{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "050e417c",
   "metadata": {},
   "source": [
    "## Workflow Methodology Image Classification"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b4c4dc0",
   "metadata": {},
   "source": [
    "### Step 1: Loading Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4d9340d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "import tensorflow as tf\n",
    "\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import (\n",
    "    Input,\n",
    "    Conv2D,\n",
    "    MaxPooling2D,\n",
    "    Flatten,\n",
    "    Dense,\n",
    "    Dropout\n",
    ")\n",
    "\n",
    "from tensorflow.keras.callbacks import EarlyStopping\n",
    "\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "\n",
    "from sklearn.metrics import (\n",
    "    classification_report,\n",
    "    confusion_matrix\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ff15f8a",
   "metadata": {},
   "source": [
    "### Step 2: Loading Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "54d61691",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Unnamed: 0             filepaths                           labels data set\n",
      "0           0  800113bb65efe69e.jpg                          healthy    train\n",
      "1           0  8002cb321f8bfcdf.jpg  scab frog_eye_leaf_spot complex    train\n",
      "2           0  80070f7fb5e2ccaa.jpg                             scab    train\n",
      "3           0  80077517781fb94f.jpg                             scab    train\n",
      "4           0  800cbf0ff87721f8.jpg                          complex    train\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv(\"data.csv\")\n",
    "\n",
    "print(df.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29febe2d",
   "metadata": {},
   "source": [
    "#### Step 2.1 Dataset Summary\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d9747ae6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset Shape: (18632, 4)\n",
      "\n",
      "Columns:\n",
      "Index(['Unnamed: 0', 'filepaths', 'labels', 'data set'], dtype='object')\n",
      "\n",
      "Class Distribution:\n",
      "labels\n",
      "scab                               4826\n",
      "healthy                            4624\n",
      "frog_eye_leaf_spot                 3181\n",
      "rust                               1860\n",
      "complex                            1602\n",
      "powdery_mildew                     1184\n",
      "scab frog_eye_leaf_spot             686\n",
      "scab frog_eye_leaf_spot complex     200\n",
      "frog_eye_leaf_spot complex          165\n",
      "rust frog_eye_leaf_spot             120\n",
      "rust complex                         97\n",
      "powdery_mildew complex               87\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(\"Dataset Shape:\", df.shape)\n",
    "\n",
    "print(\"\\nColumns:\")\n",
    "print(df.columns)\n",
    "\n",
    "print(\"\\nClass Distribution:\")\n",
    "print(df[\"labels\"].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "817a41bc",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Natalia\\AppData\\Local\\Temp\\ipykernel_11696\\3562262185.py:9: FutureWarning: \n",
      "\n",
      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `y` variable to `hue` and set `legend=False` for the same effect.\n",
      "\n",
      "  ax = sns.barplot(\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Class distribution of plant pathology dataset\n",
    "\n",
    "# Count classes\n",
    "class_counts = df[\"labels\"].value_counts()\n",
    "\n",
    "# Create figure\n",
    "plt.figure(figsize=(12,8))\n",
    "\n",
    "ax = sns.barplot(\n",
    "    x=class_counts.values,\n",
    "    y=class_counts.index,\n",
    "    palette=\"viridis\",\n",
    "    orient=\"h\"\n",
    ")\n",
    "\n",
    "# Add values at end of bars\n",
    "for i, value in enumerate(class_counts.values):\n",
    "\n",
    "    plt.text(\n",
    "        value + 20,\n",
    "        i,\n",
    "        str(value),\n",
    "        va='center',\n",
    "        fontweight='bold'\n",
    "    )\n",
    "\n",
    "plt.title(\n",
    "    \"Class Distribution of Plant Pathology Dataset\",\n",
    "    fontsize=14,\n",
    "    fontweight='bold'\n",
    ")\n",
    "\n",
    "plt.xlabel(\"Number of Images\")\n",
    "plt.ylabel(\"Disease Class\")\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f33bf9f1",
   "metadata": {},
   "source": [
    "#### Step 2.2 Filter dataset and Creating a new cell"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ba038e9b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(15675, 4)\n",
      "labels\n",
      "scab                  4826\n",
      "healthy               4624\n",
      "frog_eye_leaf_spot    3181\n",
      "rust                  1860\n",
      "powdery_mildew        1184\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "required_classes = [\n",
    "    \"healthy\",\n",
    "    \"scab\",\n",
    "    \"frog_eye_leaf_spot\",\n",
    "    \"rust\",\n",
    "    \"powdery_mildew\"\n",
    "]\n",
    "\n",
    "filtered_df = df[df[\"labels\"].isin(required_classes)]\n",
    "\n",
    "print(filtered_df.shape)\n",
    "\n",
    "print(filtered_df[\"labels\"].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "200e42f0",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Natalia\\AppData\\Local\\Temp\\ipykernel_11696\\2789883365.py:10: FutureWarning: \n",
      "\n",
      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `y` variable to `hue` and set `legend=False` for the same effect.\n",
      "\n",
      "  ax = sns.barplot(\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Filtered Dataset Distribution\n",
    "\n",
    "# Count images in each class\n",
    "class_counts = filtered_df[\"labels\"].value_counts()\n",
    "\n",
    "# Create figure\n",
    "plt.figure(figsize=(10, 6))\n",
    "\n",
    "# Horizontal bar chart\n",
    "ax = sns.barplot(\n",
    "    x=class_counts.values,\n",
    "    y=class_counts.index,\n",
    "    palette=\"viridis\"\n",
    ")\n",
    "\n",
    "# Add values to bars\n",
    "for i, value in enumerate(class_counts.values):\n",
    "    plt.text(\n",
    "        value + 50,     # Position slightly after bar\n",
    "        i,\n",
    "        str(value),\n",
    "        va='center',\n",
    "        fontweight='bold'\n",
    "    )\n",
    "\n",
    "# Titles and labels\n",
    "plt.title(\n",
    "    \"Class Distribution of Filtered Plant Disease Dataset\",\n",
    "    fontsize=14,\n",
    "    fontweight=\"bold\"\n",
    ")\n",
    "\n",
    "plt.xlabel(\"Number of Images\")\n",
    "plt.ylabel(\"Disease Class\")\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a5f31ad",
   "metadata": {},
   "source": [
    "### Step 3: Keeping Only Required Classes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f945c76b",
   "metadata": {},
   "source": [
    "#### Saving the Filtered New dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "012717d6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Filtered dataset saved!\n"
     ]
    }
   ],
   "source": [
    "filtered_df.to_csv(\n",
    "    \"filtered_plant_new_data.csv\",\n",
    "    index=False\n",
    ")\n",
    "\n",
    "print(\"Filtered dataset saved!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9eb0e040",
   "metadata": {},
   "source": [
    "### Step 4: Create Dataset Structure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "a98fcf35",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset preparation complete.\n"
     ]
    }
   ],
   "source": [
    "import shutil\n",
    "\n",
    "os.makedirs(\"plant_dataset\", exist_ok=True)\n",
    "\n",
    "for label in required_classes:\n",
    "    os.makedirs(\n",
    "        os.path.join(\"plant_dataset\", label),\n",
    "        exist_ok=True\n",
    "    )\n",
    "\n",
    "for _, row in filtered_df.iterrows():\n",
    "\n",
    "    image_name = row[\"filepaths\"]\n",
    "    label = row[\"labels\"]\n",
    "\n",
    "    source = os.path.join(\n",
    "        \"images\",\n",
    "        image_name\n",
    "    )\n",
    "\n",
    "    destination = os.path.join(\n",
    "        \"plant_dataset\",\n",
    "        label,\n",
    "        image_name\n",
    "    )\n",
    "\n",
    "    if os.path.exists(source):\n",
    "        shutil.copy2(\n",
    "            source,\n",
    "            destination\n",
    "        )\n",
    "\n",
    "print(\"Dataset preparation complete.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06824dc6",
   "metadata": {},
   "source": [
    "### Step 5: Training, Validation and Testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "636bc142",
   "metadata": {},
   "outputs": [],
   "source": [
    "#  Create a separate directory for training,validation and testing datasets\n",
    "\n",
    "import os\n",
    "import shutil\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Create output folders\n",
    "base_dir = \"plant_split_dataset\"\n",
    "\n",
    "for split in ['train', 'validation', 'test']:\n",
    "    for label in required_classes:\n",
    "        os.makedirs(\n",
    "            os.path.join(base_dir, split, label),\n",
    "            exist_ok=True\n",
    "        )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "9ca6162e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset successfully split into Train, Validation and Test sets.\n"
     ]
    }
   ],
   "source": [
    "# Split the images into training, validation and testing datasets\n",
    "for label in required_classes:\n",
    "\n",
    "    class_df = filtered_df[\n",
    "        filtered_df[\"labels\"] == label\n",
    "    ]\n",
    "\n",
    "    images = class_df[\"filepaths\"].tolist()\n",
    "\n",
    "    # 70% train, 30% temp\n",
    "    train_imgs, temp_imgs = train_test_split(\n",
    "        images,\n",
    "        test_size=0.30,\n",
    "        random_state=42\n",
    "    )\n",
    "\n",
    "    # Split remaining 30%\n",
    "    val_imgs, test_imgs = train_test_split(\n",
    "        temp_imgs,\n",
    "        test_size=0.50,\n",
    "        random_state=42\n",
    "    )\n",
    "\n",
    "    # Copy training images\n",
    "    for img in train_imgs:\n",
    "\n",
    "        shutil.copy2(\n",
    "            os.path.join(\"images\", img),\n",
    "            os.path.join(\n",
    "                base_dir,\n",
    "                \"train\",\n",
    "                label,\n",
    "                img\n",
    "            )\n",
    "        )\n",
    "\n",
    "    # Copy validation images\n",
    "    for img in val_imgs:\n",
    "\n",
    "        shutil.copy2(\n",
    "            os.path.join(\"images\", img),\n",
    "            os.path.join(\n",
    "                base_dir,\n",
    "                \"validation\",\n",
    "                label,\n",
    "                img\n",
    "            )\n",
    "        )\n",
    "\n",
    "    # Copy test images\n",
    "    for img in test_imgs:\n",
    "\n",
    "        shutil.copy2(\n",
    "            os.path.join(\"images\", img),\n",
    "            os.path.join(\n",
    "                base_dir,\n",
    "                \"test\",\n",
    "                label,\n",
    "                img\n",
    "            )\n",
    "        )\n",
    "\n",
    "print(\"Dataset successfully split into Train, Validation and Test sets.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "1cb4a508",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "TRAIN SET\n",
      "healthy 3236\n",
      "scab 3378\n",
      "frog_eye_leaf_spot 2226\n",
      "rust 1302\n",
      "powdery_mildew 828\n",
      "Total = 10970\n",
      "\n",
      "VALIDATION SET\n",
      "healthy 694\n",
      "scab 724\n",
      "frog_eye_leaf_spot 477\n",
      "rust 279\n",
      "powdery_mildew 178\n",
      "Total = 2352\n",
      "\n",
      "TEST SET\n",
      "healthy 694\n",
      "scab 724\n",
      "frog_eye_leaf_spot 478\n",
      "rust 279\n",
      "powdery_mildew 178\n",
      "Total = 2353\n"
     ]
    }
   ],
   "source": [
    "# Verify the number of images in each split\n",
    "for split in ['train', 'validation', 'test']:\n",
    "\n",
    "    print(f\"\\n{split.upper()} SET\")\n",
    "\n",
    "    total = 0\n",
    "\n",
    "    for label in required_classes:\n",
    "\n",
    "        count = len(\n",
    "            os.listdir(\n",
    "                os.path.join(\n",
    "                    base_dir,\n",
    "                    split,\n",
    "                    label\n",
    "                )\n",
    "            )\n",
    "        )\n",
    "\n",
    "        total += count\n",
    "\n",
    "        print(label, count)\n",
    "\n",
    "    print(\"Total =\", total)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "00fe9a16",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Natalia\\AppData\\Local\\Temp\\ipykernel_11696\\1592471336.py:27: FutureWarning: \n",
      "\n",
      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
      "\n",
      "  ax = sns.barplot(\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Dataset split counts\n",
    "dataset_split = {\n",
    "    \"Training\": 10970,\n",
    "    \"Validation\": 2352,\n",
    "    \"Testing\": 2353\n",
    "}\n",
    "\n",
    "# Create DataFrame\n",
    "split_df = pd.DataFrame(\n",
    "    list(dataset_split.items()),\n",
    "    columns=[\"Dataset\", \"Images\"]\n",
    ")\n",
    "\n",
    "# Calculate percentages\n",
    "split_df[\"Percentage\"] = (\n",
    "    split_df[\"Images\"] /\n",
    "    split_df[\"Images\"].sum()\n",
    ") * 100\n",
    "\n",
    "# Plot\n",
    "plt.figure(figsize=(8,6))\n",
    "\n",
    "ax = sns.barplot(\n",
    "    data=split_df,\n",
    "    x=\"Dataset\",\n",
    "    y=\"Percentage\",\n",
    "    palette=\"viridis\"\n",
    ")\n",
    "\n",
    "# Add percentage labels\n",
    "for p in ax.patches:\n",
    "\n",
    "    ax.annotate(\n",
    "\n",
    "        f\"{p.get_height():.1f}%\",\n",
    "\n",
    "        (\n",
    "            p.get_x() + p.get_width()/2,\n",
    "            p.get_height()\n",
    "        ),\n",
    "\n",
    "        ha='center',\n",
    "\n",
    "        va='bottom',\n",
    "\n",
    "        fontsize=12,\n",
    "\n",
    "        fontweight='bold'\n",
    "    )\n",
    "\n",
    "plt.title(\n",
    "    \"Percentage Distribution of Dataset Splits\",\n",
    "    fontsize=16,\n",
    "    fontweight='bold'\n",
    ")\n",
    "\n",
    "plt.xlabel(\"Dataset\")\n",
    "plt.ylabel(\"Percentage (%)\")\n",
    "\n",
    "plt.ylim(0, 80)\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7beb777",
   "metadata": {},
   "source": [
    "### Step 6: Creating Data Generators"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab9c80d8",
   "metadata": {},
   "source": [
    "#### Training Generator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a1707265",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 10970 images belonging to 5 classes.\n"
     ]
    }
   ],
   "source": [
    "train_datagen = ImageDataGenerator(\n",
    "    rescale=1./255,\n",
    "    rotation_range=20,\n",
    "    zoom_range=0.2,\n",
    "    horizontal_flip=True\n",
    ")\n",
    "\n",
    "train_generator = train_datagen.flow_from_directory(\n",
    "    \"plant_split_dataset/train\",\n",
    "    target_size=(224,224),\n",
    "    batch_size=32,\n",
    "    class_mode=\"categorical\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb688fed",
   "metadata": {},
   "source": [
    "#### Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2749b0dc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 2352 images belonging to 5 classes.\n"
     ]
    }
   ],
   "source": [
    "validation_datagen = ImageDataGenerator(\n",
    "    rescale=1./255\n",
    ")\n",
    "\n",
    "validation_generator = validation_datagen.flow_from_directory(\n",
    "    \"plant_split_dataset/validation\",\n",
    "    target_size=(224,224),\n",
    "    batch_size=32,\n",
    "    class_mode=\"categorical\",\n",
    "    shuffle=False\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a883d964",
   "metadata": {},
   "source": [
    "#### Testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "6baaf364",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 2353 images belonging to 5 classes.\n"
     ]
    }
   ],
   "source": [
    "test_datagen = ImageDataGenerator(\n",
    "    rescale=1./255\n",
    ")\n",
    "\n",
    "test_generator = test_datagen.flow_from_directory(\n",
    "    \"plant_split_dataset/test\",\n",
    "    target_size=(224,224),\n",
    "    batch_size=32,\n",
    "    class_mode=\"categorical\",\n",
    "    shuffle=False\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7fe1e47",
   "metadata": {},
   "source": [
    "## Step 7: CNN Model 1 (Baseline)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "fe09d295",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential([\n",
    "    Input(shape=(224,224,3)),\n",
    "\n",
    "    Conv2D(32, (3,3), activation='relu'),\n",
    "    MaxPooling2D(2,2),\n",
    "\n",
    "    Conv2D(64, (3,3), activation='relu'),\n",
    "    MaxPooling2D(2,2),\n",
    "\n",
    "    Conv2D(128, (3,3), activation='relu'),\n",
    "    MaxPooling2D(2,2),\n",
    "\n",
    "    Flatten(),\n",
    "\n",
    "    Dense(128, activation='relu'),\n",
    "    Dropout(0.5),\n",
    "\n",
    "    Dense(5, activation='softmax')])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4a09b25",
   "metadata": {},
   "source": [
    "### Step 8: Compiling the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "cdf5b75e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:TensorFlow GPU support is not available on native Windows for TensorFlow >= 2.11. Even if CUDA/cuDNN are installed, GPU will not be used. Please use WSL2 or the TensorFlow-DirectML plugin.\n"
     ]
    }
   ],
   "source": [
    "model.compile(\n",
    "    optimizer='adam',\n",
    "    loss='categorical_crossentropy',\n",
    "    metrics=['accuracy']\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "16612bf4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"sequential\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ conv2d (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">222</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">222</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)   │           <span style=\"color: #00af00; text-decoration-color: #00af00\">896</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">111</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">111</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)   │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">109</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">109</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)   │        <span style=\"color: #00af00; text-decoration-color: #00af00\">18,496</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">54</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">54</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)     │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">52</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">52</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)    │        <span style=\"color: #00af00; text-decoration-color: #00af00\">73,856</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">26</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">26</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)    │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ flatten (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">86528</span>)          │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)            │    <span style=\"color: #00af00; text-decoration-color: #00af00\">11,075,712</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dropout (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)            │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">5</span>)              │           <span style=\"color: #00af00; text-decoration-color: #00af00\">645</span> │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ conv2d (\u001b[38;5;33mConv2D\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m222\u001b[0m, \u001b[38;5;34m222\u001b[0m, \u001b[38;5;34m32\u001b[0m)   │           \u001b[38;5;34m896\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d (\u001b[38;5;33mMaxPooling2D\u001b[0m)    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m111\u001b[0m, \u001b[38;5;34m111\u001b[0m, \u001b[38;5;34m32\u001b[0m)   │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_1 (\u001b[38;5;33mConv2D\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m109\u001b[0m, \u001b[38;5;34m109\u001b[0m, \u001b[38;5;34m64\u001b[0m)   │        \u001b[38;5;34m18,496\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d_1 (\u001b[38;5;33mMaxPooling2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m54\u001b[0m, \u001b[38;5;34m54\u001b[0m, \u001b[38;5;34m64\u001b[0m)     │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_2 (\u001b[38;5;33mConv2D\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m52\u001b[0m, \u001b[38;5;34m52\u001b[0m, \u001b[38;5;34m128\u001b[0m)    │        \u001b[38;5;34m73,856\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d_2 (\u001b[38;5;33mMaxPooling2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m26\u001b[0m, \u001b[38;5;34m26\u001b[0m, \u001b[38;5;34m128\u001b[0m)    │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ flatten (\u001b[38;5;33mFlatten\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m86528\u001b[0m)          │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense (\u001b[38;5;33mDense\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)            │    \u001b[38;5;34m11,075,712\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dropout (\u001b[38;5;33mDropout\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)            │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_1 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m)              │           \u001b[38;5;34m645\u001b[0m │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "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\">11,169,605</span> (42.61 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m11,169,605\u001b[0m (42.61 MB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "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\">11,169,605</span> (42.61 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m11,169,605\u001b[0m (42.61 MB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "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"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6bc2f33",
   "metadata": {},
   "source": [
    "### Step 9: Early Stopping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "5bb2c69b",
   "metadata": {},
   "outputs": [],
   "source": [
    "early_stop = EarlyStopping(\n",
    "    monitor='val_loss',\n",
    "    patience=5,\n",
    "    restore_best_weights=True\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f5a2020f",
   "metadata": {},
   "source": [
    "### Step 10: Train Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "82d99651",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "\u001b[1m343/343\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m572s\u001b[0m 2s/step - accuracy: 0.4172 - loss: 1.3279 - val_accuracy: 0.5111 - val_loss: 1.1580\n",
      "Epoch 2/20\n",
      "\u001b[1m343/343\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m417s\u001b[0m 1s/step - accuracy: 0.5390 - loss: 1.0912 - val_accuracy: 0.5663 - val_loss: 1.1181\n",
      "Epoch 3/20\n",
      "\u001b[1m343/343\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m423s\u001b[0m 1s/step - accuracy: 0.6242 - loss: 0.9049 - val_accuracy: 0.6059 - val_loss: 0.8353\n",
      "Epoch 4/20\n",
      "\u001b[1m343/343\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m492s\u001b[0m 1s/step - accuracy: 0.6526 - loss: 0.8295 - val_accuracy: 0.7075 - val_loss: 0.6810\n",
      "Epoch 5/20\n",
      "\u001b[1m343/343\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m490s\u001b[0m 1s/step - accuracy: 0.6823 - loss: 0.7636 - val_accuracy: 0.7147 - val_loss: 0.6878\n",
      "Epoch 6/20\n",
      "\u001b[1m343/343\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m489s\u001b[0m 1s/step - accuracy: 0.6945 - loss: 0.7231 - val_accuracy: 0.7079 - val_loss: 0.6642\n",
      "Epoch 7/20\n",
      "\u001b[1m343/343\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m469s\u001b[0m 1s/step - accuracy: 0.7034 - loss: 0.7084 - val_accuracy: 0.7236 - val_loss: 0.6440\n",
      "Epoch 8/20\n",
      "\u001b[1m343/343\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m390s\u001b[0m 1s/step - accuracy: 0.7239 - loss: 0.6813 - val_accuracy: 0.7526 - val_loss: 0.5835\n",
      "Epoch 9/20\n",
      "\u001b[1m343/343\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m374s\u001b[0m 1s/step - accuracy: 0.7319 - loss: 0.6519 - val_accuracy: 0.7360 - val_loss: 0.6064\n",
      "Epoch 10/20\n",
      "\u001b[1m343/343\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m415s\u001b[0m 1s/step - accuracy: 0.7409 - loss: 0.6320 - val_accuracy: 0.7700 - val_loss: 0.5316\n",
      "Epoch 11/20\n",
      "\u001b[1m343/343\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m490s\u001b[0m 1s/step - accuracy: 0.7587 - loss: 0.6037 - val_accuracy: 0.7551 - val_loss: 0.5809\n",
      "Epoch 12/20\n",
      "\u001b[1m343/343\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m413s\u001b[0m 1s/step - accuracy: 0.7668 - loss: 0.5795 - val_accuracy: 0.8023 - val_loss: 0.5157\n",
      "Epoch 13/20\n",
      "\u001b[1m343/343\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m392s\u001b[0m 1s/step - accuracy: 0.7780 - loss: 0.5593 - val_accuracy: 0.7632 - val_loss: 0.6190\n",
      "Epoch 14/20\n",
      "\u001b[1m343/343\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m370s\u001b[0m 1s/step - accuracy: 0.7871 - loss: 0.5403 - val_accuracy: 0.7908 - val_loss: 0.5281\n",
      "Epoch 15/20\n",
      "\u001b[1m343/343\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m417s\u001b[0m 1s/step - accuracy: 0.8099 - loss: 0.5034 - val_accuracy: 0.8185 - val_loss: 0.4834\n",
      "Epoch 16/20\n",
      "\u001b[1m343/343\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m395s\u001b[0m 1s/step - accuracy: 0.8213 - loss: 0.4819 - val_accuracy: 0.8393 - val_loss: 0.4251\n",
      "Epoch 17/20\n",
      "\u001b[1m343/343\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m473s\u001b[0m 1s/step - accuracy: 0.8379 - loss: 0.4438 - val_accuracy: 0.8053 - val_loss: 0.4890\n",
      "Epoch 18/20\n",
      "\u001b[1m343/343\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m458s\u001b[0m 1s/step - accuracy: 0.8495 - loss: 0.4164 - val_accuracy: 0.8759 - val_loss: 0.3396\n",
      "Epoch 19/20\n",
      "\u001b[1m343/343\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m463s\u001b[0m 1s/step - accuracy: 0.8553 - loss: 0.3975 - val_accuracy: 0.8486 - val_loss: 0.4165\n",
      "Epoch 20/20\n",
      "\u001b[1m343/343\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m487s\u001b[0m 1s/step - accuracy: 0.8643 - loss: 0.3905 - val_accuracy: 0.8822 - val_loss: 0.3283\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(\n",
    "    train_generator,\n",
    "    validation_data=validation_generator,\n",
    "    epochs=20,\n",
    "    callbacks=[early_stop]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "1653fb3b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m74/74\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m33s\u001b[0m 447ms/step - accuracy: 0.8848 - loss: 0.3206\n",
      "Test Accuracy: 0.8848278522491455\n",
      "Test Loss: 0.32056480646133423\n"
     ]
    }
   ],
   "source": [
    "loss, accuracy = model.evaluate(\n",
    "    test_generator\n",
    ")\n",
    "\n",
    "print(\n",
    "    \"Test Accuracy:\",\n",
    "    accuracy\n",
    ")\n",
    "\n",
    "print(\n",
    "    \"Test Loss:\",\n",
    "    loss\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08108c32",
   "metadata": {},
   "source": [
    "#### Alternative CNN Model 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "1b636121",
   "metadata": {},
   "outputs": [],
   "source": [
    "model2 = Sequential([\n",
    "    Input(shape=(224,224,3)),\n",
    "\n",
    "    Conv2D(32,(3,3),activation='relu'),\n",
    "    MaxPooling2D(2,2),\n",
    "\n",
    "    Conv2D(64,(3,3),activation='relu'),\n",
    "    MaxPooling2D(2,2),\n",
    "\n",
    "    Conv2D(128,(3,3),activation='relu'),\n",
    "    MaxPooling2D(2,2),\n",
    "\n",
    "    Conv2D(256,(3,3),activation='relu'),\n",
    "    MaxPooling2D(2,2),\n",
    "\n",
    "    Flatten(),\n",
    "\n",
    "    Dense(256,activation='relu'),\n",
    "    Dropout(0.5),\n",
    "\n",
    "    Dense(5,activation='softmax')\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "24784e42",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Batch Size: 32\n",
      "Epochs: 20\n",
      "Learning Rate: 0.001\n",
      "Optimizer: Adam\n"
     ]
    }
   ],
   "source": [
    "batch_size = 32\n",
    "epochs = 20\n",
    "learning_rate = 0.001\n",
    "optimizer = \"Adam\"\n",
    "\n",
    "print(\"Batch Size:\", batch_size)\n",
    "print(\"Epochs:\", epochs)\n",
    "print(\"Learning Rate:\", learning_rate)\n",
    "print(\"Optimizer:\", optimizer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "374dc407",
   "metadata": {},
   "outputs": [],
   "source": [
    "model2.compile(\n",
    "    optimizer='adam',\n",
    "    loss='categorical_crossentropy',\n",
    "    metrics=['accuracy']\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "42a564ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.callbacks import EarlyStopping\n",
    "\n",
    "early_stop = EarlyStopping(\n",
    "    monitor='val_loss',\n",
    "    patience=5,\n",
    "    restore_best_weights=True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "d244a363",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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_1\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"sequential_1\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ conv2d_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\">222</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">222</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\">111</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">111</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\">109</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">109</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\">54</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">54</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\">52</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">52</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\">26</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">26</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</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\">24</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">24</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_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\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</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\">36864</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\">256</span>)            │     <span style=\"color: #00af00; text-decoration-color: #00af00\">9,437,440</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\">256</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\">1,285</span> │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ conv2d_3 (\u001b[38;5;33mConv2D\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m222\u001b[0m, \u001b[38;5;34m222\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;34m111\u001b[0m, \u001b[38;5;34m111\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;34m109\u001b[0m, \u001b[38;5;34m109\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;34m54\u001b[0m, \u001b[38;5;34m54\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;34m52\u001b[0m, \u001b[38;5;34m52\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;34m26\u001b[0m, \u001b[38;5;34m26\u001b[0m, \u001b[38;5;34m128\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;34m24\u001b[0m, \u001b[38;5;34m24\u001b[0m, \u001b[38;5;34m256\u001b[0m)    │       \u001b[38;5;34m295,168\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d_6 (\u001b[38;5;33mMaxPooling2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\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;34m36864\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;34m256\u001b[0m)            │     \u001b[38;5;34m9,437,440\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dropout_1 (\u001b[38;5;33mDropout\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\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;34m1,285\u001b[0m │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "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\">9,827,141</span> (37.49 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m9,827,141\u001b[0m (37.49 MB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "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\">9,827,141</span> (37.49 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m9,827,141\u001b[0m (37.49 MB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "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"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model2.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "09d4fc30",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "\u001b[1m343/343\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m561s\u001b[0m 2s/step - accuracy: 0.4284 - loss: 1.3164 - val_accuracy: 0.6012 - val_loss: 0.9587\n",
      "Epoch 2/20\n",
      "\u001b[1m343/343\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m550s\u001b[0m 2s/step - accuracy: 0.6314 - loss: 0.8724 - val_accuracy: 0.6807 - val_loss: 0.7432\n",
      "Epoch 3/20\n",
      "\u001b[1m343/343\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m564s\u001b[0m 2s/step - accuracy: 0.6745 - loss: 0.7657 - val_accuracy: 0.7309 - val_loss: 0.6519\n",
      "Epoch 4/20\n",
      "\u001b[1m343/343\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m514s\u001b[0m 1s/step - accuracy: 0.7149 - loss: 0.6792 - val_accuracy: 0.7496 - val_loss: 0.5943\n",
      "Epoch 5/20\n",
      "\u001b[1m343/343\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m452s\u001b[0m 1s/step - accuracy: 0.7257 - loss: 0.6377 - val_accuracy: 0.7419 - val_loss: 0.6185\n",
      "Epoch 6/20\n",
      "\u001b[1m343/343\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m485s\u001b[0m 1s/step - accuracy: 0.7515 - loss: 0.5971 - val_accuracy: 0.8163 - val_loss: 0.4826\n",
      "Epoch 7/20\n",
      "\u001b[1m343/343\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m508s\u001b[0m 1s/step - accuracy: 0.7921 - loss: 0.5312 - val_accuracy: 0.8393 - val_loss: 0.4266\n",
      "Epoch 8/20\n",
      "\u001b[1m343/343\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m481s\u001b[0m 1s/step - accuracy: 0.8588 - loss: 0.4019 - val_accuracy: 0.8652 - val_loss: 0.3853\n",
      "Epoch 9/20\n",
      "\u001b[1m343/343\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m470s\u001b[0m 1s/step - accuracy: 0.8770 - loss: 0.3556 - val_accuracy: 0.8929 - val_loss: 0.3656\n",
      "Epoch 10/20\n",
      "\u001b[1m343/343\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m529s\u001b[0m 2s/step - accuracy: 0.8799 - loss: 0.3463 - val_accuracy: 0.8984 - val_loss: 0.2949\n",
      "Epoch 11/20\n",
      "\u001b[1m343/343\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m524s\u001b[0m 2s/step - accuracy: 0.8931 - loss: 0.3075 - val_accuracy: 0.9001 - val_loss: 0.2998\n",
      "Epoch 12/20\n",
      "\u001b[1m343/343\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m492s\u001b[0m 1s/step - accuracy: 0.9031 - loss: 0.2830 - val_accuracy: 0.8797 - val_loss: 0.3473\n",
      "Epoch 13/20\n",
      "\u001b[1m343/343\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m501s\u001b[0m 1s/step - accuracy: 0.9022 - loss: 0.2897 - val_accuracy: 0.9026 - val_loss: 0.2906\n",
      "Epoch 14/20\n",
      "\u001b[1m343/343\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m492s\u001b[0m 1s/step - accuracy: 0.9121 - loss: 0.2567 - val_accuracy: 0.9026 - val_loss: 0.3128\n",
      "Epoch 15/20\n",
      "\u001b[1m343/343\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m518s\u001b[0m 2s/step - accuracy: 0.9131 - loss: 0.2522 - val_accuracy: 0.9192 - val_loss: 0.2950\n",
      "Epoch 16/20\n",
      "\u001b[1m343/343\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m566s\u001b[0m 2s/step - accuracy: 0.9189 - loss: 0.2415 - val_accuracy: 0.9209 - val_loss: 0.2741\n",
      "Epoch 17/20\n",
      "\u001b[1m343/343\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m545s\u001b[0m 2s/step - accuracy: 0.9232 - loss: 0.2390 - val_accuracy: 0.9243 - val_loss: 0.2520\n",
      "Epoch 18/20\n",
      "\u001b[1m343/343\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m597s\u001b[0m 2s/step - accuracy: 0.9210 - loss: 0.2312 - val_accuracy: 0.9239 - val_loss: 0.2218\n",
      "Epoch 19/20\n",
      "\u001b[1m343/343\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m604s\u001b[0m 2s/step - accuracy: 0.9244 - loss: 0.2236 - val_accuracy: 0.9311 - val_loss: 0.2232\n",
      "Epoch 20/20\n",
      "\u001b[1m343/343\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m413s\u001b[0m 1s/step - accuracy: 0.9302 - loss: 0.2068 - val_accuracy: 0.9324 - val_loss: 0.2205\n"
     ]
    }
   ],
   "source": [
    "history2 = model2.fit(\n",
    "    train_generator,\n",
    "    validation_data=validation_generator,\n",
    "    epochs=20,\n",
    "    callbacks=[early_stop]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "a13e26ab",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m74/74\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m37s\u001b[0m 493ms/step - accuracy: 0.9346 - loss: 0.2214\n",
      "Test Accuracy: 0.9345516562461853\n",
      "Test Loss: 0.22139178216457367\n"
     ]
    }
   ],
   "source": [
    "loss, accuracy = model2.evaluate(\n",
    "    test_generator\n",
    ")\n",
    "\n",
    "print(\n",
    "    \"Test Accuracy:\",\n",
    "    accuracy\n",
    ")\n",
    "\n",
    "print(\n",
    "    \"Test Loss:\",\n",
    "    loss\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd49e6f4",
   "metadata": {},
   "source": [
    "### Step 12: Accuracy Graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "6560301c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,5))\n",
    "\n",
    "plt.plot(\n",
    "    history.history['accuracy'],\n",
    "    label='Training'\n",
    ")\n",
    "\n",
    "plt.plot(\n",
    "    history.history['val_accuracy'],\n",
    "    label='Validation'\n",
    ")\n",
    "\n",
    "plt.title(\n",
    "    'Training and Validation Accuracy'\n",
    ")\n",
    "\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Accuracy')\n",
    "\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "e53b1f7a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,5))\n",
    "\n",
    "plt.plot(\n",
    "    history2.history['accuracy'],\n",
    "    label='Training'\n",
    ")\n",
    "\n",
    "plt.plot(\n",
    "    history2.history['val_accuracy'],\n",
    "    label='Validation'\n",
    ")\n",
    "\n",
    "plt.title(\n",
    "    'Training and Validation Accuracy'\n",
    ")\n",
    "\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Accuracy')\n",
    "\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21aee7c1",
   "metadata": {},
   "source": [
    "### Step 13: Loss Graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "bd425070",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,5))\n",
    "\n",
    "plt.plot(\n",
    "    history.history['loss'],\n",
    "    label='Training'\n",
    ")\n",
    "\n",
    "plt.plot(\n",
    "    history.history['val_loss'],\n",
    "    label='Validation'\n",
    ")\n",
    "\n",
    "plt.title(\n",
    "    'Training and Validation Loss'\n",
    ")\n",
    "\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Loss')\n",
    "\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "6de8583d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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qVaowbNgwrr32Wnr16uXs8gBo164dv/zyC0888QTPP/88UVFRTJgwgR07dlx0tgUvLy9+/PFHHn74YSZOnIiPjw+DBg3iwQcfJCYmplT1mM1mfvjhBx599FG++OILTCYTN954I2+88QZt2rQp0bGGDh3KrFmziIiI4Jprrinw3siRI0lISOD9999n0aJFNGvWjC+++II5c+awbNmyEtf90ksv4ePjw/Tp0/ntt9/o1KkTv/76K/369SvQ7plnniE9PZ1Zs2Yxe/Zs2rZty4IFC85bjtjLy4tPP/2Up59+mvvuu4+8vDxmzJhRaJjN/87Gjh3L7NmzmTFjBtHR0UyaNInHH3+8xJ/lYhYuXFjoIgvR0dG0aNGCZcuW8dRTT/Hpp5+SkpJC48aNmTFjBiNHjnS0HTZsGB988AHvvfceSUlJhIeHM3jwYMaPH+8YnvDwww/zww8/8Ouvv5KdnU2dOnV46aWX+M9//lPmn0nkcmcyXKkbRUSkAgwcOFDTIomIXCY0ZlZELmvnLj27e/dufv75Z7p37+6cgkREpEypZ1ZELmsRERGMHDmSevXqceDAAaZNm0Z2djabNm06b+5UERFxPxozKyKXtd69e/PVV1+RkJCAxWKhc+fOvPzyywqyIiKXCfXMioiIiIjb0phZEREREXFbCrMiIiIi4rYq3ZhZm81GXFwcgYGBJVpSUUREREQqhmEYpKamEhkZWWD56MJUujAbFxdHVFSUs8sQERERkYs4dOgQtWrVKrJNpQuz+csNHjp0iKCgICdXIyIiIiLnSklJISoqypHbilLpwmz+0IKgoCCFWREREREXVpwhoboBTERERETclsKsiIiIiLgthVkRERERcVuVbsysiIiIuB/DMMjLy8NqtTq7FCkjXl5eeHh4XPJxFGZFRETEpeXk5BAfH09GRoazS5EyZDKZqFWrFgEBAZd0HIVZERERcVk2m43Y2Fg8PDyIjIzE29tbix5dBgzD4NixYxw+fJiGDRteUg+twqyIiIi4rJycHGw2G1FRUfj5+Tm7HClDNWrUYP/+/eTm5l5SmNUNYCIiIuLyLrakqbifsuph15UhIiIiIm5LYVZERERE3JbCrIiIiIibiI6OZvLkycVuv2zZMkwmE0lJSeVWk7MpzIqIiIiUMZPJVORj/PjxpTru+vXrueeee4rdvkuXLsTHxxMcHFyq87kDzWZQQQzD0FQiIiIilUR8fLzj+ezZsxk7diy7du1ybDt7blXDMLBarXh6XjyW1ahRo0R1eHt7Ex4eXqJ93I16ZsvZ1P/tpvuk3/h+0xFnlyIiInJZMAyDjJw8pzwMwyhWjeHh4Y5HcHAwJpPJ8Xrnzp0EBgbyyy+/0K5dOywWCytWrGDv3r0MGDCAsLAwAgIC6NChA0uWLClw3HOHGZhMJj766CMGDRqEn58fDRs25IcffnC8f+4wg5kzZxISEsKiRYto2rQpAQEB9O7du0D4zsvL4+GHHyYkJIRq1arx5JNPMmLECAYOHFjqP7PypJ7Zcpacmcv+ExlsOpjETW1rObscERERt5eZa6XZ2EVOOfffE3rh51028empp57i9ddfp169elSpUoVDhw7Rt29f/u///g+LxcJnn31G//792bVrF7Vr177gcV544QVee+01Jk2axJQpUxg6dCgHDhygatWqhbbPyMjg9ddf5/PPP8dsNjNs2DCeeOIJvvzySwBeffVVvvzyS2bMmEHTpk15++23mTdvHj169CiTz13W1DNbzmKiQgDYcjjJqXWIiIiIa5kwYQLXXXcd9evXp2rVqsTExHDvvffSokULGjZsyIsvvkj9+vUL9LQWZuTIkQwZMoQGDRrw8ssvk5aWxrp16y7YPjc3l+nTp9O+fXvatm3Lgw8+yNKlSx3vT5kyhaeffppBgwbRpEkTpk6dSkhISFl97DKnntly1vp0mN0Rn0JWrhUfr9KvcCEiIiLg6+XB3xN6Oe3cZaV9+/YFXqelpTF+/HgWLFhAfHw8eXl5ZGZmcvDgwSKP06pVK8dzf39/goKCOHr06AXb+/n5Ub9+fcfriIgIR/vk5GQSExPp2LGj430PDw/atWuHzWYr0eerKAqz5axmiC/VA7w5npbDjvgU2tSu4uySRERE3JrJZCqzX/U7k7+/f4HXTzzxBIsXL+b111+nQYMG+Pr6csstt5CTk1Pkcby8vAq8NplMRQbPwtoXdyywK9Iwg3JmMpmIqRUCwOZDSU6tRURERFzXypUrGTlyJIMGDaJly5aEh4ezf//+Cq0hODiYsLAw1q9f79hmtVrZuHFjhdZREgqzFcAxblZhVkRERC6gYcOGzJ07l82bN7NlyxbuuOMOp/xq/6GHHmLixInMnz+fXbt28cgjj3Dq1CmXnWJUYbYCnLkJLNm5hYiIiIjLevPNN6lSpQpdunShf//+9OrVi7Zt21Z4HU8++SRDhgxh+PDhdO7cmYCAAHr16oWPj0+F11IcJsOdB0mUQkpKCsHBwSQnJxMUFFQh50zKyKH1hMUAbB57HSF+3hVyXhEREXeXlZVFbGwsdevWddkwdbmz2Ww0bdqU2267jRdffLHMjlvUn21J8pp6ZitAiJ83davbB3lvVe+siIiIuLADBw7w4Ycf8s8//7Bt2zbGjBlDbGwsd9xxh7NLK5TCbAWJqWVfE1k3gYmIiIgrM5vNzJw5kw4dOtC1a1e2bdvGkiVLaNq0qbNLK5T7z2vhJmKiQpi3OU43gYmIiIhLi4qKYuXKlc4uo9jUM1tBWp+1ElglG6YsIiIiUm4UZitI04ggvDxMHE/L4UhSprPLEREREbksKMxWEB8vD5pG2O/G07hZERERkbKhMFuB8lcC07hZERERkbKhMFuBzqwEpum5RERERMqCwmwFyr8JbNuRZPKsFb88nYiIiMjlRmG2AtWr7k+gxZPMXCv/JKY5uxwRERFxYd27d+fRRx91vI6Ojmby5MlF7mMymZg3b94ln7usjlMRFGYrkNlsolWUffGELYeTnFuMiIiIlJv+/fvTu3fvQt/7448/MJlMbN26tUTHXL9+Pffcc09ZlOcwfvx4Wrdufd72+Ph4+vTpU6bnKi8KsxVMN4GJiIhc/kaPHs3ixYs5fPjwee/NmDGD9u3b06pVqxIds0aNGvj5+ZVViUUKDw/HYrFUyLkulcJsBcsfN6vpuURERErJMCAn3TmPYi58dMMNN1CjRg1mzpxZYHtaWhpz5sxh4MCBDBkyhJo1a+Ln50fLli356quvijzmucMMdu/ezVVXXYWPjw/NmjVj8eLF5+3z5JNP0qhRI/z8/KhXrx7PP/88ubm5AMycOZMXXniBLVu2YDKZMJlMjnrPHWawbds2rrnmGnx9falWrRr33HMPaWlnhkyOHDmSgQMH8vrrrxMREUG1atV44IEHHOcqT1rOtoLlh9l/ElPJyMnDz1t/BCIiIiWSmwEvRzrn3M/Egbf/RZt5enoyfPhwZs6cybPPPovJZAJgzpw5WK1Whg0bxpw5c3jyyScJCgpiwYIF3HnnndSvX5+OHTte9Pg2m42bbrqJsLAw1q5dS3JycoHxtfkCAwOZOXMmkZGRbNu2jX/9618EBgby3//+l8GDB7N9+3YWLlzIkiVLAAgODj7vGOnp6fTq1YvOnTuzfv16jh49yt13382DDz5YIKz/9ttvRERE8Ntvv7Fnzx4GDx5M69at+de//nXRz3Mp1DNbwUKDfIgI9sFmwLbDmqJLRETkcnXXXXexd+9efv/9d8e2GTNmcPPNN1OnTh2eeOIJWrduTb169XjooYfo3bs333zzTbGOvWTJEnbu3Mlnn31GTEwMV111FS+//PJ57Z577jm6dOlCdHQ0/fv354knnnCcw9fXl4CAADw9PQkPDyc8PBxfX9/zjjFr1iyysrL47LPPaNGiBddccw1Tp07l888/JzEx0dGuSpUqTJ06lSZNmnDDDTfQr18/li5dWtKvrcTULegEMbVCiE9OYMvhJDrVq+bsckRERNyLl5+9h9RZ5y6mJk2a0KVLFz755BO6d+/Onj17+OOPP5gwYQJWq5WXX36Zb775hiNHjpCTk0N2dnaxx8Tu2LGDqKgoIiPP9FB37tz5vHazZ8/mnXfeYe/evaSlpZGXl0dQUFCxP0P+uWJiYvD3P9Mj3bVrV2w2G7t27SIsLAyA5s2b4+Hh4WgTERHBtm3bSnSu0lDPrBO0rh0CaPEEERGRUjGZ7L/qd8bj9HCB4ho9ejTfffcdqampzJgxg/r163P11VczadIk3n77bZ588kl+++03Nm/eTK9evcjJySmzr2n16tUMHTqUvn378tNPP7Fp0yaeffbZMj3H2by8vAq8NplM2GzlP6++wqwT5M9ooJvARERELm+33XYbZrOZWbNm8dlnn3HXXXdhMplYuXIlAwYMYNiwYcTExFCvXj3++eefYh+3adOmHDp0iPj4eMe2NWvWFGizatUq6tSpw7PPPkv79u1p2LAhBw4cKNDG29sbq9V60XNt2bKF9PR0x7aVK1diNptp3LhxsWsuLwqzTtCyVjAmExxJyuRYarazyxEREZFyEhAQwODBg3n66aeJj49n5MiRADRs2JDFixezatUqduzYwb333ltg/OnF9OzZk0aNGjFixAi2bNnCH3/8wbPPPlugTcOGDTl48CBff/01e/fu5Z133uH7778v0CY6OprY2Fg2b97M8ePHyc4+P5cMHToUHx8fRowYwfbt2/ntt9946KGHuPPOOx1DDJxJYdYJAiyeNAwNADTfrIiIyOVu9OjRnDp1il69ejnGuD733HO0bduWXr160b17d8LDwxk4cGCxj2k2m/n+++/JzMykY8eO3H333fzf//1fgTY33ngj//73v3nwwQdp3bo1q1at4vnnny/Q5uabb6Z379706NGDGjVqFDo9mJ+fH4sWLeLkyZN06NCBW265hWuvvZapU6eW/MsoBybDKOaEaZeJlJQUgoODSU5OLvEA6LL0nzlbmLPhMA9d04DHr3d+F72IiIgrysrKIjY2lrp16+Lj4+PscqQMFfVnW5K8pp5ZJ8m/CUzjZkVERERKT2HWSc5e1tZmq1Sd4yIiIiJlRmHWSRqHB2LxNJOSlcf+E+kX30FEREREzqMw6yReHmZa1LQvGbflcJJzixERERFxU04Ns8uXL6d///5ERkZiMpmYN29eke3nzp3LddddR40aNQgKCqJz584sWrSoYootB62jQgAtniAiInIxlex+9UqhrP5MnRpm09PTiYmJ4d133y1W++XLl3Pdddfx888/s2HDBnr06EH//v3ZtGlTOVdaPmJOh1ndBCYiIlK4/FWlMjIynFyJlLX8lcjOXgK3NDzLopjS6tOnD3369Cl2+8mTJxd4/fLLLzN//nx+/PFH2rRpU8bVlb/Wp28C+zsuhew8KxbPS/vDFBERudx4eHgQEhLC0aNHAfucp6YSLikrrsdms3Hs2DH8/Pzw9Ly0OOrUMHupbDYbqampVK1a9YJtsrOzC6xmkZKSUhGlFUtUVV+q+HlxKiOXnfGpjp5aEREROSM8PBzAEWjl8mA2m6ldu/Yl/8+JW4fZ119/nbS0NG677bYLtpk4cSIvvPBCBVZVfCaTiZioEJbtOsaWw0kKsyIiIoUwmUxEREQQGhpKbm6us8uRMuLt7Y3ZfOkjXt02zM6aNYsXXniB+fPnExoaesF2Tz/9NI899pjjdUpKClFRURVRYrG0Ph1mNx9KYnhnZ1cjIiLiujw8PC55fKVcftwyzH799dfcfffdzJkzh549exbZ1mKxYLFYKqiyktNNYCIiIiKl53bzzH711VeMGjWKr776in79+jm7nEuWvxLYvmPpJGfqVyciIiIiJeHUMJuWlsbmzZvZvHkzALGxsWzevJmDBw8C9iECw4cPd7SfNWsWw4cP54033qBTp04kJCSQkJBAcrL7ztNa1d+b2lX9ANh22H0/h4iIiIgzODXM/vnnn7Rp08YxrdZjjz1GmzZtGDt2LADx8fGOYAvwwQcfkJeXxwMPPEBERITj8cgjjzil/rLiWDxBK4GJiIiIlIhTx8x27969yNUfZs6cWeD1smXLyrcgJ4mJCuGHLXFsOpjk7FJERERE3IrbjZm9HLWOCgbsN4FpuT4RERGR4lOYdQHNI4PxNJs4npZNfHKWs8sRERERcRsKsy7Ax8uDJhGBAGzRFF0iIiIixaYw6yLyp+jarJvARERERIpNYdZFOBZP0E1gIiIiIsWmMOsi8qfn2nYkGatNN4GJiIiIFIfCrIuoXyMAf28PMnKs7Dma5uxyRERERNyCwqyL8DCbaHV63KxuAhMREREpHoVZF5I/bnaTwqyIiIhIsSjMupD8xRPUMysiIiJSPAqzLiS/Z3ZXYiqZOVbnFiMiIiLiBhRmXUhEsC9hQRasNoO/4pKdXY6IiIiIy1OYdTGOxRM01EBERETkohRmXYxj8QSFWREREZGLUph1MfmLJ2zRsrYiIiIiF6Uw62Ja1grGZIJDJzM5kZbt7HJEREREXJrCrIsJ8vGifo0AALYe1k1gIiIiIkVRmHVB+TeBafEEERERkaIpzLogLZ4gIiIiUjwKsy4o5qybwAzDcG4xIiIiIi5MYdYFNQkPwtvTTFJGLgdPZji7HBERERGXpTDrgrw9zTSPDAI036yIiIhIURRmXZRWAhMRERG5OIVZF+VYPEFhVkREROSCFGZdVH6Y3R6XQq7V5txiRERERFyUwqyLqlPNj2BfL3LybOyMT3V2OSIiIiIuSWHWRZlMJscUXZsPJzm1FhERERFXpTDrwlrX0uIJIiIiIkVRmHVhMboJTERERKRICrMuLD/M7jmWRmpWrnOLEREREXFBCrMurHqAhVpVfDEM2HY42dnliIiIiLgchVkXp5vARERERC5MYdbFtT69EpjGzYqIiIicT2HWxbWuHQLAlkMaZiAiIiJyLoVZF9c8MggPs4mElCwSkrOcXY6IiIiIS1GYdXF+3p40CgsEYLOGGoiIiIgUoDDrBlpHnV48QTeBiYiIiBSgMOsGYnQTmIiIiEihFGbdQP5NYFsPJ2O1Gc4tRkRERMSFKMy6gYahgfh5e5CWnce+Y2nOLkdERETEZSjMugEPs4kWNe3jZnUTmIiIiMgZCrNuovXplcB0E5iIiIjIGQqzbiI/zKpnVkREROQMhVk3EXM6zO6MTyUr1+rcYkRERERchMKsm4gM9qF6gIU8m8FfcSnOLkdERETEJSjMugmTyXRm8QQNNRAREREBFGbdim4CExERESlIYdaNxOgmMBEREZECFGbdSKuaIQAcOJHBqfQc5xYjIiIi4gIUZt1IsJ8X9ar7AxpqICIiIgIKs24nf6jBlkPJzi1ERERExAUozLqZM4snnHJuISIiIiIuwKlhdvny5fTv35/IyEhMJhPz5s276D7Lli2jbdu2WCwWGjRowMyZM8u9Tlfi6Jk9nIxhGM4tRkRERMTJnBpm09PTiYmJ4d133y1W+9jYWPr160ePHj3YvHkzjz76KHfffTeLFi0q50pdR9OIQLw8TJxMz+HwqUxnlyMiIiLiVJ7OPHmfPn3o06dPsdtPnz6dunXr8sYbbwDQtGlTVqxYwVtvvUWvXr3Kq0yXYvH0oFlEEFsOJ7P5UBJRVf2cXZKIiIiI07jVmNnVq1fTs2fPAtt69erF6tWrL7hPdnY2KSkpBR7urrXmmxUREREB3CzMJiQkEBYWVmBbWFgYKSkpZGYW/iv3iRMnEhwc7HhERUVVRKnl6syMBklOrUNERETE2dwqzJbG008/TXJysuNx6NAhZ5d0yfLD7Pa4ZHKtNucWIyIiIuJETh0zW1Lh4eEkJiYW2JaYmEhQUBC+vr6F7mOxWLBYLBVRXoWpW82fQB9PUrPy+CcxleaRwc4uSURERMQp3KpntnPnzixdurTAtsWLF9O5c2cnVeQcZrOJmFohgBZPEBERkcrNqWE2LS2NzZs3s3nzZsA+9dbmzZs5ePAgYB8iMHz4cEf7++67j3379vHf//6XnTt38t577/HNN9/w73//2xnlO5UWTxARERFxcpj9888/adOmDW3atAHgscceo02bNowdOxaA+Ph4R7AFqFu3LgsWLGDx4sXExMTwxhtv8NFHH1WaabnOpmVtRURERMBkVLJlpFJSUggODiY5OZmgoCBnl1NqR1Oy6PjyUkwm2Da+FwEWtxr+LCIiInJBJclrbjVmVs4IDfIhMtgHw4DtR9Q7KyIiIpWTwqwba107BNDiCSIiIlJ5Kcy6sTMzGiQ5tQ4RERERZ1GYdWNaCUxEREQqO4XZinDqAOTllPlhW9YMxmyCuOQsjqZklfnxRURERFydwmx5W/A4vNMa/p5X5of2t3jSKCwQ0LhZERERqZwUZstbYAQYNljzHpTDLGiOcbOHk8r82CIiIiKuTmG2vLUbBZ4+ELcJDq0t88Nr8QQRERGpzBRmy5t/NWh1m/35mmllfviYqGDA3jNrs1Wq9S9EREREFGYrRKcx9p87foCkg0W3LaHGYYH4eJlJzcoj9kR6mR5bRERExNUpzFaEsGZQr7t97Oy6D8v00J4eZlrWtPfObj6YVKbHFhEREXF1CrMVJb93duOnkFO2Pai6CUxEREQqK4XZitLweqhaD7KSYctXZXpoLZ4gIiIilZXCbEUxm8/0zq6ZDjZbmR269ekw+3d8Ctl51jI7roiIiIirU5itSK2HgCUITuyGvUvL7LC1qvhSzd+bXKvB33EpZXZcEREREVenMFuRLIHQdrj9eRlO02UymTTUQERERColhdmK1vFfYDLbe2aP7iyzw565CUyLJ4iIiEjloTBb0apEQ5N+9udrp5fZYR2LJ6hnVkRERCoRhVlnyL8RbMvXkHGyTA6Z3zO773g6yRm5ZXJMEREREVenMOsMdbpAeCvIy7TPO1sGqvh7E13ND9B8syIiIlJ5KMw6g8kEV9xvf77uQ7CWTU+qbgITERGRykZh1lla3AT+oZByBHb8UCaH1EpgIiIiUtkozDqLpwU6jLY/X1M2N4Ll98xuPpSMYRhlckwRERERV6Yw60zt7wIPbzi8Dg7/ecmHax4ZhKfZxPG0bI4kZZZBgSIiIiKuTWHWmQJCoeWt9udlsIiCj5cHTSOCANhySPPNioiIyOVPYdbZOt1n//n3PEiJu+TDOeab1bhZERERqQQUZp0tohXUuRJsebD+o0s+XP5NYJs1o4GIiIhUAgqzruCK04so/DkDcjIu6VBtaocAsO1wMnlW2yUWJiIiIuLaFGZdQeM+EFIHMk/Ctm8u6VD1qgcQYPEkM9fK7qNpZVSgiIiIiGtSmHUFZg/odK/9+ZrpcAnTapnNJlrVOj1uVkMNRERE5DKnMOsq2gwD7wA4tgP2LbukQzlWAtNNYCIiInKZU5h1FT7B9kALlzxNV/5NYJsOJl1aTSIiIiIuTmHWlXS8BzDB7kVwfE+pD5N/E9g/iakkZ+aWTW0iIiIiLkhh1pVUqw+Netufr3u/1IcJC/KhbnV/bAY88vUmzWogIiIily2FWVeTP03Xpi8hM6nUh3lrcGt8vMws23WM5+Ztx7iEm8pEREREXJXCrKupexWENofcdNj0eakP0zoqhClD2mI2wdfrDzHlf6UftiAiIiLiqhRmXY3JBFecXuJ27QdgzSv1oa5rFsYLA1oA8Obif/h2w+GyqFBERETEZSjMuqKWt4JfNUg+CLt+vqRD3XlFHcZ0rw/AU99t5Y/dx8qiQhERERGXoDDrirx8of1d9ueXOE0XwH+ub8yA1pHk2QzGfLGRv+NSLvmYIiIiIq5AYdZVtR8NZk84uAriNl/SocxmE6/d0orO9aqRlp3HqJnrOJKUWTZ1ioiIiDiRwqyrCoqA5jfZn6+dfsmHs3h6MP3OdjQKCyAxJZtRM9ZpDloRERFxewqzrix/mq5t30Jq4iUfLtjXi5mjOhIWZOGfxDTu/fxPsvOsl3xcEREREWdRmHVlNdtC1BVgy4U/Py6TQ0aG+DJjZEcCLJ6s2XeS/8zZis2mOWhFRETEPSnMurr8abrWfwy5WWVyyGaRQUwb1hZPs4kftsTx2qJdZXJcERERkYqmMOvqmvSHoFqQcRy2f1dmh+3WsAav3twKgOm/7+Wz1fvL7NgiIiIiFUVh1tV5eEKne+zP10yDMlyW9uZ2tXj8ukYAjP/hL379K6HMji0iIiJSERRm3UHb4eDlB4nbYP+KMj30g9c0YEjHKGwGPPz1JjYdPFWmxxcREREpTwqz7sC3CsQMsT8vg2m6zmYymXhxQAt6NK5BVq6N0Z/+yf7j6WV6DhEREZHyojDrLjqdvhFs5wI4GVumh/b0MDP1jra0rBnMyfQcRsxYx4m07DI9h4iIiEh5UJh1FzUaQYPrAAPWfVDmh/e3ePLxyPbUquLLgRMZjP70TzJzNAetiIiIuDaFWXeSP03Xxs8hK6XMDx8a6MPMUR0J9vVi86EkHv56E1bNQSsiIiIuTGHWndS/Fqo3hpxU2DyrXE7RIDSAj0a0x9vTzOK/E3nhx78wynAGBREREZGy5PQw++677xIdHY2Pjw+dOnVi3bp1RbafPHkyjRs3xtfXl6ioKP7973+TlVU2iwm4PJPpTO/s2ulgK59hAB2iqzJ5cGtMJvhs9QE+WL6vXM4jIiIicqmcGmZnz57NY489xrhx49i4cSMxMTH06tWLo0ePFtp+1qxZPPXUU4wbN44dO3bw8ccfM3v2bJ555pkKrtyJWt0OPiFwKhb+WVRup+nbMoLn+jUDYOIvO5m/+Ui5nUtERESktJwaZt98803+9a9/MWrUKJo1a8b06dPx8/Pjk08+KbT9qlWr6Nq1K3fccQfR0dFcf/31DBky5KK9uZcVbz9oN9L+fM175Xqq0VfW5a6udQH4z5ytrN57olzPJyIiIlJSTguzOTk5bNiwgZ49e54pxmymZ8+erF69utB9unTpwoYNGxzhdd++ffz888/07dv3gufJzs4mJSWlwMPtdfwXmDxg/x+QsL1cT/Vcv6b0bRlOjtXGPZ//yT+JqeV6PhEREZGScFqYPX78OFarlbCwsALbw8LCSEgofFnVO+64gwkTJnDllVfi5eVF/fr16d69e5HDDCZOnEhwcLDjERUVVaafwymCa0GzAfbna6eV66nMZhNv3taa9nWqkJqVx8hP1pGYUknGKIuIiIjLK1WYPXToEIcPH3a8XrduHY8++igffFD285+ebdmyZbz88su89957bNy4kblz57JgwQJefPHFC+7z9NNPk5yc7HgcOnSoXGusMFeMsf/cOgfSj5frqXy8PPhweHvq1fAnLjmLkTPWk5qVW67nFBERESmOUoXZO+64g99++w2AhIQErrvuOtatW8ezzz7LhAkTinWM6tWr4+HhQWJiYoHtiYmJhIeHF7rP888/z5133sndd99Ny5YtGTRoEC+//DITJ07EZrMVuo/FYiEoKKjA47JQqwPUbAfWbPhzRrmfroq/N5+O6kj1AAs74lO4/8uN5FoL/85FREREKkqpwuz27dvp2LEjAN988w0tWrRg1apVfPnll8ycObNYx/D29qZdu3YsXbrUsc1ms7F06VI6d+5c6D4ZGRmYzQVL9vDwAKh8c6GaTHDF/fbn6z+EvJxyP2VUVT8+GdkeP28P/th9nKe+21b5vncRERFxKaUKs7m5uVgsFgCWLFnCjTfeCECTJk2Ij48v9nEee+wxPvzwQz799FN27NjBmDFjSE9PZ9SoUQAMHz6cp59+2tG+f//+TJs2ja+//prY2FgWL17M888/T//+/R2htlJpNgACIyAtEf76vkJO2apWCO/e0RYPs4nvNh7mrSW7K+S8IiIiIoXxLM1OzZs3Z/r06fTr14/Fixc7xqzGxcVRrVq1Yh9n8ODBHDt2jLFjx5KQkEDr1q1ZuHCh46awgwcPFuiJfe655zCZTDz33HMcOXKEGjVq0L9/f/7v//6vNB/D/Xl4QYe74X8v2qfpanWbvce2nPVoEspLA1vw9NxtvLN0N5HBPtzesXa5n1dERETkXCajFL8nXrZsGYMGDSIlJYURI0Y45oV95pln2LlzJ3Pnzi3zQstKSkoKwcHBJCcnXx7jZ9NPwFvNIC8L7loEta+osFO/8esupvxvDx5mEx+NaE+PxqEVdm4RERG5fJUkr5UqzAJYrVZSUlKoUqWKY9v+/fvx8/MjNNR1Q81lF2YBfngYNn5qH3Zw22cVdlrDMHh8zhbmbjyCn7cHs+/pTMtawRV2fhEREbk8lSSvlWrMbGZmJtnZ2Y4ge+DAASZPnsyuXbtcOshetvKn6drxIyQdrLDTmkwmXrmpFVc2qE5GjpVRM9dz6GRGhZ1fREREpFRhdsCAAXz2mb0HMCkpiU6dOvHGG28wcOBApk0r30n8pRChTaFedzBssO7DCj21t6eZacPa0iQ8kONp2YycsY6kjPKfWUFEREQEShlmN27cSLdu3QD49ttvCQsL48CBA3z22We88847ZVqgFFP+NF0bP4XstAo9daCPFzNHdSQi2Ie9x9IZMWM9p9IVaEVERKT8lSrMZmRkEBgYCMCvv/7KTTfdhNls5oorruDAgQNlWqAUU4ProGp9yEqGLV9V+OnDg32YOaojIX5ebDmUxG3vryY+ObPC6xAREZHKpVRhtkGDBsybN49Dhw6xaNEirr/+egCOHj16+dxU5W7MZuh0n/352ulwgRXRylPj8EDm3NuZ8CAfdh9N45Zpq9l7rGJ7iUVERKRyKVWYHTt2LE888QTR0dF07NjRsWLXr7/+Sps2bcq0QCmB1neAJRhO7IG9Sy/evhw0DAvk2zGdqVfdnyNJmdw6fTXbDic7pRYRERG5/JV6aq6EhATi4+OJiYlxLGywbt06goKCaNKkSZkWWZYuy6m5zrboWVg9FepfA3dWzKpghTmRls3IGevZdiQZf28PPhzeni4NqjutHhEREXEfFTLPbL7Dhw8DUKtWrUs5TIW57MPsqQPwTmv7zAb3r7HPdOAkadl53PPZn6zaewJvDzPvDGlN7xYRTqtHRERE3EO5zzNrs9mYMGECwcHB1KlThzp16hASEsKLL76IzQljNeUsVepAk37252unO7WUAIsnM0Z1oHfzcHKsNu7/ciNfrau4eXBFRETk8leqMPvss88ydepUXnnlFTZt2sSmTZt4+eWXmTJlCs8//3xZ1ygllT9N15bZkPiXU0uxeHrw7tC2DOkYhc2Ap+du493f9nCJvxAQERERAUo5zCAyMpLp06dz4403Ftg+f/587r//fo4cOVJmBZa1y36YAYBhwMx+cGAl+FaBO+dBZGsnl2Tw+q+7ePe3vQCMvrIuz/ZtitlscmpdIiIi4nrKfZjByZMnC73Jq0mTJpw8ebI0h5SyZDLB7V9CzXaQeQo+uxEO/+nkkkz8p1cTnutnH8P78YpYnvh2C7lWDUsRERGR0itVmI2JiWHq1KnnbZ86dSqtWrW65KKkDOT3yEZdYV9I4bOBcGC1s6vi7m71eOPWGDzMJuZuPMJ9n28gM8fq7LJERETETZVqmMHvv/9Ov379qF27tmOO2dWrV3Po0CF+/vlnx1K3rqhSDDM4W3YafHU77P8DvPzhjq+h7lXOroqlOxK5/8uNZOfZ6BBdhY9GdCDY18vZZYmIiIgLKPdhBldffTX//PMPgwYNIikpiaSkJG666Sb++usvPv/881IVLeXEEgB3fGOfdzY3Hb68FfYscXZVXNs0jC/u7kSgjyfr959i8PurOZqS5eyyRERExM1c8jyzZ9uyZQtt27bFanXdXxtXup7ZfLlZMGcE/LMQPLzhts+hcW9nV8WO+BSGf7KOY6nZ1K7qx+ejO1Knmr+zyxIREREnKveeWXFDXj72ANu0P1hzYPYw+PsHZ1dF04ggvruvC7Wr+nHwZAY3T1vN33Epzi5LRERE3ITCbGXi6Q23zIAWN4MtF+aMhG3fOrsqalfz49sxnWkaEcTxtGwGf7CadbGaFUNEREQuTmG2svHwgps+hJg7wLDC3H/B5lnOrorQQB++vucKOkZXJTUrjzs/XsuSvxOdXZaIiIi4OM+SNL7pppuKfD8pKelSapGKYvaAAe/ag+3GT2He/fahB+1GOrWsYF8vPhvdkQdnbWTJjqPc+8UGXr25Fbe0q+XUukRERMR1lSjMBgcHX/T94cOHX1JBUkHMZrhhMnhaYN0H8OMjkJcDne5xalk+Xh5MH9aOJ7/bxncbD/PEnC0kZeRwd7d6Tq1LREREXFOZzmbgDirtbAYXYhjw63Ow+vQiGNe/BF0ecm5NgM1mMPGXHXz4RywAY7rX57+9GmMyaflbERGRy51mM5DiM5nsAbbbE/bXvz4Hyyc5tybAbDbxbL9mPNXHvmzytGV7eXruNvK0/K2IiIicRWFW7IH22uehx7P21/97Cf73f/ZeWye77+r6vHpzS8wm+Hr9IR6YtZGsXNedx1hEREQqlsKsnHH1f6HnC/bny1+DJeNcItAO7lCb94a2w9vTzKK/Ehk1Yz2pWbnOLktERERcgMKsFHTlo9D7FfvzlW/DwqdcItD2bhHOzFEdCLB4snrfCYZ8uIbjadnOLktEREScTGFWznfFGOj3pv352unw07/B5vyxql3qV+fre66gmr8324+kcOv01Rw+leHsskRERMSJFGalcB1G2+eixQQbZsAPD4LN+WNVW9QMZs59nakZ4kvs8XRunraKfxJTnV2WiIiIOInCrFxYm2Fw0wdgMsPmL+H7e8Ga5+yqqFcjgO/GdKFRWACJKdncOn01Gw6ccnZZIiIi4gQKs1K0VrfBLZ+A2RO2zYFvR9kXV3Cy8GAfvrm3M21rh5Ccmcuwj9YyZeluTqY7vzYRERGpOFo0QYpn588wZ4R92dtGfeC2T+2rhzlZRk4e93+5kWW7jgFg8TRzU9tajL4ymgahgU6uTkREREqjJHlNYVaKb/cS+PoOsGZDg54w+Avw8nV2VeRZbfy0NZ6PV8Sy7UiyY/vVjWow+sq6dGtYXSuHiYiIuBGF2SIozF6ifctg1u2Qlwl1r4IhX4O3v7OrAsAwDNbvP8XHK/bx69+JjhnFGoUFcFfXugxsUxMfLw/nFikiIiIXpTBbBIXZMrB/Jcy6DXLSoHZnuOMb8HGt7/LgiQxmrIrlm/WHSM+xz8JQ1d+bYZ1qM6xzHUIDfZxcoYiIiFyIwmwRFGbLyKF18MXNkJ0CNdvDsO/AN8TZVZ0nJSuXb9YfYsbK/RxJygTA28NM/5hIRl9Zl2aRugZERERcjcJsERRmy9CRjfD5IMhKgogYuHMe+FV1dlWFyrPaWPRXIh+v2MfGg0mO7V3qV2P0lXXp0TgUs1njakVERFyBwmwRFGbLWMI2+GwAZJyAsBb2QBtQw9lVFWnTwVN8vCKWX7YnYLXZL/961f0Z1TWam9vVws/b08kVioiIVG4Ks0VQmC0HR3fApzdC+lGo3hhG/ACB4c6u6qKOJGXy2ar9zFp3kNQs+2IQwb5eDOlYmxFd6hAR7PyZGkRERCojhdkiKMyWk+N74NP+kBoH/jXg+v+zL7jgBlNipWfnMefPQ8xYtZ8DJzIA8DSb6NsygtFX1iUmKsS5BYqIiFQyCrNFUJgtRydjYdZgOL7L/jq6G/R7A2o0dm5dxWS1GSzdkcjHK2JZG3vSsb1DdBVGX1mX65qF46FxtSIiIuVOYbYICrPlLC8HVr0DyydBXhaYvaDLQ3DVf8Dbz9nVFdv2I8l8siKWH7fGkWu1/xWJqurLyC51ua19LQJ9vJxcoYiIyOVLYbYICrMV5NR++Pm/sHuR/XVIbegzCRr3dmpZJZWYksVnq/fz5dqDJGXkAhBg8WRwhyhGdokmqqr7BHQRERF3oTBbBIXZCmQYsHMB/PJfSDli39bkBuj9CoREObe2EsrMsTJ302E+WRHL3mPpAJhN0Kt5OKO61qVDdBUtmSsiIlJGFGaLoDDrBNlp8PursOY9sOWBlx90fwquuB883OvX9Tabwe+7j/HJilj+2H3csb15ZBAju0TTPyZSS+aKiIhcIoXZIijMOlHi37DgMTi42v66RlO44U2o08W5dZXSroRUZq6KZe7GI2Tn2QCo5u/NHZ1qM+yKOoQFaclcERGR0lCYLYLCrJPZbLBlFvz6PGSenjGg9VC4bgL4V3dubaV0Kj2Hr9cf4vPV+4lLzgLOTO01sms0bWtXcXKFIiIi7kVhtggKsy4i4yQsGQ8bP7W/9q0CPcdDm+FgNjuzslLLs9r49e9EZq7cz7r9Z6b2iokKYVSXaPq2jMDb0z0/m4iISEVSmC2CwqyLObQOfvo3JG63v67V0T70ILylc+u6RNuPJDNz1X5+2BxHjtU+BKFGoIVhnepwR6fa1Ai0OLlCERER16UwWwSFWRdkzYN178NvL0NOGpg8oNN90ONpsAQ6u7rzZZyEAysh9g/wtMC1Yy94I9vxtGy+WnuQz9cc4GhqNgDeHmZuiIlgVJe6tKwVXJGVi4iIuAWF2SIozLqw5COw6Gn4e779dWCEfRqvZgOcuyxudpr9prXY3yF2OcRvBc76a9P9afvsDEXIybPxy/Z4Zq7az6aDSY7t7etUYWTXaHo1D8fLQ0MQREREQGG2SAqzbmD3Evj5cfvCCwANekLfSVC1XsWcPzcLDq+zB9fY5XBkg31KsbNVb2xfpnfHD/ae5LsXQ812xTr85kNJzFwZy4Jt8Y7VxSKCfRh2RR2GdKxNVX/vsv5EIiIibsWtwuy7777LpEmTSEhIICYmhilTptCxY8cLtk9KSuLZZ59l7ty5nDx5kjp16jB58mT69u1brPMpzLqJ3ExY8Zb9Yc0BDwt0exyufNT+q/2yZM2FuE1nel4PrgVrdsE2IXWg7lVQ92qo2w0Cw+2LQnw7Cv76Hqo1hHuXl2jJ3qMpWXyx9iCz1h7geFoOABZPMwNb12Rk12iaRuj6FBGRysltwuzs2bMZPnw406dPp1OnTkyePJk5c+awa9cuQkNDz2ufk5ND165dCQ0N5ZlnnqFmzZocOHCAkJAQYmJiinVOhVk3c3yPvZd23zL766r1od8bUL9H6Y9ps0HitjM9rwdW2cfqni0g/HR4vcoeXqtEF36sjJPwXmdIS4CO90Lf10pcTnaelZ+2xDNjVSzbj6Q4tl9Rryoju9TlumZheJi1upiIiFQebhNmO3XqRIcOHZg6dSoANpuNqKgoHnroIZ566vwxiNOnT2fSpEns3LkTL6/SrRylMOuGDAO2fweLnoG0RPu2FjdDr5ftPaTF2f/4P6fD6++wfwVknirYxrcKRHc70/tavWHxx+nuXgJf3mx/fuf3UP+a4n+2AmUabDhwihmr9rNwewJWm/2vZs0QX0Z0qcPg9rUJ9nOvFdNERERKwy3CbE5ODn5+fnz77bcMHDjQsX3EiBEkJSUxf/788/bp27cvVatWxc/Pj/nz51OjRg3uuOMOnnzySTw8Cl9CNDs7m+zsM78yTklJISoqSmHWHWUlw//+D9Z/CIYNLEFwzXPQ4W4wn/Pnf2r/mZ7X2OVnQnA+7wCo0/VM72tYi0ub3/anx+DPjyEwEu5fZQ/HlyAuKZMv1hzgq3UHOZWRC4Cvlwc3ta3JyC7RNAxzwVkeREREyohbhNm4uDhq1qzJqlWr6Ny5s2P7f//7X37//XfWrl173j5NmjRh//79DB06lPvvv589e/Zw//338/DDDzNu3LhCzzN+/HheeOGF87YrzLqxuM32ZXGPbLC/Dm9lX0Es/diZca9JBwvu4+kDUZ3O9LxGtr7gdFqlkpMO07vByb3Q8la4+aMyOWxWrpX5m48wY+V+diakOrbf0CqCFwe0oIpuFhMRkcvQZRtmGzVqRFZWFrGxsY6e2DfffJNJkyYRHx9f6HnUM3uZsllhw0xY+oK9x/ZcZk+o2f5Mz2utDuDlU741HVoPn1xv7zW+ZQa0uKnMDm0YBmv2nWTGyliW7EjEZkBooIXXb43hqkY1yuw8IiIirqAkYdazgmo6T/Xq1fHw8CAxseCvfxMTEwkPL3wcZEREBF5eXgWGFDRt2pSEhARycnLw9j6/l8pisWCxaLWly47ZAzqMhqb94dfn7VNkVW90pue19hVgCajYmqI6QLcnYPlr9lXNaneGoIgyObTJZKJz/Wp0rl+NbYeTeXT2JvYeS2f4J+sY2SWap/o0wcer8KE2IiIilzOnzdLu7e1Nu3btWLp0qWObzWZj6dKlBXpqz9a1a1f27NmDzWZzbPvnn3+IiIgoNMhKJRAQCje9D8/Ewb2/w/UvQsOeFR9k8139X4hoDVlJMP8B+81nZaxlrWB+eqgbwzvXAWDmqv30n7KC7UcK6aEWERG5zDl1yaHHHnuMDz/8kE8//ZQdO3YwZswY0tPTGTVqFADDhw/n6aefdrQfM2YMJ0+e5JFHHuGff/5hwYIFvPzyyzzwwAPO+gjiKpy5QtjZPLzgpg/sY3T3LoX1ZTN29ly+3h5MGNCCmaM6UCPQwu6jaQx6byXTlu11zIIgIiJSGTg1zA4ePJjXX3+dsWPH0rp1azZv3szChQsJCwsD4ODBgwXGwkZFRbFo0SLWr19Pq1atePjhh3nkkUcKncZLxGlqNIae4+3Pf33ePlduOeneOJRFj15Fr+Zh5FoNXl24kyEfrOHQyYxyO6eIiIgrcfoKYBVN88xKhbDZ4POB9tkVaraDu34Fj/Ibom4YBnM2HOaFH/4iPcdKoMWTFwY0Z1CbmphcpddaRESkmEqS15zaMyty2TKbYeB7YAm2TyG24s1yPZ3JZOK29lH88shVtKtThdTsPB77ZgsPfrWJpIyccj23iIiIMynMipSX4FrQd5L9+e+vQtymcj9l7Wp+zL7nCp64vhGeZhMLtsbTe/IfrNh9vNzPLSIi4gwKsyLlqdVt0GwA2PJg7j2Qm1nup/T0MPPgNQ2Ze38X6lX3JyEli2Efr2XCj3+TlWst9/OLiIhUJIVZkfJkMsENkyEgDI7/A0vOX42uvLSqFcJPD1/JsCtqA/DJylhunLqCv+NSKqwGERGR8qYwK1Le/KrCgHftz9dOg33LKu7U3p68NLAlM0Z2oHqAhX8S0xj47kre/11TeImIyOVBYVakIjS8DtrfZX8+737ITKrQ0/doEsqiR7txXbMwcqw2Jv6ykzs+XMORpPIf9iAiIlKeFGZFKsr1L0HVepByBH75b4WfvlqAhQ/ubMerN7fEz9uDtbEn6T15OfM3H6nwWkRERMqKwqxIRfH2h0EfgMkMW2fDX/MqvASTycTgDrX55ZFutKkdQmpWHo98vZmHvtpEckZuhdcjIiJyqRRmRSpSVAe48jH7858ehdQEp5RRp5o/c+7tzGPXNcLDbOLHLXH0fns5q/ZoCi8REXEvCrMiFe3qJyG8FWSegvkPgpMW4fP0MPPwtQ35bkwX6lb3Jz45izs+WstLP2kKLxERcR8KsyIVzdMbbvoQPCywZzH8+YlTy2kdFcKCh6/kjk72Kbw+WhHLwHdXsjNBU3iJiIjrU5gVcYbQJtBznP35r8/Bib1OLcfP25OXB7Xk4xHtqR7gzc6EVG6cspIPl+/Dpim8RETEhSnMijhLpzEQ3Q1yM+D7e8Ga5+yKuLZpGAsfvYqeTUPJsdr4v593MPSjtcRpCi8REXFRCrMizmI2w8BpYAmCw+th5VvOrgiA6gEWPhzenok3tcTXy4PV+07Qe/JyftgS5+zSREREzqMwK+JMIVHQd5L9+bJXIG6zU8vJZzKZGNKxNj8/0o3WUSGkZOXx8Feb+OiPfc4uTUREpACFWRFnazUYmt4Itjz7cIPcLGdX5FC3uj/f3teZMd3rA/DKLzvZcijJuUWJiIicRWFWxNlMJrhhMviHwrGdsHSCsysqwNPDzH97NaZvy3DybAYPf72JtGznj+8VEREBhVkR1+BfDQZMtT9f8y7ELnduPecwmUxMHNSKmiG+HDiRwdh5251dkoiICKAwK+I6GvWCdiPtz78fA1nJTi3nXMF+Xrx9e2vMJpi76QhzNx52dkkiIiIKsyIu5fr/gyp1IeUw/PKks6s5T/voqjzasxEAz8/bTuzxdCdXJCIilZ3CrIgrsQTAoPfBZIYtX8HfPzi7ovM80KMBHetWJT3HysNfbSInz+bskkREpBJTmBVxNbU7QddH7c9/fARSE51azrk8zCbevr01IX5ebDuSzOu/7nJ2SSIiUokpzIq4ou5PQ3hLyDwJPzwEhmstKRsR7MtrN7cC4IPl+/j9n2NOrkhERCorhVkRV+TpDYM+AA8L7F4EGz91dkXnub55OMM71wHg8W82cyw128kViYhIZaQwK+KqwprBtWPtzxc+Ayddb/WtZ/o2pUl4IMfTcnjsm83YbK7VgywiIpc/hVkRV3bF/RDdDXLT4fv7wGZ1dkUF+Hh5MGVIG3y8zPyx+zgfrXC9wC0iIpc3hVkRV2Y2w8D3wBIEh9bCysnOrug8DcMCGXtDcwBeW7hLy92KiEiFUpgVcXUhtaHPq/bnv02E+K3OracQQzpG0aeFlrsVEZGKpzAr4g5ihkCTG8CWC3PvgdwsZ1dUgMlk4pWbtNytiIhUPIVZEXdgMkH/t8E/FI7tsE/XpeVuRUREFGZF3IZ/dbhxiv35tm9gSnvY/BXYXGcFrnOXu92v5W5FRKScKcyKuJPGveHO76FaQ0g/CvPugxm9IX6LsytzKLDc7dda7lZERMqXwqyIu6l/DYxZBT1fAC9/+ywHH3SHnx6DjJPOrq7AcrdbD2u5WxERKV8KsyLuyNMbrnwUHvoTWtwChg3+/BimtIMNM50+H62WuxURkYqiMCvizoIi4ZaPYcRPUKMpZJ6EHx+Bj66FwxucWpqWuxURkYqgMCtyOajbDe77A3pNtC+wELcJProG5j8I6cedVtbZy90+PmeLlrsVEZEypzArcrnw8ILO98ODf0LMHfZtmz6HKW1h3YdgrfiFDM5e7nb5P8f4eEVshdcgIiKXN4VZkctNYBgMmgZ3LYLwlvb5aH9+wn6T2IHVFV5OgeVuF+1k6+GkCq9BREQuXwqzIper2lfAPb9DvzfAJwQSt9mn8Zp7D6QmVGgp+cvd5loNHv5Ky92KiEjZUZgVuZyZPaDD3fDQRmg7AjDB1tn2BRdWTQVrboWUcfZyt/u13K2IiJQhhVmRysC/Gtz4DvxrKdRsBzmp8OuzMP1K2Pd7hZSg5W5FRKQ8KMyKVCY128HoJfZlcf2qwbGd8NmNMGckJJd/uNRytyIiUtYUZkUqG7MZ2g6HhzZAx3vAZIa/voepHeCPNyGvfOeD1XK3IiJSlhRmRSor3yrQdxLcuxxqd4bcDFj6ArzXGXYvKbfTnrvc7Rta7lZERC6BwqxIZRfeEkb9AoM+gIAwOLkXvrwZvh4Kp/aXyykjgn159fRyt+8v38dyLXcrIiKlpDArImAyQcxg+4ILnR8Ekwfs/Ane7QTLXoHczDI/Za/m4dx5hX2528e+2aLlbkVEpFQUZkXkDJ8g6PV/MGYlRHeDvCxYNtEeanf+DEbZLkf7bL/85W6ztdytiIiUisKsiJwvtCmM+BFumQFBNSHpAHw9BD64GrbMhrycMjmNlrsVEZFLpTArIoUzmaDFTfDAOrjy3+DpA/Fb4Pt7YHIL+H0SpJ+45NNouVsREbkUJsMo498buriUlBSCg4NJTk4mKCjI2eWIuI/0E7BhBqz7ENJOL4fr6QOtBsMVY+y9uaVkGAb3f7mRX7YnEF3Nj58e7kaAxbOMChcREXdTkrymMCsiJZOXA3/Pg9XvQvzmM9vr9YDOD0D9a+1z2ZZQckYufd5eTlxyFje1rcmbt7Uuq4pFRMTNlCSvaZiBiJSMpze0ug3uWQajFkLTG+0LL+z7Db68Bd7rBOs/hpySre4V7OfF20Pa2Je73XiE7zdpuVsREbk4lwiz7777LtHR0fj4+NCpUyfWrVtXrP2+/vprTCYTAwcOLN8CReR8JhPU6QyDP4eHN9mn9LIEwfF/YMFj8GYzWDIeko8U+5Adzlru9rnvtdytiIhcnNPD7OzZs3nssccYN24cGzduJCYmhl69enH06NEi99u/fz9PPPEE3bp1q6BKReSCqkTbp/T691/Q+1X766wkWPEWvN0Kvh0NhzcU61Ba7lZERErC6WNmO3XqRIcOHZg6dSoANpuNqKgoHnroIZ566qlC97FarVx11VXcdddd/PHHHyQlJTFv3rxinU9jZkUqgM0K/yyE1e/BgRVntkd1st8s1qQ/eFz4Bq/45Ez6vP0HSRm53H1lXZ7t1xSTyVQBhYuIiCtwmzGzOTk5bNiwgZ49ezq2mc1mevbsyerVqy+434QJEwgNDWX06NEXPUd2djYpKSkFHiJSzswe0KQfjFoA9y6HmDvA7AWH1sKckfBOa1j5DmQmFbr72cvdfrQilhunrmTx34lUsvtVRUSkGJwaZo8fP47VaiUsLKzA9rCwMBISEgrdZ8WKFXz88cd8+OGHxTrHxIkTCQ4OdjyioqIuuW4RKYGIGBg0zT4E4ar/gl81SD4Ei5+3j6v9+T9wYu95u/VqHs4zfZvg6+XBtiPJ/OuzP+n3zgoWbo/XSmEiIuLg9DGzJZGamsqdd97Jhx9+SPXq1Yu1z9NPP01ycrLjcejQoXKuUkQKFRgG1zwL//4bbpwCoc0gNx3WfQBT2sGs2yF2eYElc++5qj4rnuzBmO718ff24O/4FO77YiN93v6Dn7bGYVWoFRGp9Jw6ZjYnJwc/Pz++/fbbAjMSjBgxgqSkJObPn1+g/ebNm2nTpg0eHh6ObTab/eYQs9nMrl27qF+/fpHn1JhZERdhGBD7u31c7e5FZ7aHtbSPq21xM3j5ODafSs/hk5WxzFy5n9TsPAAahAbw0DUNuKFVJB5mjakVEblcuNWiCZ06daJjx45MmTIFsIfT2rVr8+CDD553A1hWVhZ79uwpsO25554jNTWVt99+m0aNGuHt7V3k+RRmRVzQ8T2wdhpsngW5GfZt/jWg/WhoPwoCwx1NkzNymbEqlk9WxJKSZQ+19ar780CPBgxoHYmnh1v9wklERArhVmF29uzZjBgxgvfff5+OHTsyefJkvvnmG3bu3ElYWBjDhw+nZs2aTJw4sdD9R44cqdkMRC4Xmadgw6f2oQcpZ81PWyUaIttCzXb2R0QrUmzefLZqPx+tiCUpIxeA2lX9eLBHAwa1rYmXQq2IiNsqSV5z+uLngwcP5tixY4wdO5aEhARat27NwoULHTeFHTx4EHMplsYUETfkWwWufNS+LO6OH2HNNDi8Dk7ttz/+mmtvZzITFNqMB2u25e7rW/PDsQgmbTJz8GQG//1uK28v3c0DPRpwc7uaWDw9ijihiIi4O6f3zFY09cyKuJnMJIjbBHEb4chGOLIBUuPPa2Z4+pLo35j/pdRiTXY0m4365AXWZkyPBtzaPgofL4VaERF34VbDDCqawqzIZSAl7kywjdsIRzZBdvJ5zU4ZAWy11WOPVyNqt+xGt+698KkS4YSCRUSkJBRmi6AwK3IZstng5F57uD0dco2ErZisOec1TbWE4xvdAc+o9vbxt5GtwRJY8TWLiMgFKcwWQWFWpJLIy4Gjf5F76E8Obv0Dc9xG6tgOYzad+0+eCWo0Pn1zWVv7jWZhLcCz6JlRRESk/CjMFkFhVqRyyrXa+HHdPyxb9isRaX/TyryXNh77iOT4+Y09LBAzGHq/At7+FV+siEglpzBbBIVZkcotz2pj/uY4pv62h9jj6VQnmSt89jMs6jjtPGPxit8IWUn2xtUbw60zIayZM0sWEal0FGaLoDArIgBWm8FPW+OY8r897DmaBkCgxZORXepwT9QRAn++H9ISwNMX+r0BbYY6uWIRkcpDYbYICrMicjarzeCX7fFMWbqHXYmpAPh7e3BdHTP3n3qNRmnrAYiLHsSJq1+mRtWqVA/w1kpjIiLlSGG2CAqzIlIYm83g178TeGfpHv6OTwHAhI37PX7gMc85eJgM/rHV5P7cR9hLLar5WwgNtBAWZCE00IfQIAuhQT6EBuZv96F6gAVvT4VeEZGSUpgtgsKsiBTFMAzWxp5k37F0jqZmcTQ1m5DEtYw++n9Us50kw7DwfO4ovrNdVazjVfX3tgfc00HXEX7P2hYaZKl8K5VlnARLEHg4fSFKEXFBCrNFUJgVkVJJOwZz/wX7fgPgVKNb2drqORIyzRxNyeZoajaJKfbweyw1m6OpWeRai//Pa4ifFxHBvvRrGc5tHaIIDfQpr0/iXCf2wvJJsHU2VImGPpOgYU9nVyUiLkZhtggKsyJSajYb/PEGLHsZDBvUaAK3fgqhTQppapCUmcvR1CwSU7I5ejroOn6eDryJKdnk5NkK7OtpNtG7RTjDrqhDp7pVMZlMFfUJy8/JWFj+Omz5Cgxrwfea9odeEyEkyjm1iYjLUZgtgsKsiFyy2D/gu9GQlgheftDvTWg9pFSHMgyDlMw8jqZmsfVwMl+uPcDGg0mO9xuGBjDsijoMaluTIB+vMvoAFejUAXtP7JavwJZn39bweuj6KOz6GdZMs4dbT1+4+j/Q+SEtWCEiCrNFUZgVkTKRdvT0sINl9tdthtl/Ze7td8mH/isumS/WHGTepiNk5tp7Mf28PRjQuibDrqhN88jgSz5HuUs6BH+8Dpu+OBNiG/SE7k9DrfZn2iX+DT8/AQdW2l9Xawh9J0H9HhVfs4i4DIXZIijMikiZsVlPDzuYeHrYQVO47VP78rhlICUrl+83HuHzNQccc+ECtK0dwp2d69CnRQQ+Xi5241jyYft3svFzsOXat9XrAT2egaiOhe9jGLD1G/j1OUg/at/WbCD0ehmCa1ZI2SLiWhRmi6AwKyJlLnY5fHf3mWEHN7wFMbeX2eHzZ1j4fM0BFm1PIM9m/2e7ip8Xt3WIYmjHOtSuduk9wpckJQ7+eBM2fgrWHPu2uldB92egTufiHSMzyf4/Bus+sP/PgZc/dH8SOo3R0AORSkZhtggKsyJSLtKO2gNt7O/212U47OBsR1OzmL3uEF+tO0hcchYAJhNc3agGwzrVoUeTUDzMFXjDWEo8rHgLNswEa7Z9W3Q3+3CC6K6lO2bCNljwOBxaa39dvTH0e90ejkWkUlCYLYLCrIiUG5vVfsf+somAAaHN7LMd1GhU5qfKs9r4bdcxPl9zgOX/HHNsrxniyx2danNb+yhqBFrK/LwOqYmwcjL8+Qnk2UM1tbtAj6fLJnTabPabxhaPhYzj9m0tboHrX4KgiEs/voi4NIXZIijMiki52/e7vZc2/aj9V+U3vAUxg8vtdPuPpzNr3UG++fMQSRn2capeHib6tIhg2BV16BBdpeym90o7Zg+x6z+GvEz7tqhO9jGxda+2dxOXpcxT8L+X7OfDAO9Ae2DueA94uOHsDiJSLAqzRVCYFZEKkZoIc++2j6cFaHOn/S59L99yO2VWrpUFW+P5fM0BNh9KcmxvHBbIsCtqM7BNTQJLO71X+nFY+Tas/whyM+zbanWwDyeof03Zh9hzxW2CBU/AkT/tr0Ob24ce1OlSvucVEadQmC2CwqyIVBib1T7H6rJXsA87aA63ziyXYQfn2n4kmS/WHGD+5jjH9F7+3h4MbFOTYVfUoWlEMf/9yzgJq96BtR9Abrp9W2Rbe09sg57lH2LPZrPBps9hyXjIPGnfFjMErpsAAaEVV0dJ2Gxw/B/ISYPQpuDt7+yKRNyCwmwRFGZFpMLtW3Z62MEx+7CD/pOh1W0VcurkzFzmbjzMF2sOsPdYumN7+zpVuLNzHXq3CMfiWcj0XhknYfVUWPu+PYgBRLS2h9iG11dsiC2stqUvwIZPAQMsQXDNc9B+NHh4Oq8ugLwciN8CB1fBwTVwcLV9qAQAJqjWACJaQXhLCG9lfwTUcGrJIq5IYbYICrMi4hSpCfZAu/8P++u2I6DPq+U67OBshmGwet8JvlxzkEV/nZneq5q/NwPb1OSqRjXoEF0FP2sqrH7PvjJXTqp95/CW9im2Gvdxbog91+ENsOAxiN9sfx3eEvq+AbU7VVwN2alwaN2Z4Hr4zzNjifN5+YEl0D51W2ECI84Kty3tYTckGszmci9fxFUpzBZBYVZEnMZmhd9fhd9fwzHs4LZPoXrDCi3jaEoWX68/xKy1B0lIsc9EEEQ6d3stZLTnQvwNew+uEdocU4+nockNrhViz2az2qcFWzoBspLs21oPg+teAP/qZX++tKP20Hpgtf1nwlb7nLhn860KtTvb59et3RkiYuw3q6UdtbeP32qffixhK5zYCxTyn2HvwNMBt+WZntwaTTXfrlQaCrNFUJgVEafb+5t9KVzHsIO3odWtFXd+w4C8bPIyU1i9I5b0P2fT5djXBGEPsTttUUzOu5k/PDvRoW51utavTpcG1WgaHoS5IuewLYn047BknH35XACfYLh2LLQbBeZSrpJmGHByX8HwenLv+e1CatunJat9hf2GtOqNih/+s9Mg8S97sE04HXIT/z4zZ+/ZzF5Qo8k5wxRa2D+ryGVGYbYICrMi4hJKOuzAMCAn3f5r7Zy0gj+z0+xDArLP3l7UtlSw5Z13ipyqjVhb+1/MTmvLyn0nOXV6mq98Vf296VyvGl0aVKNr/erUqeZXdlN+lZVD6+xDDxK22V9HtIZ+b0Ktdhff12a173dwzZkxr+cNDTDZ5w/O73Wt3bnsl9y15tpvGkvYdroX9/QjK7nw9lWiT4fbmDM9uYERrtubLlIMCrNFUJgVEZdhs9pnOlg+CTDsK11Va3AmhBYIq2kU+uvoS+XlB9XqQ9dHofkgRy+mzWawIyGFVXtOsGrvcdbGniQjx1pg15ohvnSuX42up8NtaJBP2ddXGjarfV7a/70E2cmACdoOh57jwa/qmXa5mXBkw+le11VwaP2ZccL5PLztszfk97pGdQTfKhX5aewMA5IPFRyikLDNvq0wftXtwbZmW6jV0T6Nmn+1iq1Z5BIozBZBYVZEXM7e/8F3/zqz0lVRTGb7eEpLAHgH2H9aAk8/P+unJeBMuwu+H1DsX8HnWm1sOZTEyj0nWLn3OJsOniLXWvA/Hw1CA+havxpdGlTninrVCPZ18qIGaUdh8TjYMsv+2rcKXPlv+5CEg2vsc9faCvY+YwmyB9bane3hNbIteLlISC9MxskzwTY/6B7fdf44XoCq9ezBNqqD/WdoM+fP/iByAQqzRVCYFRGXlJoIO36wh1VL0DlhNejMcy8/l/j1cUZOHn/uP8XKvcdZtecE2+OSOfu/JmYTtKgZTJf61enaoBrt61TF17uUY1cLYRgGKVl5JGfkciojh6TMXJIyckjKyCXp9LbkTPvPyKRNjE59l/q2A+cfKCC84JCBsOalH2PrKnIz4ejf9inCDm+Aw+vswxbO5eV/uue2gz3A1+pQPjfNiZSCwmwRFGZFRMpeUkYOa/adcPTc7jtrTlsAbw8zbeuEOG4ma1UrBC8PM4ZhkJad5wihSZk5nMrIJTnD/jN/m/390z8zc0nOzMVqK/5/vjywMsLjV3p5rCfWFs56WxP+9mpGs2YxDGhTky71q+HpcRlPhZVx0j6k4tA6OLze/jw75fx2VevZQ21+wA1trt5bcQqF2SIozIqIlL+E5CxW7T3OytNjbuOTswq87+/tga+3B0kZuY45b0vD18uDKn5eBPt5E+LrRRV/L4J9vani50WInxchvt72n372bVm5Nn7eHs8Pm+M4knRmPtjqAd70axnBja1r0rZ2iOvd2FbWbFY4tsvea3tovT3gHt91fruze2/zA656b6UCKMwWQWFWRKRiGYZB7PF0Vu49wao9x1m97wRJ58yUYPE0U8UvP3jaQ2h+MA3x87IHVkdItW8L9vXCx6t0QwJsNoONB08xf3McC7bFczI9x/FeVFVfboyJZEDrmjQKC7ykz+5WMk+dGZZwaN2Fe2+r1D0zLEG9t1JOFGaLoDArIuJcNpvB7qNp2AzDEWBLG0rLQq7Vxoo9x/lhcxyL/kooMGtDk/BABrSuSf+YCGpV8XNajU5hs9l7aw+tO9ODW2jvrZ/9Rrn8G8tqddASvXLJFGaLoDArIiIXkpljZcmOROZvjuP3f44WmLGhQ3QVbmxdk34tI6jqX0lX4so8dXrs7Xp7wD284fT0Z+eo1sA+nVn+jXVV67nEjYviPhRmi6AwKyIixZGUkcMv2xOYv/kIa2NPOmZr8DSb6NawOje2juS6ZuEEWCrxr9jze28Prz9zc9mxnee38w89M1dv7SsgrKWGJkiRFGaLoDArIiIllZCcxU9b45i/OY5tR870RPp4menZNIwBrWtydaMaeHtexjMiFFfmKXuwzV8GOG4jWHMKtvEOsA9HqN3ZHm5rtQdvf+fUKy5JYbYICrMiInIp9h5L44fNcfywJY7Y42emIAv29aJvy3BujKlJp7pVMZv1a3UAcrPsC1QcXH36sfb8oQlmT4iIORNua3fWrAmVnMJsERRmRUSkLBiGwbYjyczfHMePW+I4mprteC88yIf+MREMaF2T5pFBl/9UXyVhs8GxHXBglX0ltoOrIeXI+e2qNSw4NKFKXY27rUQUZougMCsiImXNajNYu+8EP2yJ4+dt8aRk5Tneq1fDn6sa1iDI1wt/bw/8LZ74Wzzw8/YkwOKJn7eH/afF0/G+1+W8gENhkg6eCbYHVtvD7rkCws+6qewKCGuhcbeXMYXZIijMiohIecrOs/L7rmPM3xLHkr8Tyc6zlfgY3h5m/Cwe+HufH3zzw7C/tyd+p9+3b7OHYUdbiwfVAywE+Xi6X89wxskz424ProYjG8FWcG5ivAPt04Hlh9ua7cG7kk2fdhlTmC2CwqyIiFSU1KxcFv+dyK6EVNJz8sjItpKek0f66Z8Z2VbSsvPIyMkjPcdKTimC78X4e3sQEeJLRLAPkcG+RISc+RkRbN/u7+ozMuRm2sfd5g9NOLT2/AUdPH2gQU9o2h8a9QLfKs6pVcqEwmwRFGZFRMRV5VptZwVee8DNyM47HXjPCr7Z1jPv57fNtj93tM3KIzU77+InBYJ8PIk8HXgjQnyJDD4ddE8H3/BgH6cubHEemxWO7jjTc3tgNaTGnXnf7Al1r7IH28b9IDDMebVKqSjMFkFhVkREKovMHCtxyZkkJGcRl5RJfHIW8cmZxCXZf8YnZRU78Fbz93b05kaeDr0RwT6OEBwW5OO8sb6GAYl/wY4fYMePcPTvs9402YchNO1vf4TUdk6NUiIKs0VQmBURETkjNSuX+LPDblImcclnwm5cciZZuRcf/mAyQWighbAgey+uxdOMt4cZb8/Tj7Of578ubJun2b6vpxlvD48i3juzn6fZVHBc8PE9sPNHe7A9sqFgoRGtTwfbG6FGo7L9MqXMKMwWQWFWRESk+AzDICkj90wP7+nAe3YATkjOIsda9uN9i8tkAn9vT6r4e1HV30I1f2+q+ntTLcCb2h4naZayguijSwk5th6TcVad1Ruf6bGNiNHUXy5EYbYICrMiIiJly2YzOJGeQ3xyJkdTssnOs5Fjtd/QlpNnO/3a5nidc87r7ELeyz37/XP3t9qw2koeX6qRTE+PjfQ2r6OreTveJqvjvRNe4eyu2oO4yJ7kRrSnaoCvPRD7e1M1wJtAixvOCuHGFGaLoDArIiLi/qw246wwbL/h7WR6DifSczh5+nEiLYeT6dkFt6XnkJNnI5AMepg30dtjPd3NW/AznVn04qgRwq/Wdiy0dWSNrSl5eOLtYaaKvxfV/C1UC7D3/OaH3Sr5odff4tgW7OulVeAugcJsERRmRUREKi/DMEjPsXIyLYcT6dmcTM8hKSUZv4PLqZWwmAanVuBrS3O0TzL8WWpry0JrB5bbWpGNd7HOYzZBFb+zQm+AN1X8vB1DIKoG2IdDVPE78563ZyVbLKMICrNFUJgVERGRC8rLgf3L7TeP7VwA6cccb1k9/TgRcTWxNa5hR8AVJGR7c/J0ID675zc1q3gzRJwr0MfznJ5eb8cYYMc2Py+qB3oTEex3Wff8KswWQWFWREREisVmtS/QsOP0zAjJh8685+EN9bpDSB2w5dkfhg1seVjzcsnNzSU3N8f+My+XvNxcrNY8bHm52Ky52Kx5GFb7fobNigc2PLHiYTr9Eyue2PAg/z37c0+T/Qa2PMNMrtkbw+wNnhbMXr54WXzw8PIBT4t9EQkPb/tPT+9zXlvOPDwsF2iT//OcbUE1wcOr3L96hdkiKMyKiIhIiRmGfRWyHT/a57M9scfZFTnHgxugeoNyP01J8pqLr18nIiIi4gJMJqjZ1v64diwc2wW7F0F2mn3FMbPH6Z+eZ70ubNtZr00Xa+NR6H65NoNDx5LYl3CKA4knOHz0FEdOJJOSloY3uVjIxZs8LOTgbcrD35xHZICJiAAzEX4mavhBNYuBrzkP8rIhLwusOfafead/WrNPv5ddsI2nxdl/EudRmBUREREpCZMJQpvYH07gBdQLrEG9egW3J2fmsishlV0JKexISGVnfAq7ElJJz7HCKeyPs1Tz96ZJRCBNwoNoEm7/2TAswLWWLi4Glxhm8O677zJp0iQSEhKIiYlhypQpdOzYsdC2H374IZ999hnbt28HoF27drz88ssXbH8uDTMQERGRysJmMziSlMmO+BR2JqSyMyGFnfGpxJ5Ip7AEaDZB3er+NIkIoml4II1PB91aVXwrdJ5dtxozO3v2bIYPH8706dPp1KkTkydPZs6cOezatYvQ0NDz2g8dOpSuXbvSpUsXfHx8ePXVV/n+++/566+/qFmz5kXPpzArIiIilV1mjpXdR1PZGZ/KjtMBd2dCCqcycgttH2jxpHF4IBNvaknDsMByr8+twmynTp3o0KEDU6dOBcBmsxEVFcVDDz3EU089ddH9rVYrVapUYerUqQwfPvyi7RVmRURERM5nGAbHUrMLDFHYkZDKnqOp5FrtcXHtM9cSFuRT7rW4zQ1gOTk5bNiwgaefftqxzWw207NnT1avXl2sY2RkZJCbm0vVqlULfT87O5vs7DOreqSkpFxa0SIiIiKXIZPJRGiQD6FBPlzdqIZje67Vxr5j6fyTmEpooOvdAObUpSaOHz+O1WolLCyswPawsDASEhKKdYwnn3ySyMhIevbsWej7EydOJDg42PGIioq65LpFREREKgsvDzONwwPpHxNZoeNmi8ut10175ZVX+Prrr/n+++/x8Sm8y/vpp58mOTnZ8Th06FCh7URERETE/Th1mEH16tXx8PAgMTGxwPbExETCw8OL3Pf111/nlVdeYcmSJbRq1eqC7SwWCxaL63WJi4iIiMilc2rPrLe3N+3atWPp0qWObTabjaVLl9K5c+cL7vfaa6/x4osvsnDhQtq3b18RpYqIiIiIC3L6ogmPPfYYI0aMoH379nTs2JHJkyeTnp7OqFGjABg+fDg1a9Zk4sSJALz66quMHTuWWbNmER0d7RhbGxAQQEBAgNM+h4iIiIhUPKeH2cGDB3Ps2DHGjh1LQkICrVu3ZuHChY6bwg4ePIjZfKYDedq0aeTk5HDLLbcUOM64ceMYP358RZYuIiIiIk7m9HlmK5rmmRURERFxbSXJa249m4GIiIiIVG4KsyIiIiLithRmRURERMRtKcyKiIiIiNtSmBURERERt6UwKyIiIiJuS2FWRERERNyWwqyIiIiIuC2nrwBW0fLXiEhJSXFyJSIiIiJSmPycVpy1vSpdmE1NTQUgKirKyZWIiIiISFFSU1MJDg4usk2lW87WZrMRFxdHYGAgJpOp3M+XkpJCVFQUhw4d0vK5F6Hvqnj0PRWPvqfi0fdUPPqeikffU/HpuyqaYRikpqYSGRmJ2Vz0qNhK1zNrNpupVatWhZ83KChIF2sx6bsqHn1PxaPvqXj0PRWPvqfi0fdUfPquLuxiPbL5dAOYiIiIiLgthVkRERERcVsKs+XMYrEwbtw4LBaLs0txefquikffU/HoeyoefU/Fo++pePQ9FZ++q7JT6W4AExEREZHLh3pmRURERMRtKcyKiIiIiNtSmBURERERt6UwKyIiIiJuS2G2DLz77rtER0fj4+NDp06dWLduXZHt58yZQ5MmTfDx8aFly5b8/PPPFVSp80ycOJEOHToQGBhIaGgoAwcOZNeuXUXuM3PmTEwmU4GHj49PBVXsHOPHjz/vMzdp0qTIfSrj9RQdHX3e92QymXjggQcKbV9ZrqXly5fTv39/IiMjMZlMzJs3r8D7hmEwduxYIiIi8PX1pWfPnuzevfuixy3pv3GurqjvKTc3lyeffJKWLVvi7+9PZGQkw4cPJy4urshjlubvrju42DU1cuTI8z537969L3rcynRNAYX+e2UymZg0adIFj3m5XlPlQWH2Es2ePZvHHnuMcePGsXHjRmJiYujVqxdHjx4ttP2qVasYMmQIo0ePZtOmTQwcOJCBAweyffv2Cq68Yv3+++888MADrFmzhsWLF5Obm8v1119Penp6kfsFBQURHx/veBw4cKCCKnae5s2bF/jMK1asuGDbyno9rV+/vsB3tHjxYgBuvfXWC+5TGa6l9PR0YmJiePfddwt9/7XXXuOdd95h+vTprF27Fn9/f3r16kVWVtYFj1nSf+PcQVHfU0ZGBhs3buT5559n48aNzJ07l127dnHjjTde9Lgl+bvrLi52TQH07t27wOf+6quvijxmZbumgALfT3x8PJ988gkmk4mbb765yONejtdUuTDkknTs2NF44IEHHK+tVqsRGRlpTJw4sdD2t912m9GvX78C2zp16mTce++95Vqnqzl69KgBGL///vsF28yYMcMIDg6uuKJcwLhx44yYmJhit9f1ZPfII48Y9evXN2w2W6HvV8ZrCTC+//57x2ubzWaEh4cbkyZNcmxLSkoyLBaL8dVXX13wOCX9N87dnPs9FWbdunUGYBw4cOCCbUr6d9cdFfZdjRgxwhgwYECJjqNryjAGDBhgXHPNNUW2qQzXVFlRz+wlyMnJYcOGDfTs2dOxzWw207NnT1avXl3oPqtXry7QHqBXr14XbH+5Sk5OBqBq1apFtktLS6NOnTpERUUxYMAA/vrrr4ooz6l2795NZGQk9erVY+jQoRw8ePCCbXU92f8efvHFF9x1112YTKYLtquM19LZYmNjSUhIKHC9BAcH06lTpwteL6X5N+5ylJycjMlkIiQkpMh2Jfm7ezlZtmwZoaGhNG7cmDFjxnDixIkLttU1BYmJiSxYsIDRo0dftG1lvaZKSmH2Ehw/fhyr1UpYWFiB7WFhYSQkJBS6T0JCQonaX45sNhuPPvooXbt2pUWLFhds17hxYz755BPmz5/PF198gc1mo0uXLhw+fLgCq61YnTp1YubMmSxcuJBp06YRGxtLt27dSE1NLbS9rieYN28eSUlJjBw58oJtKuO1dK78a6Ik10tp/o273GRlZfHkk08yZMgQgoKCLtiupH93Lxe9e/fms88+Y+nSpbz66qv8/vvv9OnTB6vVWmh7XVPw6aefEhgYyE033VRku8p6TZWGp7MLkMrngQceYPv27Rcd+9O5c2c6d+7seN2lSxeaNm3K+++/z4svvljeZTpFnz59HM9btWpFp06dqFOnDt98802x/i++Mvr444/p06cPkZGRF2xTGa8luXS5ubncdtttGIbBtGnTimxbWf/u3n777Y7nLVu2pFWrVtSvX59ly5Zx7bXXOrEy1/XJJ58wdOjQi96EWlmvqdJQz+wlqF69Oh4eHiQmJhbYnpiYSHh4eKH7hIeHl6j95ebBBx/kp59+4rfffqNWrVol2tfLy4s2bdqwZ8+ecqrO9YSEhNCoUaMLfubKfj0dOHCAJUuWcPfdd5dov8p4LeVfEyW5Xkrzb9zlIj/IHjhwgMWLFxfZK1uYi/3dvVzVq1eP6tWrX/BzV+ZrCuCPP/5g165dJf43CyrvNVUcCrOXwNvbm3bt2rF06VLHNpvNxtKlSwv0Ap2tc+fOBdoDLF68+ILtLxeGYfDggw/y/fff87///Y+6deuW+BhWq5Vt27YRERFRDhW6prS0NPbu3XvBz1xZr6d8M2bMIDQ0lH79+pVov8p4LdWtW5fw8PAC10tKSgpr16694PVSmn/jLgf5QXb37t0sWbKEatWqlfgYF/u7e7k6fPgwJ06cuODnrqzXVL6PP/6Ydu3aERMTU+J9K+s1VSzOvgPN3X399deGxWIxZs6cafz999/GPffcY4SEhBgJCQmGYRjGnXfeaTz11FOO9itXrjQ8PT2N119/3dixY4cxbtw4w8vLy9i2bZuzPkKFGDNmjBEcHGwsW7bMiI+PdzwyMjIcbc79rl544QVj0aJFxt69e40NGzYYt99+u+Hj42P89ddfzvgIFeLxxx83li1bZsTGxhorV640evbsaVSvXt04evSoYRi6ns5mtVqN2rVrG08++eR571XWayk1NdXYtGmTsWnTJgMw3nzzTWPTpk2Ou/BfeeUVIyQkxJg/f76xdetWY8CAAUbdunWNzMxMxzGuueYaY8qUKY7XF/s3zh0V9T3l5OQYN954o1GrVi1j8+bNBf69ys7Odhzj3O/pYn933VVR31VqaqrxxBNPGKtXrzZiY2ONJUuWGG3btjUaNmxoZGVlOY5R2a+pfMnJyYafn58xbdq0Qo9RWa6p8qAwWwamTJli1K5d2/D29jY6duxorFmzxvHe1VdfbYwYMaJA+2+++cZo1KiR4e3tbTRv3txYsGBBBVdc8YBCHzNmzHC0Ofe7evTRRx3fa1hYmNG3b19j48aNFV98BRo8eLARERFheHt7GzVr1jQGDx5s7Nmzx/G+rqczFi1aZADGrl27znuvsl5Lv/32W6F/z/K/C5vNZjz//PNGWFiYYbFYjGuvvfa8769OnTrGuHHjCmwr6t84d1TU9xQbG3vBf69+++03xzHO/Z4u9nfXXRX1XWVkZBjXX3+9UaNGDcPLy8uoU6eO8a9//eu8UFrZr6l877//vuHr62skJSUVeozKck2VB5NhGEa5dv2KiIiIiJQTjZkVEREREbelMCsiIiIibkthVkRERETclsKsiIiIiLgthVkRERERcVsKsyIiIiLithRmRURERMRtKcyKiIiIiNtSmBURqaRMJhPz5s1zdhkiIpdEYVZExAlGjhyJyWQ679G7d29nlyYi4lY8nV2AiEhl1bt3b2bMmFFgm8VicVI1IiLuST2zIiJOYrFYCA8PL/CoUqUKYB8CMG3aNPr06YOvry/16tXj22+/LbD/tm3buOaaa/D19aVatWrcc889pKWlFWjzySef0Lx5cywWCxERETz44IMF3j9+/DiDBg3Cz8+Phg0b8sMPP5TvhxYRKWMKsyIiLur555/n5ptvZsuWLQwdOpTbb7+dHTt2AJCenk6vXr2oUqUK69evZ86cOSxZsqRAWJ02bRoPPPAA99xzD9u2beOHH36gQYMGBc7xwgsvcNttt7F161b69u3L0KFDOXnyZIV+ThGRS2EyDMNwdhEiIpXNyJEj+eKLL/Dx8Smw/ZlnnuGZZ57BZDJx3333MW3aNMd7V1xxBW3btuW9997jww8/5Mknn+TQoUP4+/sD8PPPP9O/f3/i4uIICwujZs2ajBo1ipdeeqnQGkwmE8899xwvvvgiYA/IAQEB/PLLLxq7KyJuQ2NmRUScpEePHgXCKkDVqlUdzzt37lzgvc6dO7N582YAduzYQUxMjCPIAnTt2hWbzcauXbswmUzExcVx7bXXFllDq1atHM/9/f0JCgri6NGjpf1IIiIVTmFWRMRJ/P39z/u1f1nx9fUtVjsvL68Cr00mEzabrTxKEhEpFxozKyLiotasWXPe66ZNmwLQtGlTtmzZQnp6uuP9lStXYjabady4MYGBgURHR7N06dIKrVlEpKKpZ1ZExEmys7NJSEgosM3T05Pq1asDMGfOHNq3b8+VV17Jl19+ybp16/j4448BGDp0KOPGjWPEiBGMHz+eY8eO8dBDD3HnnXcSFhYGwPjx47nvvvsIDQ2lT58+pKamsnLlSh566KGK/aAiIuVIYVZExEkWLlxIREREgW2NGzdm586dgH2mga+//pr777+fiIgIvvrqK5o1awaAn58fixYt4pFHHqFDhw74+flx88038+abbzqONWLECLKysnjrrbd44oknqF69OrfcckvFfUARkQqg2QxERFyQyWTi+++/Z+DAgc4uRUTEpWnMrIiIiIi4LYVZEREREXFbGjMrIuKCNAJMRKR41DMrIiIiIm5LYVZERERE3JbCrIiIiIi4LYVZEREREXFbCrMiIiIi4rYUZkVERETEbSnMioiIiIjbUpgVEREREbf1//Kt3Z4nDVYcAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,5))\n",
    "\n",
    "plt.plot(\n",
    "    history2.history['loss'],\n",
    "    label='Training'\n",
    ")\n",
    "\n",
    "plt.plot(\n",
    "    history2.history['val_loss'],\n",
    "    label='Validation'\n",
    ")\n",
    "\n",
    "plt.title(\n",
    "    'Training and Validation Loss'\n",
    ")\n",
    "\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Loss')\n",
    "\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b730d77",
   "metadata": {},
   "source": [
    "### Step 14: Classification Report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "cacfd742",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m74/74\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m27s\u001b[0m 366ms/step\n",
      "                    precision    recall  f1-score   support\n",
      "\n",
      "frog_eye_leaf_spot       0.88      0.97      0.92       478\n",
      "           healthy       0.88      0.89      0.88       694\n",
      "    powdery_mildew       0.93      0.77      0.84       178\n",
      "              rust       0.98      0.89      0.94       279\n",
      "              scab       0.85      0.85      0.85       724\n",
      "\n",
      "          accuracy                           0.88      2353\n",
      "         macro avg       0.90      0.87      0.89      2353\n",
      "      weighted avg       0.89      0.88      0.88      2353\n",
      "\n"
     ]
    }
   ],
   "source": [
    "predictions = model.predict(\n",
    "    test_generator\n",
    ")\n",
    "\n",
    "y_pred = np.argmax(\n",
    "    predictions,\n",
    "    axis=1\n",
    ")\n",
    "\n",
    "print(\n",
    "    classification_report(\n",
    "        test_generator.classes,\n",
    "        y_pred,\n",
    "        target_names=list(\n",
    "            test_generator.class_indices.keys()\n",
    "        )\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "9a636cab",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m74/74\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m29s\u001b[0m 388ms/step\n",
      "                    precision    recall  f1-score   support\n",
      "\n",
      "frog_eye_leaf_spot       0.98      0.96      0.97       478\n",
      "           healthy       0.91      0.96      0.94       694\n",
      "    powdery_mildew       0.90      0.81      0.85       178\n",
      "              rust       0.92      0.97      0.94       279\n",
      "              scab       0.94      0.91      0.92       724\n",
      "\n",
      "          accuracy                           0.93      2353\n",
      "         macro avg       0.93      0.92      0.93      2353\n",
      "      weighted avg       0.93      0.93      0.93      2353\n",
      "\n"
     ]
    }
   ],
   "source": [
    "predictions = model2.predict(\n",
    "    test_generator\n",
    ")\n",
    "\n",
    "y_pred = np.argmax(\n",
    "    predictions,\n",
    "    axis=1\n",
    ")\n",
    "\n",
    "print(\n",
    "    classification_report(\n",
    "        test_generator.classes,\n",
    "        y_pred,\n",
    "        target_names=list(\n",
    "            test_generator.class_indices.keys()\n",
    "        )\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "c36cfea0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = confusion_matrix(\n",
    "    test_generator.classes,\n",
    "    y_pred\n",
    ")\n",
    "\n",
    "plt.figure(figsize=(8,6))\n",
    "\n",
    "sns.heatmap(\n",
    "    cm,\n",
    "    annot=True,\n",
    "    fmt='d',\n",
    "    cmap='Blues',\n",
    "    xticklabels=test_generator.class_indices.keys(),\n",
    "    yticklabels=test_generator.class_indices.keys()\n",
    ")\n",
    "\n",
    "plt.xlabel(\"Predicted\")\n",
    "plt.ylabel(\"Actual\")\n",
    "plt.title(\"Confusion Matrix\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "bbd31be4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = confusion_matrix(\n",
    "    test_generator.classes,\n",
    "    y_pred\n",
    ")\n",
    "\n",
    "plt.figure(figsize=(8,6))\n",
    "\n",
    "sns.heatmap(\n",
    "    cm,\n",
    "    annot=True,\n",
    "    fmt='d',\n",
    "    cmap='Blues',\n",
    "    xticklabels=test_generator.class_indices.keys(),\n",
    "    yticklabels=test_generator.class_indices.keys()\n",
    ")\n",
    "\n",
    "plt.xlabel(\"Predicted\")\n",
    "plt.ylabel(\"Actual\")\n",
    "plt.title(\"Confusion Matrix\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "64a69b45",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = confusion_matrix(\n",
    "    test_generator.classes,\n",
    "    y_pred\n",
    ")\n",
    "\n",
    "plt.figure(figsize=(8,6))\n",
    "\n",
    "sns.heatmap(\n",
    "    cm,\n",
    "    annot=True,\n",
    "    fmt='d',\n",
    "    cmap='Blues',\n",
    "    xticklabels=test_generator.class_indices.keys(),\n",
    "    yticklabels=test_generator.class_indices.keys()\n",
    ")\n",
    "\n",
    "plt.xlabel(\"Predicted\")\n",
    "plt.ylabel(\"Actual\")\n",
    "plt.title(\"Confusion Matrix\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "82b7b268",
   "metadata": {},
   "outputs": [],
   "source": [
    "model2.save(\"2plant_disease_cnn.keras\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "a29c1842",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save(\"1plant_disease_cnn.keras\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff66d2f0",
   "metadata": {},
   "source": [
    "### Step 15: Confusion Matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37920340",
   "metadata": {},
   "source": [
    "## Model 2 – Deeper CNN"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db8abf7c",
   "metadata": {},
   "source": [
    "## Model 3 – Lightweight CNN with GlobalAveragePooling"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "07194fc2",
   "metadata": {},
   "source": [
    "## Model 4 – CNN with Batch Normalization"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6dadd1c",
   "metadata": {},
   "source": [
    "## Model 5 – Transfer Learning "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.13.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}