{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# **CNN Experiment 5 - Final Model**"
      ],
      "metadata": {
        "id": "ltloHB_i7QKl"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 1. Google Drive and Dataset Setup\n",
        "\n",
        "Google Drive is connected and the dataset is extracted."
      ],
      "metadata": {
        "id": "KlmJ5P9y75oA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount(\"/content/drive\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Sw0Vl5JgvbyO",
        "outputId": "6bc15ed9-a7ca-46c5-9461-883fcf80f5e4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import zipfile\n",
        "\n",
        "zip_path = \"/content/drive/MyDrive/PlantPathology_Split.zip\"\n",
        "extract_path = \"/content\"\n",
        "\n",
        "with zipfile.ZipFile(zip_path, \"r\") as zip_ref:\n",
        "    zip_ref.extractall(\"/content\")\n",
        "\n",
        "print(\"Unzip completed.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aU0-O3E6If7i",
        "outputId": "9f9ce384-77dd-43e7-8e58-2b8bc3eb741c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Unzip completed.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 2. Five-Class Dataset Preparation\n",
        "\n",
        "The five selected classes are copied into a new dataset."
      ],
      "metadata": {
        "id": "R-XH99SJ7_Wb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import shutil\n",
        "\n",
        "source_dir = \"/content/PlantPathology_Split\"\n",
        "target_dir = \"/content/PlantPathology_5Classes\"\n",
        "\n",
        "selected_classes = [\n",
        "    \"healthy\",\n",
        "    \"scab\",\n",
        "    \"frog_eye_leaf_spot\",\n",
        "    \"rust\",\n",
        "    \"powdery_mildew\"\n",
        "]\n",
        "\n",
        "splits = [\"train\", \"valid\", \"test\"]\n",
        "\n",
        "# Remove old version if it already exists\n",
        "if os.path.exists(target_dir):\n",
        "    shutil.rmtree(target_dir)\n",
        "\n",
        "# Copy only the five required classes\n",
        "for split in splits:\n",
        "    for class_name in selected_classes:\n",
        "\n",
        "        source_class_dir = os.path.join(\n",
        "            source_dir,\n",
        "            split,\n",
        "            class_name\n",
        "        )\n",
        "\n",
        "        target_class_dir = os.path.join(\n",
        "            target_dir,\n",
        "            split,\n",
        "            class_name\n",
        "        )\n",
        "\n",
        "        if not os.path.exists(source_class_dir):\n",
        "            print(f\"Missing folder: {source_class_dir}\")\n",
        "            continue\n",
        "\n",
        "        shutil.copytree(\n",
        "            source_class_dir,\n",
        "            target_class_dir\n",
        "        )\n",
        "\n",
        "print(\"Five-class dataset created.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7ItFYHBXIw2Q",
        "outputId": "389e7126-cb07-4fc4-f151-bd843f2ff390"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Five-class dataset created.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for split in splits:\n",
        "    print(f\"\\n{split.upper()}\")\n",
        "\n",
        "    total = 0\n",
        "\n",
        "    for class_name in selected_classes:\n",
        "        class_path = os.path.join(\n",
        "            target_dir,\n",
        "            split,\n",
        "            class_name\n",
        "        )\n",
        "\n",
        "        image_count = len([\n",
        "            file_name\n",
        "            for file_name in os.listdir(class_path)\n",
        "            if os.path.isfile(\n",
        "                os.path.join(class_path, file_name)\n",
        "            )\n",
        "        ])\n",
        "\n",
        "        total += image_count\n",
        "\n",
        "        print(f\"{class_name}: {image_count}\")\n",
        "\n",
        "    print(f\"Total: {total}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Vd601LX1I72b",
        "outputId": "48f495ae-e846-43ac-d901-aa9c399e1508"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "TRAIN\n",
            "healthy: 2074\n",
            "scab: 2334\n",
            "frog_eye_leaf_spot: 1448\n",
            "rust: 864\n",
            "powdery_mildew: 587\n",
            "Total: 7307\n",
            "\n",
            "VALID\n",
            "healthy: 444\n",
            "scab: 500\n",
            "frog_eye_leaf_spot: 310\n",
            "rust: 185\n",
            "powdery_mildew: 125\n",
            "Total: 1564\n",
            "\n",
            "TEST\n",
            "healthy: 445\n",
            "scab: 500\n",
            "frog_eye_leaf_spot: 310\n",
            "rust: 186\n",
            "powdery_mildew: 126\n",
            "Total: 1567\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 4. TensorFlow Data Pipeline\n",
        "\n",
        "The images are loaded at 512 x 512 resolution."
      ],
      "metadata": {
        "id": "I-1YKL2n8Dc0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "\n",
        "IMG_SIZE = (512, 512)\n",
        "BATCH_SIZE = 32\n",
        "\n",
        "train_dir = \"/content/PlantPathology_5Classes/train\"\n",
        "valid_dir = \"/content/PlantPathology_5Classes/valid\"\n",
        "test_dir = \"/content/PlantPathology_5Classes/test\"\n",
        "\n",
        "train_ds = tf.keras.utils.image_dataset_from_directory(\n",
        "    train_dir,\n",
        "    image_size=IMG_SIZE,\n",
        "    batch_size=BATCH_SIZE,\n",
        "    label_mode=\"int\",\n",
        "    shuffle=True,\n",
        "    seed=42\n",
        ")\n",
        "\n",
        "valid_ds = tf.keras.utils.image_dataset_from_directory(\n",
        "    valid_dir,\n",
        "    image_size=IMG_SIZE,\n",
        "    batch_size=BATCH_SIZE,\n",
        "    label_mode=\"int\",\n",
        "    shuffle=False\n",
        ")\n",
        "\n",
        "test_ds = tf.keras.utils.image_dataset_from_directory(\n",
        "    test_dir,\n",
        "    image_size=IMG_SIZE,\n",
        "    batch_size=BATCH_SIZE,\n",
        "    label_mode=\"int\",\n",
        "    shuffle=False\n",
        ")\n",
        "\n",
        "class_names = train_ds.class_names\n",
        "\n",
        "print(\"\\nClasses:\")\n",
        "print(class_names)\n",
        "\n",
        "print(\"\\nNumber of classes:\", len(class_names))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JVa-slm2JAtn",
        "outputId": "2ca26c14-2623-4a7f-b24a-f3d9b0362698"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 7307 files belonging to 5 classes.\n",
            "Found 1564 files belonging to 5 classes.\n",
            "Found 1567 files belonging to 5 classes.\n",
            "\n",
            "Classes:\n",
            "['frog_eye_leaf_spot', 'healthy', 'powdery_mildew', 'rust', 'scab']\n",
            "\n",
            "Number of classes: 5\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 5. Dataset Performance Optimisation\n",
        "\n",
        "Prefetching is used to improve data-loading performance."
      ],
      "metadata": {
        "id": "dnQISBaE8IkH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "AUTOTUNE = tf.data.AUTOTUNE\n",
        "\n",
        "train_ds = train_ds.prefetch(\n",
        "    buffer_size=AUTOTUNE\n",
        ")\n",
        "\n",
        "valid_ds = valid_ds.prefetch(\n",
        "    buffer_size=AUTOTUNE\n",
        ")\n",
        "\n",
        "test_ds = test_ds.prefetch(\n",
        "    buffer_size=AUTOTUNE\n",
        ")"
      ],
      "metadata": {
        "id": "DAiQ_LIXJgZG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 6. Data Augmentation\n",
        "\n",
        "Random transformations are applied to reduce overfitting.\n"
      ],
      "metadata": {
        "id": "1bbj7Ux-Q2l3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras import layers, models\n",
        "\n",
        "data_augmentation = models.Sequential([\n",
        "    layers.RandomFlip(\"horizontal\"),\n",
        "    layers.RandomRotation(0.1),\n",
        "    layers.RandomZoom(0.1)\n",
        "], name=\"data_augmentation\")"
      ],
      "metadata": {
        "id": "KEoPAuaESM3f"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 7. Final CNN Architecture\n",
        "\n",
        "The final CNN contains multiple convolutional layers for feature extraction."
      ],
      "metadata": {
        "id": "74rwYnFFSTjN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras import layers, models\n",
        "\n",
        "num_classes = len(class_names)\n",
        "\n",
        "experiment5_model = models.Sequential([\n",
        "    layers.Input(shape=(512, 512, 3)),\n",
        "\n",
        "    data_augmentation,\n",
        "\n",
        "    layers.Rescaling(1./255),\n",
        "\n",
        "    layers.Conv2D(32, 3, activation=\"relu\"),\n",
        "    layers.MaxPooling2D(),\n",
        "\n",
        "    layers.Conv2D(64, 3, activation=\"relu\"),\n",
        "    layers.MaxPooling2D(),\n",
        "\n",
        "    layers.Conv2D(128, 3, activation=\"relu\"),\n",
        "    layers.MaxPooling2D(),\n",
        "\n",
        "    layers.Conv2D(256, 3, activation=\"relu\"),\n",
        "    layers.MaxPooling2D(),\n",
        "\n",
        "    layers.Flatten(),\n",
        "\n",
        "    layers.Dense(128, activation=\"relu\"),\n",
        "    layers.Dropout(0.3),\n",
        "\n",
        "    layers.Dense(num_classes, activation=\"softmax\")\n",
        "], name=\"experiment5_cnn\")\n",
        "\n",
        "experiment5_model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 577
        },
        "id": "P5ON57sTkftU",
        "outputId": "94a0c964-f040-4d84-e598-adae7de17002"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1mModel: \"experiment5_cnn\"\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: \"experiment5_cnn\"</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",
              "│ data_augmentation (\u001b[38;5;33mSequential\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m512\u001b[0m, \u001b[38;5;34m512\u001b[0m, \u001b[38;5;34m3\u001b[0m)    │             \u001b[38;5;34m0\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ rescaling_1 (\u001b[38;5;33mRescaling\u001b[0m)         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m512\u001b[0m, \u001b[38;5;34m512\u001b[0m, \u001b[38;5;34m3\u001b[0m)    │             \u001b[38;5;34m0\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv2d_4 (\u001b[38;5;33mConv2D\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m510\u001b[0m, \u001b[38;5;34m510\u001b[0m, \u001b[38;5;34m32\u001b[0m)   │           \u001b[38;5;34m896\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ max_pooling2d_4 (\u001b[38;5;33mMaxPooling2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m255\u001b[0m, \u001b[38;5;34m255\u001b[0m, \u001b[38;5;34m32\u001b[0m)   │             \u001b[38;5;34m0\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv2d_5 (\u001b[38;5;33mConv2D\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m253\u001b[0m, \u001b[38;5;34m253\u001b[0m, \u001b[38;5;34m64\u001b[0m)   │        \u001b[38;5;34m18,496\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ max_pooling2d_5 (\u001b[38;5;33mMaxPooling2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m126\u001b[0m, \u001b[38;5;34m126\u001b[0m, \u001b[38;5;34m64\u001b[0m)   │             \u001b[38;5;34m0\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv2d_6 (\u001b[38;5;33mConv2D\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m124\u001b[0m, \u001b[38;5;34m124\u001b[0m, \u001b[38;5;34m128\u001b[0m)  │        \u001b[38;5;34m73,856\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ max_pooling2d_6 (\u001b[38;5;33mMaxPooling2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m62\u001b[0m, \u001b[38;5;34m62\u001b[0m, \u001b[38;5;34m128\u001b[0m)    │             \u001b[38;5;34m0\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv2d_7 (\u001b[38;5;33mConv2D\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m60\u001b[0m, \u001b[38;5;34m60\u001b[0m, \u001b[38;5;34m256\u001b[0m)    │       \u001b[38;5;34m295,168\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ max_pooling2d_7 (\u001b[38;5;33mMaxPooling2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m30\u001b[0m, \u001b[38;5;34m30\u001b[0m, \u001b[38;5;34m256\u001b[0m)    │             \u001b[38;5;34m0\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ flatten_1 (\u001b[38;5;33mFlatten\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m230400\u001b[0m)         │             \u001b[38;5;34m0\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dense_2 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)            │    \u001b[38;5;34m29,491,328\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dropout_1 (\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_3 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m)              │           \u001b[38;5;34m645\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",
              "│ data_augmentation (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Sequential</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>)    │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ rescaling_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Rescaling</span>)         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>)    │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv2d_4 (<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\">510</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">510</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)   │           <span style=\"color: #00af00; text-decoration-color: #00af00\">896</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ max_pooling2d_4 (<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\">255</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">255</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)   │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv2d_5 (<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\">253</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">253</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)   │        <span style=\"color: #00af00; text-decoration-color: #00af00\">18,496</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ max_pooling2d_5 (<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\">126</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">126</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)   │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv2d_6 (<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\">124</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">124</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)  │        <span style=\"color: #00af00; text-decoration-color: #00af00\">73,856</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ max_pooling2d_6 (<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\">62</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">62</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)    │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv2d_7 (<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\">60</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">60</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)    │       <span style=\"color: #00af00; text-decoration-color: #00af00\">295,168</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ max_pooling2d_7 (<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\">30</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">30</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)    │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ flatten_1 (<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\">230400</span>)         │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dense_2 (<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\">29,491,328</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dropout_1 (<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_3 (<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\">5</span>)              │           <span style=\"color: #00af00; text-decoration-color: #00af00\">645</span> │\n",
              "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m29,880,389\u001b[0m (113.98 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\">29,880,389</span> (113.98 MB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m29,880,389\u001b[0m (113.98 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\">29,880,389</span> (113.98 MB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 8. Model Compilation\n",
        "\n",
        "The model is compiled using Adam and categorical classification loss."
      ],
      "metadata": {
        "id": "jvebUxgvmfZ3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.optimizers import Adam\n",
        "\n",
        "experiment5_model.compile(\n",
        "    optimizer=Adam(learning_rate=0.0003),\n",
        "    loss=\"sparse_categorical_crossentropy\",\n",
        "    metrics=[\"accuracy\"]\n",
        ")"
      ],
      "metadata": {
        "id": "W_UYNLmFmt7U"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 9. Training Callbacks\n",
        "\n",
        "Callbacks are used to save the best model and control training."
      ],
      "metadata": {
        "id": "Dma-yagpTsAt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.callbacks import (\n",
        "    EarlyStopping,\n",
        "    ModelCheckpoint\n",
        ")\n",
        "\n",
        "early_stop = EarlyStopping(\n",
        "    monitor=\"val_loss\",\n",
        "    patience=5,\n",
        "    restore_best_weights=True,\n",
        "    verbose=1\n",
        ")\n",
        "\n",
        "checkpoint = ModelCheckpoint(\n",
        "    filepath=\"/content/drive/MyDrive/experiment5_cnn_best.keras\",\n",
        "    monitor=\"val_loss\",\n",
        "    save_best_only=True,\n",
        "    verbose=0\n",
        ")\n",
        "\n",
        "callbacks_experiment5 = [\n",
        "    early_stop,\n",
        "    checkpoint\n",
        "]"
      ],
      "metadata": {
        "id": "TDUEPMO3mzPY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 10. Model Training\n",
        "\n",
        "The model is trained using the training and validation datasets.\n",
        "\n"
      ],
      "metadata": {
        "id": "A01eBBYNm7KH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "history_experiment5 = experiment5_model.fit(\n",
        "    train_ds,\n",
        "    validation_data=valid_ds,\n",
        "    epochs=30,\n",
        "    callbacks=callbacks_experiment5,\n",
        "    verbose=1\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sCV2uCaNnGNL",
        "outputId": "5e584702-a15b-4ce3-a646-a8a38dddbd4c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/30\n",
            "\u001b[1m229/229\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m83s\u001b[0m 353ms/step - accuracy: 0.3944 - loss: 1.3756 - val_accuracy: 0.4527 - val_loss: 1.2536\n",
            "Epoch 2/30\n",
            "\u001b[1m229/229\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m77s\u001b[0m 336ms/step - accuracy: 0.5656 - loss: 1.0773 - val_accuracy: 0.6394 - val_loss: 0.9073\n",
            "Epoch 3/30\n",
            "\u001b[1m229/229\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m77s\u001b[0m 334ms/step - accuracy: 0.6510 - loss: 0.8695 - val_accuracy: 0.6605 - val_loss: 0.7936\n",
            "Epoch 4/30\n",
            "\u001b[1m229/229\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m72s\u001b[0m 315ms/step - accuracy: 0.6948 - loss: 0.7668 - val_accuracy: 0.7065 - val_loss: 0.6965\n",
            "Epoch 5/30\n",
            "\u001b[1m229/229\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m86s\u001b[0m 331ms/step - accuracy: 0.7211 - loss: 0.7067 - val_accuracy: 0.7251 - val_loss: 0.6440\n",
            "Epoch 6/30\n",
            "\u001b[1m229/229\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m77s\u001b[0m 334ms/step - accuracy: 0.7366 - loss: 0.6629 - val_accuracy: 0.7295 - val_loss: 0.6121\n",
            "Epoch 7/30\n",
            "\u001b[1m229/229\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m70s\u001b[0m 305ms/step - accuracy: 0.7517 - loss: 0.6198 - val_accuracy: 0.7295 - val_loss: 0.6793\n",
            "Epoch 8/30\n",
            "\u001b[1m229/229\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m75s\u001b[0m 325ms/step - accuracy: 0.7641 - loss: 0.5856 - val_accuracy: 0.7276 - val_loss: 0.6762\n",
            "Epoch 9/30\n",
            "\u001b[1m229/229\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m72s\u001b[0m 313ms/step - accuracy: 0.7797 - loss: 0.5537 - val_accuracy: 0.7455 - val_loss: 0.5753\n",
            "Epoch 10/30\n",
            "\u001b[1m229/229\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m80s\u001b[0m 306ms/step - accuracy: 0.7840 - loss: 0.5445 - val_accuracy: 0.7807 - val_loss: 0.5942\n",
            "Epoch 11/30\n",
            "\u001b[1m229/229\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m76s\u001b[0m 334ms/step - accuracy: 0.7980 - loss: 0.5007 - val_accuracy: 0.7839 - val_loss: 0.5418\n",
            "Epoch 12/30\n",
            "\u001b[1m229/229\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m77s\u001b[0m 335ms/step - accuracy: 0.8137 - loss: 0.4773 - val_accuracy: 0.8370 - val_loss: 0.4341\n",
            "Epoch 13/30\n",
            "\u001b[1m229/229\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m72s\u001b[0m 313ms/step - accuracy: 0.8396 - loss: 0.4233 - val_accuracy: 0.8446 - val_loss: 0.4597\n",
            "Epoch 14/30\n",
            "\u001b[1m229/229\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m76s\u001b[0m 332ms/step - accuracy: 0.8463 - loss: 0.4133 - val_accuracy: 0.8587 - val_loss: 0.4237\n",
            "Epoch 15/30\n",
            "\u001b[1m229/229\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m77s\u001b[0m 334ms/step - accuracy: 0.8656 - loss: 0.3646 - val_accuracy: 0.8676 - val_loss: 0.3645\n",
            "Epoch 16/30\n",
            "\u001b[1m229/229\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m75s\u001b[0m 325ms/step - accuracy: 0.8714 - loss: 0.3563 - val_accuracy: 0.8338 - val_loss: 0.4608\n",
            "Epoch 17/30\n",
            "\u001b[1m229/229\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m76s\u001b[0m 332ms/step - accuracy: 0.8931 - loss: 0.3001 - val_accuracy: 0.8855 - val_loss: 0.3205\n",
            "Epoch 18/30\n",
            "\u001b[1m229/229\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m72s\u001b[0m 313ms/step - accuracy: 0.8985 - loss: 0.2882 - val_accuracy: 0.8696 - val_loss: 0.3735\n",
            "Epoch 19/30\n",
            "\u001b[1m229/229\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m83s\u001b[0m 316ms/step - accuracy: 0.9006 - loss: 0.2835 - val_accuracy: 0.9041 - val_loss: 0.2827\n",
            "Epoch 20/30\n",
            "\u001b[1m229/229\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m74s\u001b[0m 324ms/step - accuracy: 0.9071 - loss: 0.2661 - val_accuracy: 0.9047 - val_loss: 0.2955\n",
            "Epoch 21/30\n",
            "\u001b[1m229/229\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m76s\u001b[0m 332ms/step - accuracy: 0.9210 - loss: 0.2366 - val_accuracy: 0.9022 - val_loss: 0.2767\n",
            "Epoch 22/30\n",
            "\u001b[1m229/229\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m74s\u001b[0m 322ms/step - accuracy: 0.9228 - loss: 0.2250 - val_accuracy: 0.9124 - val_loss: 0.2646\n",
            "Epoch 23/30\n",
            "\u001b[1m229/229\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m80s\u001b[0m 315ms/step - accuracy: 0.9265 - loss: 0.2222 - val_accuracy: 0.9022 - val_loss: 0.2777\n",
            "Epoch 24/30\n",
            "\u001b[1m229/229\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m72s\u001b[0m 316ms/step - accuracy: 0.9287 - loss: 0.2005 - val_accuracy: 0.9060 - val_loss: 0.2952\n",
            "Epoch 25/30\n",
            "\u001b[1m229/229\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m71s\u001b[0m 309ms/step - accuracy: 0.9331 - loss: 0.1984 - val_accuracy: 0.9028 - val_loss: 0.3027\n",
            "Epoch 26/30\n",
            "\u001b[1m229/229\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m74s\u001b[0m 323ms/step - accuracy: 0.9383 - loss: 0.1872 - val_accuracy: 0.9073 - val_loss: 0.2681\n",
            "Epoch 27/30\n",
            "\u001b[1m229/229\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m74s\u001b[0m 321ms/step - accuracy: 0.9387 - loss: 0.1805 - val_accuracy: 0.9156 - val_loss: 0.2462\n",
            "Epoch 28/30\n",
            "\u001b[1m229/229\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m80s\u001b[0m 313ms/step - accuracy: 0.9399 - loss: 0.1708 - val_accuracy: 0.9073 - val_loss: 0.2885\n",
            "Epoch 29/30\n",
            "\u001b[1m229/229\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m75s\u001b[0m 327ms/step - accuracy: 0.9425 - loss: 0.1621 - val_accuracy: 0.9277 - val_loss: 0.2277\n",
            "Epoch 30/30\n",
            "\u001b[1m229/229\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m74s\u001b[0m 324ms/step - accuracy: 0.9468 - loss: 0.1504 - val_accuracy: 0.9309 - val_loss: 0.2258\n",
            "Restoring model weights from the end of the best epoch: 30.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 11. Training Results\n",
        "\n",
        "The model achieved strong training and validation performance."
      ],
      "metadata": {
        "id": "5L_2yO0EfEsf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from IPython.display import display\n",
        "\n",
        "max_epochs = 30\n",
        "epochs_completed = len(history_experiment5.history[\"loss\"])\n",
        "\n",
        "# Best epoch based on the lowest validation loss\n",
        "best_epoch = int(\n",
        "    np.argmin(history_experiment5.history[\"val_loss\"]) + 1\n",
        ")\n",
        "\n",
        "best_val_loss = history_experiment5.history[\"val_loss\"][best_epoch - 1]\n",
        "best_val_accuracy = history_experiment5.history[\"val_accuracy\"][best_epoch - 1]\n",
        "\n",
        "train_loss_at_best_epoch = history_experiment5.history[\"loss\"][best_epoch - 1]\n",
        "train_accuracy_at_best_epoch = history_experiment5.history[\"accuracy\"][best_epoch - 1]\n",
        "\n",
        "# Highest validation accuracy\n",
        "highest_val_accuracy = max(\n",
        "    history_experiment5.history[\"val_accuracy\"]\n",
        ")\n",
        "\n",
        "highest_val_accuracy_epoch = int(\n",
        "    np.argmax(history_experiment5.history[\"val_accuracy\"]) + 1\n",
        ")\n",
        "\n",
        "# EarlyStopping and checkpoint\n",
        "early_stopping_activated = epochs_completed < max_epochs\n",
        "\n",
        "checkpoint_path = (\n",
        "    \"/content/drive/MyDrive/experiment5_cnn_best.keras\"\n",
        ")\n",
        "\n",
        "checkpoint_saved = os.path.exists(checkpoint_path)\n",
        "\n",
        "summary_table = pd.DataFrame({\n",
        "    \"Metric\": [\n",
        "        \"Maximum Epochs\",\n",
        "        \"Epochs Completed\",\n",
        "        \"Early Stopping Activated\",\n",
        "        \"Best Checkpoint Epoch\",\n",
        "        \"Training Accuracy at Best Epoch\",\n",
        "        \"Validation Accuracy at Best Epoch\",\n",
        "        \"Training Loss at Best Epoch\",\n",
        "        \"Best Validation Loss\",\n",
        "        \"Highest Validation Accuracy\",\n",
        "        \"Highest Validation Accuracy Epoch\",\n",
        "        \"Best Checkpoint Saved\"\n",
        "    ],\n",
        "    \"Result\": [\n",
        "        max_epochs,\n",
        "        epochs_completed,\n",
        "        \"Yes\" if early_stopping_activated else \"No\",\n",
        "        best_epoch,\n",
        "        f\"{train_accuracy_at_best_epoch * 100:.2f}%\",\n",
        "        f\"{best_val_accuracy * 100:.2f}%\",\n",
        "        f\"{train_loss_at_best_epoch:.4f}\",\n",
        "        f\"{best_val_loss:.4f}\",\n",
        "        f\"{highest_val_accuracy * 100:.2f}%\",\n",
        "        highest_val_accuracy_epoch,\n",
        "        \"Yes\" if checkpoint_saved else \"No\"\n",
        "    ]\n",
        "})\n",
        "\n",
        "display(\n",
        "    summary_table.style\n",
        "    .set_caption(\n",
        "        \"Experiment 5 CNN with Additional Convolutional Block Results\"\n",
        "    )\n",
        "    .hide(axis=\"index\")\n",
        "    .set_properties(**{\n",
        "        \"text-align\": \"center\",\n",
        "        \"font-size\": \"14px\"\n",
        "    })\n",
        "    .set_table_styles([\n",
        "        {\n",
        "            \"selector\": \"caption\",\n",
        "            \"props\": [\n",
        "                (\"font-size\", \"15px\"),\n",
        "                (\"font-weight\", \"bold\"),\n",
        "                (\"text-align\", \"left\"),\n",
        "                (\"padding-bottom\", \"10px\"),\n",
        "                (\"white-space\", \"nowrap\")\n",
        "            ]\n",
        "        },\n",
        "        {\n",
        "            \"selector\": \"th\",\n",
        "            \"props\": [\n",
        "                (\"border\", \"1px solid black\"),\n",
        "                (\"font-weight\", \"bold\"),\n",
        "                (\"text-align\", \"center\"),\n",
        "                (\"padding\", \"9px\")\n",
        "            ]\n",
        "        },\n",
        "        {\n",
        "            \"selector\": \"td\",\n",
        "            \"props\": [\n",
        "                (\"border\", \"1px solid black\"),\n",
        "                (\"padding\", \"9px\")\n",
        "            ]\n",
        "        }\n",
        "    ])\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 481
        },
        "id": "1czy2PEbNj6F",
        "outputId": "c9cc34b3-6731-41bc-fffe-4e6b13253fc6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7b99148a5d00>"
            ],
            "text/html": [
              "<style type=\"text/css\">\n",
              "#T_defff caption {\n",
              "  font-size: 15px;\n",
              "  font-weight: bold;\n",
              "  text-align: left;\n",
              "  padding-bottom: 10px;\n",
              "  white-space: nowrap;\n",
              "}\n",
              "#T_defff th {\n",
              "  border: 1px solid black;\n",
              "  font-weight: bold;\n",
              "  text-align: center;\n",
              "  padding: 9px;\n",
              "}\n",
              "#T_defff td {\n",
              "  border: 1px solid black;\n",
              "  padding: 9px;\n",
              "}\n",
              "#T_defff_row0_col0, #T_defff_row0_col1, #T_defff_row1_col0, #T_defff_row1_col1, #T_defff_row2_col0, #T_defff_row2_col1, #T_defff_row3_col0, #T_defff_row3_col1, #T_defff_row4_col0, #T_defff_row4_col1, #T_defff_row5_col0, #T_defff_row5_col1, #T_defff_row6_col0, #T_defff_row6_col1, #T_defff_row7_col0, #T_defff_row7_col1, #T_defff_row8_col0, #T_defff_row8_col1, #T_defff_row9_col0, #T_defff_row9_col1, #T_defff_row10_col0, #T_defff_row10_col1 {\n",
              "  text-align: center;\n",
              "  font-size: 14px;\n",
              "}\n",
              "</style>\n",
              "<table id=\"T_defff\" class=\"dataframe\">\n",
              "  <caption>Experiment 5 CNN with Additional Convolutional Block Results</caption>\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th id=\"T_defff_level0_col0\" class=\"col_heading level0 col0\" >Metric</th>\n",
              "      <th id=\"T_defff_level0_col1\" class=\"col_heading level0 col1\" >Result</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td id=\"T_defff_row0_col0\" class=\"data row0 col0\" >Maximum Epochs</td>\n",
              "      <td id=\"T_defff_row0_col1\" class=\"data row0 col1\" >30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_defff_row1_col0\" class=\"data row1 col0\" >Epochs Completed</td>\n",
              "      <td id=\"T_defff_row1_col1\" class=\"data row1 col1\" >30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_defff_row2_col0\" class=\"data row2 col0\" >Early Stopping Activated</td>\n",
              "      <td id=\"T_defff_row2_col1\" class=\"data row2 col1\" >No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_defff_row3_col0\" class=\"data row3 col0\" >Best Checkpoint Epoch</td>\n",
              "      <td id=\"T_defff_row3_col1\" class=\"data row3 col1\" >30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_defff_row4_col0\" class=\"data row4 col0\" >Training Accuracy at Best Epoch</td>\n",
              "      <td id=\"T_defff_row4_col1\" class=\"data row4 col1\" >94.68%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_defff_row5_col0\" class=\"data row5 col0\" >Validation Accuracy at Best Epoch</td>\n",
              "      <td id=\"T_defff_row5_col1\" class=\"data row5 col1\" >93.09%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_defff_row6_col0\" class=\"data row6 col0\" >Training Loss at Best Epoch</td>\n",
              "      <td id=\"T_defff_row6_col1\" class=\"data row6 col1\" >0.1504</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_defff_row7_col0\" class=\"data row7 col0\" >Best Validation Loss</td>\n",
              "      <td id=\"T_defff_row7_col1\" class=\"data row7 col1\" >0.2258</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_defff_row8_col0\" class=\"data row8 col0\" >Highest Validation Accuracy</td>\n",
              "      <td id=\"T_defff_row8_col1\" class=\"data row8 col1\" >93.09%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_defff_row9_col0\" class=\"data row9 col0\" >Highest Validation Accuracy Epoch</td>\n",
              "      <td id=\"T_defff_row9_col1\" class=\"data row9 col1\" >30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_defff_row10_col0\" class=\"data row10 col0\" >Best Checkpoint Saved</td>\n",
              "      <td id=\"T_defff_row10_col1\" class=\"data row10 col1\" >Yes</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 12. Training and Validation Curves\n",
        "\n",
        "The curves show stable learning with limited overfitting."
      ],
      "metadata": {
        "id": "jaVXmrCwgw9v"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "epochs = range(\n",
        "    1,\n",
        "    len(history_experiment5.history[\"accuracy\"]) + 1\n",
        ")\n",
        "\n",
        "best_epoch = int(\n",
        "    np.argmin(history_experiment5.history[\"val_loss\"]) + 1\n",
        ")\n",
        "\n",
        "plt.figure(figsize=(12, 5))\n",
        "\n",
        "# Accuracy graph\n",
        "plt.subplot(1, 2, 1)\n",
        "\n",
        "plt.plot(\n",
        "    epochs,\n",
        "    history_experiment5.history[\"accuracy\"],\n",
        "    label=\"Training Accuracy\",\n",
        "    linewidth=2.5\n",
        ")\n",
        "\n",
        "plt.plot(\n",
        "    epochs,\n",
        "    history_experiment5.history[\"val_accuracy\"],\n",
        "    label=\"Validation Accuracy\",\n",
        "    linewidth=2.5\n",
        ")\n",
        "\n",
        "plt.axvline(\n",
        "    best_epoch,\n",
        "    linestyle=\"--\",\n",
        "    label=f\"Best Epoch ({best_epoch})\"\n",
        ")\n",
        "\n",
        "plt.title(\"experiment 5 CNN Accuracy\")\n",
        "plt.xlabel(\"Epoch\")\n",
        "plt.ylabel(\"Accuracy\")\n",
        "plt.xticks(epochs)\n",
        "plt.legend()\n",
        "plt.grid(alpha=0.3)\n",
        "\n",
        "# Loss graph\n",
        "plt.subplot(1, 2, 2)\n",
        "\n",
        "plt.plot(\n",
        "    epochs,\n",
        "    history_experiment5.history[\"loss\"],\n",
        "    label=\"Training Loss\",\n",
        "    linewidth=2.5\n",
        ")\n",
        "\n",
        "plt.plot(\n",
        "    epochs,\n",
        "    history_experiment5.history[\"val_loss\"],\n",
        "    label=\"Validation Loss\",\n",
        "    linewidth=2.5\n",
        ")\n",
        "\n",
        "plt.axvline(\n",
        "    best_epoch,\n",
        "    linestyle=\"--\",\n",
        "    label=f\"Best Epoch ({best_epoch})\"\n",
        ")\n",
        "\n",
        "plt.title(\"experiment 5 CNN Loss\")\n",
        "plt.xlabel(\"Epoch\")\n",
        "plt.ylabel(\"Loss\")\n",
        "plt.xticks(epochs)\n",
        "plt.legend()\n",
        "plt.grid(alpha=0.3)\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 507
        },
        "id": "XAX2KmjuNoNE",
        "outputId": "6d1d3703-a18b-4169-aad4-4fa568a6f5ea"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x500 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 13. Final Test Setup\n",
        "\n",
        "The independent test dataset is prepared for final evaluation."
      ],
      "metadata": {
        "id": "b7AYi7N_87oP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "final_model = tf.keras.models.load_model(MODEL_PATH)\n",
        "\n",
        "test_ds = tf.keras.utils.image_dataset_from_directory(\n",
        "    TEST_DIR,\n",
        "    image_size=IMG_SIZE,\n",
        "    batch_size=BATCH_SIZE,\n",
        "    shuffle=False\n",
        ")\n",
        "\n",
        "class_names = test_ds.class_names\n",
        "\n",
        "print(\"\\nClass order:\", class_names)\n",
        "\n",
        "test_ds = test_ds.prefetch(tf.data.AUTOTUNE)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XAKlTKnWoR-W",
        "outputId": "a2b5fa3b-9cd5-4149-f40c-4ba397a68103"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 1567 files belonging to 5 classes.\n",
            "\n",
            "Class order: ['frog_eye_leaf_spot', 'healthy', 'powdery_mildew', 'rust', 'scab']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 15. Final Test Evaluation\n",
        "\n",
        "The final model achieved 93.87% test accuracy."
      ],
      "metadata": {
        "id": "TxRElqeB9Itr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "test_loss, test_accuracy = final_model.evaluate(\n",
        "    test_ds,\n",
        "    verbose=1\n",
        ")\n",
        "\n",
        "print(\"\\nFINAL TEST RESULTS\")\n",
        "print(f\"Test Loss: {test_loss:.4f}\")\n",
        "print(f\"Test Accuracy: {test_accuracy * 100:.2f}%\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kHV-bikGoY08",
        "outputId": "9df49d7f-d5f4-4744-8c02-19cbad3b984c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m49/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 103ms/step - accuracy: 0.9387 - loss: 0.1940\n",
            "\n",
            "FINAL TEST RESULTS\n",
            "Test Loss: 0.1940\n",
            "Test Accuracy: 93.87%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 16. Classification Report\n",
        "\n",
        "The model achieved strong results across all five classes."
      ],
      "metadata": {
        "id": "B7bbwWdz9O11"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "prediction_probabilities = final_model.predict(\n",
        "    test_ds,\n",
        "    verbose=1\n",
        ")\n",
        "\n",
        "y_pred = np.argmax(prediction_probabilities, axis=1)\n",
        "\n",
        "y_true = np.concatenate(\n",
        "    [labels.numpy() for images, labels in test_ds],\n",
        "    axis=0\n",
        ")\n",
        "\n",
        "report = classification_report(\n",
        "    y_true,\n",
        "    y_pred,\n",
        "    target_names=class_names,\n",
        "    digits=4,\n",
        "    zero_division=0,\n",
        "    output_dict=True\n",
        ")\n",
        "\n",
        "report_df = pd.DataFrame(report).transpose()\n",
        "\n",
        "display(report_df.round(4))\n",
        "\n",
        "report_df.to_csv(\n",
        "    os.path.join(RESULTS_DIR, \"classification_report.csv\")\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 318
        },
        "id": "tL3yxwMYogOG",
        "outputId": "372186c4-a232-4a1a-991e-5ef048976961"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m49/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 170ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                    precision  recall  f1-score    support\n",
              "frog_eye_leaf_spot     0.9388  0.9903    0.9639   310.0000\n",
              "healthy                0.9231  0.9438    0.9333   445.0000\n",
              "powdery_mildew         0.8741  0.9365    0.9042   126.0000\n",
              "rust                   0.9669  0.9409    0.9537   186.0000\n",
              "scab                   0.9616  0.9020    0.9309   500.0000\n",
              "accuracy               0.9387  0.9387    0.9387     0.9387\n",
              "macro avg              0.9329  0.9427    0.9372  1567.0000\n",
              "weighted avg           0.9397  0.9387    0.9387  1567.0000"
            ],
            "text/html": [
              "\n",
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              "    <div>\n",
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              "    }\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>precision</th>\n",
              "      <th>recall</th>\n",
              "      <th>f1-score</th>\n",
              "      <th>support</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>frog_eye_leaf_spot</th>\n",
              "      <td>0.9388</td>\n",
              "      <td>0.9903</td>\n",
              "      <td>0.9639</td>\n",
              "      <td>310.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>healthy</th>\n",
              "      <td>0.9231</td>\n",
              "      <td>0.9438</td>\n",
              "      <td>0.9333</td>\n",
              "      <td>445.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>powdery_mildew</th>\n",
              "      <td>0.8741</td>\n",
              "      <td>0.9365</td>\n",
              "      <td>0.9042</td>\n",
              "      <td>126.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>rust</th>\n",
              "      <td>0.9669</td>\n",
              "      <td>0.9409</td>\n",
              "      <td>0.9537</td>\n",
              "      <td>186.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>scab</th>\n",
              "      <td>0.9616</td>\n",
              "      <td>0.9020</td>\n",
              "      <td>0.9309</td>\n",
              "      <td>500.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>accuracy</th>\n",
              "      <td>0.9387</td>\n",
              "      <td>0.9387</td>\n",
              "      <td>0.9387</td>\n",
              "      <td>0.9387</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>macro avg</th>\n",
              "      <td>0.9329</td>\n",
              "      <td>0.9427</td>\n",
              "      <td>0.9372</td>\n",
              "      <td>1567.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>weighted avg</th>\n",
              "      <td>0.9397</td>\n",
              "      <td>0.9387</td>\n",
              "      <td>0.9387</td>\n",
              "      <td>1567.0000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "    }\n",
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              "\n",
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              "      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",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-7dccb71a-7f97-4c18-9750-a77616518db3 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-7dccb71a-7f97-4c18-9750-a77616518db3');\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",
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              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "type": "dataframe",
              "summary": "{\n  \"name\": \")\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"precision\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.02835493154386275,\n        \"min\": 0.8741,\n        \"max\": 0.9669,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.9231,\n          0.9387,\n          0.9388\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"recall\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.023872099674246125,\n        \"min\": 0.902,\n        \"max\": 0.9903,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          0.9903,\n          0.9438,\n          0.9387\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"f1-score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.017453673375129777,\n        \"min\": 0.9042,\n        \"max\": 0.9639,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          0.9639,\n          0.9333,\n          0.9387\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"support\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 625.8762359213999,\n        \"min\": 0.9387,\n        \"max\": 1567.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          310.0,\n          445.0,\n          0.9387\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 17. Confusion Matrix\n",
        "\n",
        "Most test images were classified correctly."
      ],
      "metadata": {
        "id": "d9UgG9f99T8K"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "cm = confusion_matrix(y_true, y_pred)\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(10, 8))\n",
        "\n",
        "disp = ConfusionMatrixDisplay(\n",
        "    confusion_matrix=cm,\n",
        "    display_labels=class_names\n",
        ")\n",
        "\n",
        "disp.plot(\n",
        "    ax=ax,\n",
        "    cmap=\"Blues\",\n",
        "    values_format=\"d\",\n",
        "    colorbar=False\n",
        ")\n",
        "\n",
        "plt.title(\"Final CNN Test Confusion Matrix\")\n",
        "plt.xlabel(\"Predicted Class\")\n",
        "plt.ylabel(\"True Class\")\n",
        "plt.xticks(rotation=35, ha=\"right\")\n",
        "plt.tight_layout()\n",
        "\n",
        "plt.savefig(\n",
        "    os.path.join(RESULTS_DIR, \"final_test_confusion_matrix.png\"),\n",
        "    dpi=300,\n",
        "    bbox_inches=\"tight\"\n",
        ")\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "n4qpmhpGox-j",
        "outputId": "12e5651f-5704-4d82-bf32-726f93e4c729",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 807
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}