.8194\n",
            "===== FINAL MODEL SUMMARY =====\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1mModel: \"sequential_5\"\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_5\"</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_15 (\u001b[38;5;33mConv2D\u001b[0m)              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m)     │           \u001b[38;5;34m896\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ batch_normalization_15          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m)     │           \u001b[38;5;34m128\u001b[0m │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)            │                        │               │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ max_pooling2d_15 (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m32\u001b[0m)     │             \u001b[38;5;34m0\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv2d_16 (\u001b[38;5;33mConv2D\u001b[0m)              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m64\u001b[0m)     │        \u001b[38;5;34m18,496\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ batch_normalization_16          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m64\u001b[0m)     │           \u001b[38;5;34m256\u001b[0m │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)            │                        │               │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ max_pooling2d_16 (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m64\u001b[0m)       │             \u001b[38;5;34m0\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv2d_17 (\u001b[38;5;33mConv2D\u001b[0m)              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m128\u001b[0m)      │        \u001b[38;5;34m73,856\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ batch_normalization_17          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m128\u001b[0m)      │           \u001b[38;5;34m512\u001b[0m │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)            │                        │               │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ max_pooling2d_17 (\u001b[38;5;33mMaxPooling2D\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;34m0\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ flatten_5 (\u001b[38;5;33mFlatten\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2048\u001b[0m)           │             \u001b[38;5;34m0\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dense_10 (\u001b[38;5;33mDense\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)            │       \u001b[38;5;34m262,272\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dropout_5 (\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_11 (\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_15 (<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\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)     │           <span style=\"color: #00af00; text-decoration-color: #00af00\">896</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ batch_normalization_15          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)     │           <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)            │                        │               │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ max_pooling2d_15 (<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\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)     │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv2d_16 (<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\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)     │        <span style=\"color: #00af00; text-decoration-color: #00af00\">18,496</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ batch_normalization_16          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)     │           <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)            │                        │               │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ max_pooling2d_16 (<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\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)       │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv2d_17 (<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\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)      │        <span style=\"color: #00af00; text-decoration-color: #00af00\">73,856</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ batch_normalization_17          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)      │           <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)            │                        │               │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ max_pooling2d_17 (<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\">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\">0</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ flatten_5 (<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\">2048</span>)           │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dense_10 (<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\">262,272</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dropout_5 (<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_11 (<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;34m1,072,224\u001b[0m (4.09 MB)\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\">1,072,224</span> (4.09 MB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m357,258\u001b[0m (1.36 MB)\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\">357,258</span> (1.36 MB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m448\u001b[0m (1.75 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\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">448</span> (1.75 KB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Optimizer params: \u001b[0m\u001b[38;5;34m714,518\u001b[0m (2.73 MB)\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\"> Optimizer params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">714,518</span> (2.73 MB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "===== BEST HYPERPARAMETERS =====\n",
            "Batch Size: 32, Learning Rate: 0.0005\n",
            "\n",
            "===== PERFORMANCE INTERPRETATION =====\n",
            "Moderate performance – model learns patterns but could improve.\n"
          ]
        }
      ],
      "source": [
        "\n",
        "print(\"\\n===== FINAL MODEL PERFORMANCE =====\")\n",
        "print(f\"Test Accuracy: {test_acc:.4f}\")\n",
        "print(f\"Test Loss: {test_loss:.4f}\")\n",
        "# Cell 13: Model summary, best params, performance interpretation\n",
        "print(\"===== FINAL MODEL SUMMARY =====\")\n",
        "model.summary()\n",
        "\n",
        "print(\"\\n===== BEST HYPERPARAMETERS =====\")\n",
        "print(f\"Batch Size: {batch_size}, Learning Rate: {learning_rate}\")\n",
        "\n",
        "# Interpretation\n",
        "if test_acc > 0.8:\n",
        "    performance_comment = \"Excellent performance – model generalizes well.\"\n",
        "elif test_acc > 0.6:\n",
        "    performance_comment = \"Moderate performance – model learns patterns but could improve.\"\n",
        "else:\n",
        "    performance_comment = \"Low performance – may need architectural changes or more data.\"\n",
        "\n",
        "print(\"\\n===== PERFORMANCE INTERPRETATION =====\")\n",
        "print(performance_comment)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "609973fd",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "609973fd",
        "outputId": "90ef4208-0179-449a-cb33-17d47296664e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n"
          ]
        },
        {
          "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",
            "/usr/local/lib/python3.12/dist-packages/keras/src/trainers/data_adapters/py_dataset_adapter.py:121: UserWarning: Your `PyDataset` class should call `super().__init__(**kwargs)` in its constructor. `**kwargs` can include `workers`, `use_multiprocessing`, `max_queue_size`. Do not pass these arguments to `fit()`, as they will be ignored.\n",
            "  self._warn_if_super_not_called()\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m42s\u001b[0m 23ms/step - accuracy: 0.3191 - loss: 1.9424 - val_accuracy: 0.5155 - val_loss: 1.3393\n",
            "Epoch 2/20\n",
            "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m33s\u001b[0m 21ms/step - accuracy: 0.4645 - loss: 1.4751 - val_accuracy: 0.5785 - val_loss: 1.1969\n",
            "Epoch 3/20\n",
            "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m32s\u001b[0m 21ms/step - accuracy: 0.5283 - loss: 1.3232 - val_accuracy: 0.4775 - val_loss: 1.8945\n",
            "Epoch 4/20\n",
            "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m33s\u001b[0m 21ms/step - accuracy: 0.5816 - loss: 1.1974 - val_accuracy: 0.6356 - val_loss: 1.0839\n",
            "Epoch 5/20\n",
            "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m33s\u001b[0m 21ms/step - accuracy: 0.6140 - loss: 1.1095 - val_accuracy: 0.6310 - val_loss: 1.0726\n",
            "Epoch 6/20\n",
            "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m32s\u001b[0m 21ms/step - accuracy: 0.6320 - loss: 1.0505 - val_accuracy: 0.6277 - val_loss: 1.2265\n",
            "Epoch 7/20\n",
            "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m33s\u001b[0m 21ms/step - accuracy: 0.6559 - loss: 1.0015 - val_accuracy: 0.6702 - val_loss: 0.9535\n",
            "Epoch 8/20\n",
            "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m33s\u001b[0m 21ms/step - accuracy: 0.6673 - loss: 0.9581 - val_accuracy: 0.6838 - val_loss: 0.9430\n",
            "Epoch 9/20\n",
            "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m32s\u001b[0m 21ms/step - accuracy: 0.6754 - loss: 0.9372 - val_accuracy: 0.7094 - val_loss: 0.8249\n",
            "Epoch 10/20\n",
            "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m33s\u001b[0m 21ms/step - accuracy: 0.6952 - loss: 0.8936 - val_accuracy: 0.7332 - val_loss: 0.7886\n",
            "Epoch 11/20\n",
            "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m33s\u001b[0m 21ms/step - accuracy: 0.7030 - loss: 0.8618 - val_accuracy: 0.7130 - val_loss: 0.8670\n",
            "Epoch 12/20\n",
            "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m33s\u001b[0m 21ms/step - accuracy: 0.7105 - loss: 0.8472 - val_accuracy: 0.7328 - val_loss: 0.8136\n",
            "Epoch 13/20\n",
            "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m33s\u001b[0m 21ms/step - accuracy: 0.7186 - loss: 0.8251 - val_accuracy: 0.7289 - val_loss: 0.8096\n",
            "Epoch 14/20\n",
            "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m33s\u001b[0m 21ms/step - accuracy: 0.7225 - loss: 0.8081 - val_accuracy: 0.7640 - val_loss: 0.6836\n",
            "Epoch 15/20\n",
            "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m33s\u001b[0m 21ms/step - accuracy: 0.7280 - loss: 0.7941 - val_accuracy: 0.6816 - val_loss: 0.9981\n",
            "Epoch 16/20\n",
            "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m33s\u001b[0m 21ms/step - accuracy: 0.7357 - loss: 0.7727 - val_accuracy: 0.7100 - val_loss: 0.8672\n",
            "Epoch 17/20\n",
            "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m34s\u001b[0m 22ms/step - accuracy: 0.7425 - loss: 0.7552 - val_accuracy: 0.7739 - val_loss: 0.6722\n",
            "Epoch 18/20\n",
            "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m33s\u001b[0m 21ms/step - accuracy: 0.7446 - loss: 0.7492 - val_accuracy: 0.7291 - val_loss: 0.8038\n",
            "Epoch 19/20\n",
            "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m32s\u001b[0m 21ms/step - accuracy: 0.7534 - loss: 0.7256 - val_accuracy: 0.7587 - val_loss: 0.7340\n",
            "Epoch 20/20\n",
            "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m33s\u001b[0m 21ms/step - accuracy: 0.7535 - loss: 0.7262 - val_accuracy: 0.7903 - val_loss: 0.6450\n"
          ]
        }
      ],
      "source": [
        "# Cell 14: Data Augmentation for improved generalization\n",
        "datagen = ImageDataGenerator(\n",
        "    rotation_range=15,\n",
        "    width_shift_range=0.1,\n",
        "    height_shift_range=0.1,\n",
        "    horizontal_flip=True,\n",
        "    zoom_range=0.1,\n",
        "    fill_mode='nearest'\n",
        ")\n",
        "datagen.fit(X_train)\n",
        "\n",
        "aug_model = create_cnn_model()\n",
        "aug_model.compile(optimizer=Adam(learning_rate=learning_rate),\n",
        "                  loss='categorical_crossentropy',\n",
        "                  metrics=['accuracy'])\n",
        "\n",
        "history_aug = aug_model.fit(\n",
        "    datagen.flow(X_train, y_train, batch_size=batch_size),\n",
        "    validation_data=(X_test, y_test),\n",
        "    epochs=20,\n",
        "    verbose=1\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "6239871e",
      "metadata": {
        "id": "6239871e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 647
        },
        "outputId": "a53c131e-3cf0-44f1-bc9a-bb27b7782994"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Original Model Accuracy: 0.7761\n",
            "Augmented Model Accuracy: 0.7903\n",
            "Accuracy Improvement: 0.0142\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": 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            "text/plain": [
              "   Model Version  Validation Accuracy  Test Loss\n",
              "0   Original CNN               0.7761     0.8194\n",
              "1  Augmented CNN               0.7903     0.6450"
            ],
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              "      spin 1s steps(1) infinite;\n",
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              "\n",
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              "    </style>\n",
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              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('comparison_table');\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "comparison_table",
              "summary": "{\n  \"name\": \"comparison_table\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": \"Model Version\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Augmented CNN\",\n          \"Original CNN\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Validation Accuracy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.010040916292848968,\n        \"min\": 0.7761,\n        \"max\": 0.7903,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.7903,\n          0.7761\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Test Loss\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.12331942263893389,\n        \"min\": 0.645,\n        \"max\": 0.8194,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.645,\n          0.8194\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Cell 15: Compare original and augmented model performance\n",
        "aug_test_loss, aug_test_acc = aug_model.evaluate(X_test, y_test, verbose=0)\n",
        "\n",
        "print(\"Original Model Accuracy:\", round(test_acc,4))\n",
        "print(\"Augmented Model Accuracy:\", round(aug_test_acc,4))\n",
        "print(\"Accuracy Improvement:\", round(aug_test_acc - test_acc,4))\n",
        "\n",
        "# Plot comparison\n",
        "plt.figure(figsize=(10,5))\n",
        "plt.plot(history.history['val_accuracy'], label='Original Validation Acc', linestyle='--')\n",
        "plt.plot(history_aug.history['val_accuracy'], label='Augmented Validation Acc', linewidth=2)\n",
        "plt.title('Validation Accuracy Comparison')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Validation Accuracy')\n",
        "plt.legend()\n",
        "plt.show()\n",
        "\n",
        "# Table comparison\n",
        "comparison_table = pd.DataFrame({\n",
        "    'Model Version': ['Original CNN','Augmented CNN'],\n",
        "    'Validation Accuracy': [round(test_acc,4), round(aug_test_acc,4)],\n",
        "    'Test Loss': [round(test_loss,4), round(aug_test_loss,4)]\n",
        "})\n",
        "display(comparison_table)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "3ede8af4",
      "metadata": {
        "id": "3ede8af4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "16897233-adea-484f-9cd6-0c38821b77bf"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "✅ All results exported to /mnt/data/cifar10_results/\n"
          ]
        }
      ],
      "source": [
        "# Cell 16: Export results to CSV for report\n",
        "os.makedirs('/mnt/data/cifar10_results', exist_ok=True)\n",
        "df_results.to_csv('/mnt/data/cifar10_results/hyperparameter_results.csv', index=False)\n",
        "pd.DataFrame({'Metric':['Test Accuracy','Test Loss'], 'Value':[round(test_acc,4), round(test_loss,4)]})    .to_csv('/mnt/data/cifar10_results/model_metrics.csv', index=False)\n",
        "comparison_table.to_csv('/mnt/data/cifar10_results/augmentation_comparison.csv', index=False)\n",
        "\n",
        "print(\"✅ All results exported to /mnt/data/cifar10_results/\")"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4",
      "toc_visible": true
    },
    "language_info": {
      "name": "python"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "nbformat": 4,
  "nbformat_minor": 5
}