{
 "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",
   "metadata": {},
   "source": [
    "## 1. Data Access and File Verification"
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "from google.colab import drive\n",
    "drive.mount('/content/drive')"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "U7UVgM_Ou476",
    "outputId": "033ba2fa-469c-41cf-efda-4c5f789026d9"
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "J3g07eDFqzmY",
    "outputId": "4f9b0208-d206-4570-bee7-eac47b1590da"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "True\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "print(os.path.exists(\"/content/drive/MyDrive/PlantPathology_Split.zip\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Dataset Extraction and Directory Check"
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "!unzip -q \"/content/drive/MyDrive/PlantPathology_Split.zip\" -d \"/content/\""
   ],
   "metadata": {
    "id": "HDCNqa4xwaq5",
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "outputId": "f0ad64d2-d04f-4dbf-c51e-084d5f3ba3a3"
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "replace /content/PlantPathology_Split/test/complex/8052c7272b3b3f5c.jpg? [y]es, [n]o, [A]ll, [N]one, [r]ename: "
     ]
    }
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Dataset Loading and Configuration"
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "!ls \"/content/PlantPathology_Split\""
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "241jCc1XyfV6",
    "outputId": "69b7f379-0077-48eb-ce4d-df48e7fce21f"
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "test  train  valid\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "# Load libraries\n",
    "import os\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras import layers, models\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Image settings\n",
    "IMG_SIZE = (512, 512)\n",
    "BATCH_SIZE = 32\n",
    "\n",
    "# Required five classes\n",
    "selected_classes = [\n",
    "    \"frog_eye_leaf_spot\",\n",
    "    \"healthy\",\n",
    "    \"powdery_mildew\",\n",
    "    \"rust\",\n",
    "    \"scab\"\n",
    "]\n",
    "\n",
    "train_ds = tf.keras.utils.image_dataset_from_directory(\n",
    "    \"/content/PlantPathology_Split/train\",\n",
    "    class_names=selected_classes,\n",
    "    image_size=IMG_SIZE,\n",
    "    batch_size=BATCH_SIZE,\n",
    "    label_mode=\"int\"\n",
    ")\n",
    "\n",
    "valid_ds = tf.keras.utils.image_dataset_from_directory(\n",
    "    \"/content/PlantPathology_Split/valid\",\n",
    "    class_names=selected_classes,\n",
    "    image_size=IMG_SIZE,\n",
    "    batch_size=BATCH_SIZE,\n",
    "    label_mode=\"int\"\n",
    ")\n",
    "\n",
    "test_ds = tf.keras.utils.image_dataset_from_directory(\n",
    "    \"/content/PlantPathology_Split/test\",\n",
    "    class_names=selected_classes,\n",
    "    image_size=IMG_SIZE,\n",
    "    batch_size=BATCH_SIZE,\n",
    "    label_mode=\"int\",\n",
    "    shuffle=False\n",
    ")\n",
    "\n",
    "# Display selected classes\n",
    "class_names = train_ds.class_names\n",
    "\n",
    "print(\"Classes:\")\n",
    "print(class_names)"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "MFt4tEfiynlQ",
    "outputId": "d6f2bfb4-f01b-4153-8cf6-650104ea927c"
   },
   "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",
      "Classes:\n",
      "['frog_eye_leaf_spot', 'healthy', 'powdery_mildew', 'rust', 'scab']\n"
     ]
    }
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Dataset Pipeline Optimisation and Data Augmentation"
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "AUTOTUNE = tf.data.AUTOTUNE\n",
    "\n",
    "train_ds = train_ds.prefetch(AUTOTUNE)\n",
    "valid_ds = valid_ds.prefetch(AUTOTUNE)\n",
    "test_ds = test_ds.prefetch(AUTOTUNE)"
   ],
   "metadata": {
    "id": "T28L_smdztQU"
   },
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. CNN with Data Augmentation Architecture"
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "# Data augmentation\n",
    "data_augmentation = models.Sequential([\n",
    "    layers.RandomFlip(\"horizontal\"),\n",
    "    layers.RandomRotation(0.1),\n",
    "    layers.RandomZoom(0.1)\n",
    "])"
   ],
   "metadata": {
    "id": "5uyE7gahE1FM"
   },
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "# Build the Augmented CNN model\n",
    "num_classes = len(class_names)\n",
    "\n",
    "augmented_cnn_model = models.Sequential([\n",
    "    layers.Rescaling(1./255, input_shape=(512, 512, 3)),\n",
    "\n",
    "    data_augmentation,\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.Flatten(),\n",
    "\n",
    "    layers.Dense(128, activation='relu'),\n",
    "    layers.Dropout(0.5),\n",
    "\n",
    "    layers.Dense(num_classes, activation='softmax')\n",
    "])\n",
    "\n",
    "augmented_cnn_model.summary()"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 513
    },
    "id": "m-SN90ngz6xW",
    "outputId": "de299048-d8d3-4bb9-e7cb-d77b6efddee7"
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "\u001b[1mModel: \"sequential_3\"\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_3\"</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",
       "│ 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",
       "│ sequential_2 (\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",
       "│ conv2d_3 (\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_3 (\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_4 (\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_4 (\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_5 (\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_5 (\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",
       "│ flatten_1 (\u001b[38;5;33mFlatten\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m492032\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;34m62,980,224\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",
       "│ 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",
       "│ sequential_2 (<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",
       "│ conv2d_3 (<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_3 (<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_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\">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_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\">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_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\">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_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\">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",
       "│ 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\">492032</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\">62,980,224</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;34m63,074,117\u001b[0m (240.61 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\">63,074,117</span> (240.61 MB)\n",
       "</pre>\n"
      ]
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m63,074,117\u001b[0m (240.61 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\">63,074,117</span> (240.61 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",
   "metadata": {},
   "source": [
    "## 6. Model Compilation and Early Stopping"
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "# Compile the Augmented CNN model\n",
    "augmented_cnn_model.compile(\n",
    "    optimizer=tf.keras.optimizers.Adam(\n",
    "        learning_rate=0.0003\n",
    "    ),\n",
    "    loss='sparse_categorical_crossentropy',\n",
    "    metrics=['accuracy']\n",
    ")"
   ],
   "metadata": {
    "id": "syzhnwu-z8Sv"
   },
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7. Model Training"
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "checkpoint_augmented = tf.keras.callbacks.ModelCheckpoint(\n",
    "    \"/content/drive/MyDrive/augmented_cnn_best.keras\",\n",
    "    monitor=\"val_loss\",\n",
    "    save_best_only=True\n",
    ")"
   ],
   "metadata": {
    "id": "d-9ZBs5xrEDp"
   },
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "# Stop when validation loss no longer improves\n",
    "early_stopping_augmented = tf.keras.callbacks.EarlyStopping(\n",
    "    monitor='val_loss',\n",
    "    patience=8,\n",
    "    restore_best_weights=True\n",
    ")"
   ],
   "metadata": {
    "id": "i9zcvYUn71Ma"
   },
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 8. Training and Validation Performance"
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "# Train the Augmented CNN model\n",
    "history_augmented_cnn = augmented_cnn_model.fit(\n",
    "    train_ds,\n",
    "    validation_data=valid_ds,\n",
    "    epochs=40,\n",
    "    callbacks=[\n",
    "        early_stopping_augmented,\n",
    "        checkpoint_augmented\n",
    "    ]\n",
    ")"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "l9YL4e4f0ZWI",
    "outputId": "4f809493-a243-4519-ec6c-4c4d040198a0"
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Epoch 1/40\n",
      "\u001b[1m229/229\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m78s\u001b[0m 292ms/step - accuracy: 0.3487 - loss: 1.5285 - val_accuracy: 0.4648 - val_loss: 1.2865\n",
      "Epoch 2/40\n",
      "\u001b[1m229/229\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m68s\u001b[0m 298ms/step - accuracy: 0.4125 - loss: 1.3405 - val_accuracy: 0.4552 - val_loss: 1.2769\n",
      "Epoch 3/40\n",
      "\u001b[1m229/229\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m71s\u001b[0m 308ms/step - accuracy: 0.4665 - loss: 1.2510 - val_accuracy: 0.5512 - val_loss: 1.1318\n",
      "Epoch 4/40\n",
      "\u001b[1m229/229\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m68s\u001b[0m 296ms/step - accuracy: 0.5042 - loss: 1.1751 - val_accuracy: 0.5723 - val_loss: 1.0783\n",
      "Epoch 5/40\n",
      "\u001b[1m229/229\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m71s\u001b[0m 310ms/step - accuracy: 0.5239 - loss: 1.1344 - val_accuracy: 0.5774 - val_loss: 1.0252\n",
      "Epoch 6/40\n",
      "\u001b[1m229/229\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m69s\u001b[0m 301ms/step - accuracy: 0.5517 - loss: 1.0924 - val_accuracy: 0.6221 - val_loss: 0.9708\n",
      "Epoch 7/40\n",
      "\u001b[1m229/229\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m71s\u001b[0m 310ms/step - accuracy: 0.5703 - loss: 1.0552 - val_accuracy: 0.6240 - val_loss: 0.9385\n",
      "Epoch 8/40\n",
      "\u001b[1m229/229\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m73s\u001b[0m 319ms/step - accuracy: 0.5929 - loss: 1.0188 - val_accuracy: 0.6119 - val_loss: 0.9081\n",
      "Epoch 9/40\n",
      "\u001b[1m229/229\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m79s\u001b[0m 304ms/step - accuracy: 0.6174 - loss: 0.9581 - val_accuracy: 0.6471 - val_loss: 0.9004\n",
      "Epoch 10/40\n",
      "\u001b[1m229/229\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m70s\u001b[0m 307ms/step - accuracy: 0.6353 - loss: 0.9070 - val_accuracy: 0.6426 - val_loss: 0.8662\n",
      "Epoch 11/40\n",
      "\u001b[1m229/229\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m86s\u001b[0m 325ms/step - accuracy: 0.6442 - loss: 0.8856 - val_accuracy: 0.6720 - val_loss: 0.8016\n",
      "Epoch 12/40\n",
      "\u001b[1m229/229\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m92s\u001b[0m 402ms/step - accuracy: 0.6596 - loss: 0.8541 - val_accuracy: 0.6829 - val_loss: 0.7552\n",
      "Epoch 13/40\n",
      "\u001b[1m229/229\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m61s\u001b[0m 265ms/step - accuracy: 0.6725 - loss: 0.8272 - val_accuracy: 0.6375 - val_loss: 0.8772\n",
      "Epoch 14/40\n",
      "\u001b[1m229/229\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m65s\u001b[0m 285ms/step - accuracy: 0.6762 - loss: 0.7994 - val_accuracy: 0.6893 - val_loss: 0.7634\n",
      "Epoch 15/40\n",
      "\u001b[1m229/229\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m73s\u001b[0m 319ms/step - accuracy: 0.6776 - loss: 0.7995 - val_accuracy: 0.7052 - val_loss: 0.7311\n",
      "Epoch 16/40\n",
      "\u001b[1m229/229\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m82s\u001b[0m 319ms/step - accuracy: 0.6913 - loss: 0.7769 - val_accuracy: 0.7404 - val_loss: 0.6894\n",
      "Epoch 17/40\n",
      "\u001b[1m229/229\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m63s\u001b[0m 276ms/step - accuracy: 0.6977 - loss: 0.7639 - val_accuracy: 0.7142 - val_loss: 0.7325\n",
      "Epoch 18/40\n",
      "\u001b[1m229/229\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m68s\u001b[0m 296ms/step - accuracy: 0.7029 - loss: 0.7452 - val_accuracy: 0.6950 - val_loss: 0.7374\n",
      "Epoch 19/40\n",
      "\u001b[1m229/229\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m68s\u001b[0m 297ms/step - accuracy: 0.7179 - loss: 0.7164 - val_accuracy: 0.7353 - val_loss: 0.6662\n",
      "Epoch 20/40\n",
      "\u001b[1m229/229\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m76s\u001b[0m 270ms/step - accuracy: 0.7186 - loss: 0.7137 - val_accuracy: 0.7289 - val_loss: 0.6780\n",
      "Epoch 21/40\n",
      "\u001b[1m229/229\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m91s\u001b[0m 312ms/step - accuracy: 0.7200 - loss: 0.7137 - val_accuracy: 0.7334 - val_loss: 0.6652\n",
      "Epoch 22/40\n",
      "\u001b[1m229/229\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m63s\u001b[0m 274ms/step - accuracy: 0.7263 - loss: 0.6924 - val_accuracy: 0.7148 - val_loss: 0.7123\n",
      "Epoch 23/40\n",
      "\u001b[1m229/229\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m68s\u001b[0m 297ms/step - accuracy: 0.7312 - loss: 0.6776 - val_accuracy: 0.7366 - val_loss: 0.6498\n",
      "Epoch 24/40\n",
      "\u001b[1m229/229\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m64s\u001b[0m 277ms/step - accuracy: 0.7407 - loss: 0.6591 - val_accuracy: 0.7455 - val_loss: 0.7050\n",
      "Epoch 25/40\n",
      "\u001b[1m229/229\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m88s\u001b[0m 305ms/step - accuracy: 0.7303 - loss: 0.6803 - val_accuracy: 0.7391 - val_loss: 0.6480\n",
      "Epoch 26/40\n",
      "\u001b[1m229/229\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m80s\u001b[0m 294ms/step - accuracy: 0.7392 - loss: 0.6531 - val_accuracy: 0.7826 - val_loss: 0.5899\n",
      "Epoch 27/40\n",
      "\u001b[1m229/229\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m63s\u001b[0m 275ms/step - accuracy: 0.7420 - loss: 0.6539 - val_accuracy: 0.7442 - val_loss: 0.6451\n",
      "Epoch 28/40\n",
      "\u001b[1m229/229\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m87s\u001b[0m 299ms/step - accuracy: 0.7423 - loss: 0.6390 - val_accuracy: 0.7711 - val_loss: 0.5789\n",
      "Epoch 29/40\n",
      "\u001b[1m229/229\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m62s\u001b[0m 271ms/step - accuracy: 0.7516 - loss: 0.6246 - val_accuracy: 0.7327 - val_loss: 0.6253\n",
      "Epoch 30/40\n",
      "\u001b[1m229/229\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m80s\u001b[0m 264ms/step - accuracy: 0.7520 - loss: 0.6266 - val_accuracy: 0.7462 - val_loss: 0.6250\n",
      "Epoch 31/40\n",
      "\u001b[1m229/229\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m82s\u001b[0m 264ms/step - accuracy: 0.7583 - loss: 0.6086 - val_accuracy: 0.7685 - val_loss: 0.5955\n",
      "Epoch 32/40\n",
      "\u001b[1m229/229\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m61s\u001b[0m 266ms/step - accuracy: 0.7545 - loss: 0.6319 - val_accuracy: 0.7826 - val_loss: 0.6027\n",
      "Epoch 33/40\n",
      "\u001b[1m229/229\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m87s\u001b[0m 288ms/step - accuracy: 0.7628 - loss: 0.5910 - val_accuracy: 0.7935 - val_loss: 0.5453\n",
      "Epoch 34/40\n",
      "\u001b[1m229/229\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m64s\u001b[0m 277ms/step - accuracy: 0.7642 - loss: 0.5931 - val_accuracy: 0.7621 - val_loss: 0.6093\n",
      "Epoch 35/40\n",
      "\u001b[1m229/229\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m62s\u001b[0m 273ms/step - accuracy: 0.7667 - loss: 0.5992 - val_accuracy: 0.7769 - val_loss: 0.5965\n",
      "Epoch 36/40\n",
      "\u001b[1m229/229\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m61s\u001b[0m 267ms/step - accuracy: 0.7717 - loss: 0.5763 - val_accuracy: 0.7596 - val_loss: 0.6165\n",
      "Epoch 37/40\n",
      "\u001b[1m229/229\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m63s\u001b[0m 273ms/step - accuracy: 0.7894 - loss: 0.5525 - val_accuracy: 0.7602 - val_loss: 0.6121\n",
      "Epoch 38/40\n",
      "\u001b[1m229/229\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m63s\u001b[0m 273ms/step - accuracy: 0.7765 - loss: 0.5614 - val_accuracy: 0.7666 - val_loss: 0.5975\n",
      "Epoch 39/40\n",
      "\u001b[1m229/229\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m70s\u001b[0m 307ms/step - accuracy: 0.7782 - loss: 0.5620 - val_accuracy: 0.7903 - val_loss: 0.5450\n",
      "Epoch 40/40\n",
      "\u001b[1m229/229\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m70s\u001b[0m 306ms/step - accuracy: 0.7872 - loss: 0.5430 - val_accuracy: 0.8005 - val_loss: 0.5398\n"
     ]
    }
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 9. Epoch-by-Epoch Results"
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "# Plot training and validation results\n",
    "plt.figure(figsize=(12, 5))\n",
    "\n",
    "# Accuracy\n",
    "plt.subplot(1, 2, 1)\n",
    "\n",
    "plt.plot(\n",
    "    history_augmented_cnn.history['accuracy'],\n",
    "    color='lightskyblue'\n",
    ")\n",
    "\n",
    "plt.plot(\n",
    "    history_augmented_cnn.history['val_accuracy'],\n",
    "    color='lightgreen'\n",
    ")\n",
    "\n",
    "plt.title('Augmented CNN Model Accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.legend(['Training', 'Validation'])\n",
    "\n",
    "# Loss\n",
    "plt.subplot(1, 2, 2)\n",
    "\n",
    "plt.plot(\n",
    "    history_augmented_cnn.history['loss'],\n",
    "    color='lightskyblue'\n",
    ")\n",
    "\n",
    "plt.plot(\n",
    "    history_augmented_cnn.history['val_loss'],\n",
    "    color='lightgreen'\n",
    ")\n",
    "\n",
    "plt.title('Augmented CNN Model Loss')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend(['Training', 'Validation'])\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 344
    },
    "id": "7FmxqaGeLUbY",
    "outputId": "d7971cea-4b3c-4053-ca25-18250a0df882"
   },
   "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": "code",
   "source": [
    "# Create a table with the results from each completed epoch\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "augmented_cnn_results = pd.DataFrame({\n",
    "    'Epoch': range(\n",
    "        1,\n",
    "        len(history_augmented_cnn.history['accuracy']) + 1\n",
    "    ),\n",
    "    'Training Accuracy': history_augmented_cnn.history['accuracy'],\n",
    "    'Validation Accuracy': history_augmented_cnn.history['val_accuracy'],\n",
    "    'Training Loss': history_augmented_cnn.history['loss'],\n",
    "    'Validation Loss': history_augmented_cnn.history['val_loss']\n",
    "})\n",
    "\n",
    "augmented_cnn_results.round(4)"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "zLG9Df2j3cp3",
    "outputId": "be23006e-306c-494a-d781-cde69899e978"
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "    Epoch  Training Accuracy  Validation Accuracy  Training Loss  \\\n",
       "0       1             0.3487               0.4648         1.5285   \n",
       "1       2             0.4125               0.4552         1.3405   \n",
       "2       3             0.4665               0.5512         1.2510   \n",
       "3       4             0.5042               0.5723         1.1751   \n",
       "4       5             0.5239               0.5774         1.1344   \n",
       "5       6             0.5517               0.6221         1.0924   \n",
       "6       7             0.5703               0.6240         1.0552   \n",
       "7       8             0.5929               0.6119         1.0188   \n",
       "8       9             0.6174               0.6471         0.9581   \n",
       "9      10             0.6353               0.6426         0.9070   \n",
       "10     11             0.6442               0.6720         0.8856   \n",
       "11     12             0.6596               0.6829         0.8541   \n",
       "12     13             0.6725               0.6375         0.8272   \n",
       "13     14             0.6762               0.6893         0.7994   \n",
       "14     15             0.6776               0.7052         0.7995   \n",
       "15     16             0.6913               0.7404         0.7769   \n",
       "16     17             0.6977               0.7142         0.7639   \n",
       "17     18             0.7029               0.6950         0.7452   \n",
       "18     19             0.7179               0.7353         0.7164   \n",
       "19     20             0.7186               0.7289         0.7137   \n",
       "20     21             0.7200               0.7334         0.7137   \n",
       "21     22             0.7263               0.7148         0.6924   \n",
       "22     23             0.7312               0.7366         0.6776   \n",
       "23     24             0.7407               0.7455         0.6591   \n",
       "24     25             0.7303               0.7391         0.6803   \n",
       "25     26             0.7392               0.7826         0.6531   \n",
       "26     27             0.7420               0.7442         0.6539   \n",
       "27     28             0.7423               0.7711         0.6390   \n",
       "28     29             0.7516               0.7327         0.6246   \n",
       "29     30             0.7520               0.7462         0.6266   \n",
       "30     31             0.7583               0.7685         0.6086   \n",
       "31     32             0.7545               0.7826         0.6319   \n",
       "32     33             0.7628               0.7935         0.5910   \n",
       "33     34             0.7642               0.7621         0.5931   \n",
       "34     35             0.7667               0.7769         0.5992   \n",
       "35     36             0.7717               0.7596         0.5763   \n",
       "36     37             0.7894               0.7602         0.5525   \n",
       "37     38             0.7765               0.7666         0.5614   \n",
       "38     39             0.7782               0.7903         0.5620   \n",
       "39     40             0.7872               0.8005         0.5430   \n",
       "\n",
       "    Validation Loss  \n",
       "0            1.2865  \n",
       "1            1.2769  \n",
       "2            1.1318  \n",
       "3            1.0783  \n",
       "4            1.0252  \n",
       "5            0.9708  \n",
       "6            0.9385  \n",
       "7            0.9081  \n",
       "8            0.9004  \n",
       "9            0.8662  \n",
       "10           0.8016  \n",
       "11           0.7552  \n",
       "12           0.8772  \n",
       "13           0.7634  \n",
       "14           0.7311  \n",
       "15           0.6894  \n",
       "16           0.7325  \n",
       "17           0.7374  \n",
       "18           0.6662  \n",
       "19           0.6780  \n",
       "20           0.6652  \n",
       "21           0.7123  \n",
       "22           0.6498  \n",
       "23           0.7050  \n",
       "24           0.6480  \n",
       "25           0.5899  \n",
       "26           0.6451  \n",
       "27           0.5789  \n",
       "28           0.6253  \n",
       "29           0.6250  \n",
       "30           0.5955  \n",
       "31           0.6027  \n",
       "32           0.5453  \n",
       "33           0.6093  \n",
       "34           0.5965  \n",
       "35           0.6165  \n",
       "36           0.6121  \n",
       "37           0.5975  \n",
       "38           0.5450  \n",
       "39           0.5398  "
      ],
      "text/html": [
       "\n",
       "  <div id=\"df-4b988c19-b5bc-4765-aecf-062cade5d1e0\" class=\"colab-df-container\">\n",
       "    <div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Epoch</th>\n",
       "      <th>Training Accuracy</th>\n",
       "      <th>Validation Accuracy</th>\n",
       "      <th>Training Loss</th>\n",
       "      <th>Validation Loss</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.3487</td>\n",
       "      <td>0.4648</td>\n",
       "      <td>1.5285</td>\n",
       "      <td>1.2865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.4125</td>\n",
       "      <td>0.4552</td>\n",
       "      <td>1.3405</td>\n",
       "      <td>1.2769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0.4665</td>\n",
       "      <td>0.5512</td>\n",
       "      <td>1.2510</td>\n",
       "      <td>1.1318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0.5042</td>\n",
       "      <td>0.5723</td>\n",
       "      <td>1.1751</td>\n",
       "      <td>1.0783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0.5239</td>\n",
       "      <td>0.5774</td>\n",
       "      <td>1.1344</td>\n",
       "      <td>1.0252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>0.5517</td>\n",
       "      <td>0.6221</td>\n",
       "      <td>1.0924</td>\n",
       "      <td>0.9708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>0.5703</td>\n",
       "      <td>0.6240</td>\n",
       "      <td>1.0552</td>\n",
       "      <td>0.9385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>0.5929</td>\n",
       "      <td>0.6119</td>\n",
       "      <td>1.0188</td>\n",
       "      <td>0.9081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>0.6174</td>\n",
       "      <td>0.6471</td>\n",
       "      <td>0.9581</td>\n",
       "      <td>0.9004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>0.6353</td>\n",
       "      <td>0.6426</td>\n",
       "      <td>0.9070</td>\n",
       "      <td>0.8662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>0.6442</td>\n",
       "      <td>0.6720</td>\n",
       "      <td>0.8856</td>\n",
       "      <td>0.8016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>0.6596</td>\n",
       "      <td>0.6829</td>\n",
       "      <td>0.8541</td>\n",
       "      <td>0.7552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>0.6725</td>\n",
       "      <td>0.6375</td>\n",
       "      <td>0.8272</td>\n",
       "      <td>0.8772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>0.6762</td>\n",
       "      <td>0.6893</td>\n",
       "      <td>0.7994</td>\n",
       "      <td>0.7634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>0.6776</td>\n",
       "      <td>0.7052</td>\n",
       "      <td>0.7995</td>\n",
       "      <td>0.7311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>0.6913</td>\n",
       "      <td>0.7404</td>\n",
       "      <td>0.7769</td>\n",
       "      <td>0.6894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>0.6977</td>\n",
       "      <td>0.7142</td>\n",
       "      <td>0.7639</td>\n",
       "      <td>0.7325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18</td>\n",
       "      <td>0.7029</td>\n",
       "      <td>0.6950</td>\n",
       "      <td>0.7452</td>\n",
       "      <td>0.7374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>0.7179</td>\n",
       "      <td>0.7353</td>\n",
       "      <td>0.7164</td>\n",
       "      <td>0.6662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>0.7186</td>\n",
       "      <td>0.7289</td>\n",
       "      <td>0.7137</td>\n",
       "      <td>0.6780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>21</td>\n",
       "      <td>0.7200</td>\n",
       "      <td>0.7334</td>\n",
       "      <td>0.7137</td>\n",
       "      <td>0.6652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>22</td>\n",
       "      <td>0.7263</td>\n",
       "      <td>0.7148</td>\n",
       "      <td>0.6924</td>\n",
       "      <td>0.7123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>23</td>\n",
       "      <td>0.7312</td>\n",
       "      <td>0.7366</td>\n",
       "      <td>0.6776</td>\n",
       "      <td>0.6498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>24</td>\n",
       "      <td>0.7407</td>\n",
       "      <td>0.7455</td>\n",
       "      <td>0.6591</td>\n",
       "      <td>0.7050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>25</td>\n",
       "      <td>0.7303</td>\n",
       "      <td>0.7391</td>\n",
       "      <td>0.6803</td>\n",
       "      <td>0.6480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>26</td>\n",
       "      <td>0.7392</td>\n",
       "      <td>0.7826</td>\n",
       "      <td>0.6531</td>\n",
       "      <td>0.5899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>27</td>\n",
       "      <td>0.7420</td>\n",
       "      <td>0.7442</td>\n",
       "      <td>0.6539</td>\n",
       "      <td>0.6451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>28</td>\n",
       "      <td>0.7423</td>\n",
       "      <td>0.7711</td>\n",
       "      <td>0.6390</td>\n",
       "      <td>0.5789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>29</td>\n",
       "      <td>0.7516</td>\n",
       "      <td>0.7327</td>\n",
       "      <td>0.6246</td>\n",
       "      <td>0.6253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>30</td>\n",
       "      <td>0.7520</td>\n",
       "      <td>0.7462</td>\n",
       "      <td>0.6266</td>\n",
       "      <td>0.6250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>31</td>\n",
       "      <td>0.7583</td>\n",
       "      <td>0.7685</td>\n",
       "      <td>0.6086</td>\n",
       "      <td>0.5955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>32</td>\n",
       "      <td>0.7545</td>\n",
       "      <td>0.7826</td>\n",
       "      <td>0.6319</td>\n",
       "      <td>0.6027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>33</td>\n",
       "      <td>0.7628</td>\n",
       "      <td>0.7935</td>\n",
       "      <td>0.5910</td>\n",
       "      <td>0.5453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>34</td>\n",
       "      <td>0.7642</td>\n",
       "      <td>0.7621</td>\n",
       "      <td>0.5931</td>\n",
       "      <td>0.6093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>35</td>\n",
       "      <td>0.7667</td>\n",
       "      <td>0.7769</td>\n",
       "      <td>0.5992</td>\n",
       "      <td>0.5965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>36</td>\n",
       "      <td>0.7717</td>\n",
       "      <td>0.7596</td>\n",
       "      <td>0.5763</td>\n",
       "      <td>0.6165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>37</td>\n",
       "      <td>0.7894</td>\n",
       "      <td>0.7602</td>\n",
       "      <td>0.5525</td>\n",
       "      <td>0.6121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>38</td>\n",
       "      <td>0.7765</td>\n",
       "      <td>0.7666</td>\n",
       "      <td>0.5614</td>\n",
       "      <td>0.5975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>39</td>\n",
       "      <td>0.7782</td>\n",
       "      <td>0.7903</td>\n",
       "      <td>0.5620</td>\n",
       "      <td>0.5450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>40</td>\n",
       "      <td>0.7872</td>\n",
       "      <td>0.8005</td>\n",
       "      <td>0.5430</td>\n",
       "      <td>0.5398</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "    <div class=\"colab-df-buttons\">\n",
       "\n",
       "  <div class=\"colab-df-container\">\n",
       "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4b988c19-b5bc-4765-aecf-062cade5d1e0')\"\n",
       "            title=\"Convert this dataframe to an interactive table.\"\n",
       "            style=\"display:none;\">\n",
       "\n",
       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
       "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
       "  </svg>\n",
       "    </button>\n",
       "\n",
       "  <style>\n",
       "    .colab-df-container {\n",
       "      display:flex;\n",
       "      gap: 12px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert {\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-convert: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",
       "    .colab-df-buttons div {\n",
       "      margin-bottom: 4px;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert {\n",
       "      background-color: #3B4455;\n",
       "      fill: #D2E3FC;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert: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",
       "\n",
       "    <script>\n",
       "      const buttonEl =\n",
       "        document.querySelector('#df-4b988c19-b5bc-4765-aecf-062cade5d1e0 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-4b988c19-b5bc-4765-aecf-062cade5d1e0');\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",
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       "        docLink.innerHTML = docLinkHtml;\n",
       "        element.appendChild(docLink);\n",
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       "\n",
       "\n",
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       "  </div>\n"
      ],
      "application/vnd.google.colaboratory.intrinsic+json": {
       "type": "dataframe",
       "summary": "{\n  \"name\": \"augmented_cnn_results\",\n  \"rows\": 40,\n  \"fields\": [\n    {\n      \"column\": \"Epoch\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11,\n        \"min\": 1,\n        \"max\": 40,\n        \"num_unique_values\": 40,\n        \"samples\": [\n          20,\n          17,\n          16\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Training Accuracy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.10716586174279998,\n        \"min\": 0.3487,\n        \"max\": 0.7894,\n        \"num_unique_values\": 40,\n        \"samples\": [\n          0.7186,\n          0.6977,\n          0.6913\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Validation Accuracy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.08573617070961774,\n        \"min\": 0.4552,\n        \"max\": 0.8005,\n        \"num_unique_values\": 39,\n        \"samples\": [\n          0.7769,\n          0.7666,\n          0.5774\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Training Loss\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2387624771340327,\n        \"min\": 0.543,\n        \"max\": 1.5285,\n        \"num_unique_values\": 39,\n        \"samples\": [\n          0.5992,\n          0.5614,\n          1.1344\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Validation Loss\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.19485956488107278,\n        \"min\": 0.5398,\n        \"max\": 1.2865,\n        \"num_unique_values\": 40,\n        \"samples\": [\n          0.678,\n          0.7325,\n          0.6894\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
      }
     },
     "metadata": {},
     "execution_count": 23
    }
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 10. Best Epoch Identification"
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "# Find the best epoch based on the lowest validation loss\n",
    "best_augmented_epoch = augmented_cnn_results.loc[\n",
    "    augmented_cnn_results['Validation Loss'].idxmin()\n",
    "]\n",
    "\n",
    "print(\"Best Augmented CNN epoch:\")\n",
    "print(best_augmented_epoch.round(4))"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "bQ1eBtqE4PXd",
    "outputId": "559226d2-bef4-4fa2-f841-9d4aa378d4b2"
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Best Augmented CNN epoch:\n",
      "Epoch                  40.0000\n",
      "Training Accuracy       0.7872\n",
      "Validation Accuracy     0.8005\n",
      "Training Loss           0.5430\n",
      "Validation Loss         0.5398\n",
      "Name: 39, dtype: float64\n"
     ]
    }
   ]
  }
 ]
}