Terrain_V4.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing necessary Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Install necessary packages and import libraries\n",
    "# The first few lines ensure that TensorFlow, Scipy, and Matplotlib are installed.\n",
    "# pip install tensorflow\n",
    "# pip install scipy\n",
    "# pip install matplotlib\n",
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Inline plotting with Matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# %matplotlib inline\n",
    "# this  command ensures that plots are displayed directly in the notebook after being generated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Building a Convolutional Neural Network (CNN) model for terrain classification\n",
    "# Importing layers from Keras for building the model architecture\n",
    "from keras.preprocessing import image\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Conv2D\n",
    "from keras.layers import MaxPooling2D\n",
    "from keras.layers import Flatten\n",
    "from keras.layers import Dense\n",
    "from keras.layers import Dropout\n",
    "from keras.layers import BatchNormalization\n",
    "from keras.models import load_model\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "from tensorflow.keras.callbacks import LearningRateScheduler, ReduceLROnPlateau\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Defining the CNN model\n",
    "The following code defines a CNN model using TensorFlow's Keras API. The model is designed for terrain classification with a series of convolutional layers, pooling layers, and fully connected layers.\n",
    "\n",
    "* **Convolutional Layers:** These layers apply filters to the input image, helping to detect various features (like edges, textures, etc.).\n",
    "* **Max Pooling:** This operation reduces the spatial dimensions (width and height) of the output from the convolutional layers, which helps in reducing computational complexity and controlling overfitting.\n",
    "* **Batch Normalization:** This technique normalizes the outputs of a previous activation layer, which helps stabilize learning and can accelerate training.\n",
    "* **Dropout Layers:** These layers randomly set a fraction of the input units to zero during training to help prevent overfitting.\n",
    "* **Softmax Layer:** This is used in the output layer for multi-class classification problems, providing probabilities for each class.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the CNN model architecture with residual connections\n",
    "def create_model():\n",
    "    model = tf.keras.Sequential([\n",
    "        tf.keras.layers.Input(shape=(64, 64, 3)),\n",
    "        tf.keras.layers.Conv2D(32, kernel_size=3, activation='relu'),\n",
    "        tf.keras.layers.MaxPooling2D(pool_size=2, strides=2),\n",
    "        tf.keras.layers.BatchNormalization(),\n",
    "\n",
    "        tf.keras.layers.Conv2D(64, kernel_size=3, activation='relu'),\n",
    "        tf.keras.layers.MaxPooling2D(pool_size=2, strides=2),\n",
    "        tf.keras.layers.BatchNormalization(),\n",
    "\n",
    "        tf.keras.layers.Conv2D(128, kernel_size=3, activation='relu'),\n",
    "        tf.keras.layers.MaxPooling2D(pool_size=2, strides=2),\n",
    "        tf.keras.layers.BatchNormalization(),\n",
    "\n",
    "        tf.keras.layers.Flatten(),\n",
    "\n",
    "        tf.keras.layers.Dense(1024, activation='relu'),\n",
    "        tf.keras.layers.Dropout(0.2),\n",
    "        tf.keras.layers.BatchNormalization(),\n",
    "\n",
    "        tf.keras.layers.Dense(1024, activation='relu'),\n",
    "        tf.keras.layers.Dropout(0.2),\n",
    "        tf.keras.layers.BatchNormalization(),\n",
    "\n",
    "        tf.keras.layers.Dense(256, activation='relu'),\n",
    "        tf.keras.layers.Dropout(0.2),\n",
    "        tf.keras.layers.BatchNormalization(),\n",
    "\n",
    "        tf.keras.layers.Dense(5, activation='softmax')\n",
    "    ])\n",
    "    return model\n",
    "\n",
    "model = create_model()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compiling the CNN model\n",
    "The model is compiled using the Adam optimizer, with categorical crossentropy as the loss function, and accuracy as the evaluation metric."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compile the model\n",
    "# Categorical crossentropy is used since the labels are one-hot encoded.\n",
    "# Adam optimizer is used for its adaptive learning rate.\n",
    "\n",
    "model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Augmentation and Preprocessing\n",
    "This section applies data augmentation techniques using the `ImageDataGenerator` from Keras to generate more training samples and preprocess the images. These operations include rescaling, rotating, shifting, and flipping.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data augmentation and preprocessing for training and validation datasets\n",
    "# The ImageDataGenerator applies various transformations to augment the training data\n",
    "# Data augmentation for training dataset\n",
    "train_datagen = ImageDataGenerator(\n",
    "    rescale=1./255,\n",
    "    shear_range=0.2,\n",
    "    zoom_range=0.2,\n",
    "    horizontal_flip=True,\n",
    "    rotation_range=30,\n",
    "    brightness_range=[0.8, 1.2],\n",
    "    width_shift_range=0.2,\n",
    "    height_shift_range=0.2\n",
    ")\n",
    "\n",
    "# Validation dataset (only rescaling)\n",
    "test_datagen = ImageDataGenerator(rescale=1./255)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading and Preparing the Data\n",
    "In this section, the training and validation data are loaded from the respective directories, and the images are prepared for the model using the augmentation techniques defined earlier.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 10000 images belonging to 5 classes.\n",
      "Found 500 images belonging to 5 classes.\n"
     ]
    }
   ],
   "source": [
    "# Assuming you've created 'train_subset' and 'test_subset' directories\n",
    "training_set = train_datagen.flow_from_directory(\n",
    "    r\"D:\\Jupyter Notebook\\Contribution\\Terrain-Recognition\\Data Main\\Training Data\",\n",
    "    target_size=(64, 64),\n",
    "    batch_size=64,\n",
    "    class_mode='categorical'\n",
    ")\n",
    "\n",
    "test_set = test_datagen.flow_from_directory(\n",
    "    r\"D:\\Jupyter Notebook\\Contribution\\Terrain-Recognition\\Data Main\\Testing Data\",\n",
    "    target_size=(64, 64),\n",
    "    batch_size=64,\n",
    "    class_mode='categorical'\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define learning rate scheduler\n",
    "def scheduler(epoch, lr):\n",
    "    if epoch < 10:\n",
    "        return 1e-2\n",
    "    else:\n",
    "        return 1e-3\n",
    "\n",
    "lr_schedule = LearningRateScheduler(scheduler)\n",
    "lr_reduction = ReduceLROnPlateau(monitor='val_loss', factor=0.5, patience=3, min_lr=1e-6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
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       "<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_2\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"sequential_2\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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     "data": {
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       "<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_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\">62</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">62</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_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\">31</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">31</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ batch_normalization_12               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">31</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">31</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)          │             <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)                 │                             │                 │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ conv2d_7 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">29</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">29</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_7 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ batch_normalization_13               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)          │             <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)                 │                             │                 │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ conv2d_8 (<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\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</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_8 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)           │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ batch_normalization_14               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)           │             <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)                 │                             │                 │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ flatten_2 (<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\">4608</span>)                │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dense_8 (<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\">1024</span>)                │       <span style=\"color: #00af00; text-decoration-color: #00af00\">4,719,616</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dropout_6 (<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\">1024</span>)                │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ batch_normalization_15               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1024</span>)                │           <span style=\"color: #00af00; text-decoration-color: #00af00\">4,096</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)                 │                             │                 │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dense_9 (<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\">1024</span>)                │       <span style=\"color: #00af00; text-decoration-color: #00af00\">1,049,600</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dropout_7 (<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\">1024</span>)                │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ batch_normalization_16               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1024</span>)                │           <span style=\"color: #00af00; text-decoration-color: #00af00\">4,096</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)                 │                             │                 │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dense_10 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)                 │         <span style=\"color: #00af00; text-decoration-color: #00af00\">262,400</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dropout_8 (<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",
       "│ batch_normalization_17               │ (<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\">1,024</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)                 │                             │                 │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dense_11 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">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_6 (\u001b[38;5;33mConv2D\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m62\u001b[0m, \u001b[38;5;34m62\u001b[0m, \u001b[38;5;34m32\u001b[0m)          │             \u001b[38;5;34m896\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ max_pooling2d_6 (\u001b[38;5;33mMaxPooling2D\u001b[0m)       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m31\u001b[0m, \u001b[38;5;34m31\u001b[0m, \u001b[38;5;34m32\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ batch_normalization_12               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m31\u001b[0m, \u001b[38;5;34m31\u001b[0m, \u001b[38;5;34m32\u001b[0m)          │             \u001b[38;5;34m128\u001b[0m │\n",
       "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)                 │                             │                 │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ conv2d_7 (\u001b[38;5;33mConv2D\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m29\u001b[0m, \u001b[38;5;34m29\u001b[0m, \u001b[38;5;34m64\u001b[0m)          │          \u001b[38;5;34m18,496\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ max_pooling2d_7 (\u001b[38;5;33mMaxPooling2D\u001b[0m)       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m64\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ batch_normalization_13               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m64\u001b[0m)          │             \u001b[38;5;34m256\u001b[0m │\n",
       "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)                 │                             │                 │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ conv2d_8 (\u001b[38;5;33mConv2D\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m128\u001b[0m)         │          \u001b[38;5;34m73,856\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ max_pooling2d_8 (\u001b[38;5;33mMaxPooling2D\u001b[0m)       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m128\u001b[0m)           │               \u001b[38;5;34m0\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ batch_normalization_14               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m128\u001b[0m)           │             \u001b[38;5;34m512\u001b[0m │\n",
       "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)                 │                             │                 │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ flatten_2 (\u001b[38;5;33mFlatten\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4608\u001b[0m)                │               \u001b[38;5;34m0\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dense_8 (\u001b[38;5;33mDense\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1024\u001b[0m)                │       \u001b[38;5;34m4,719,616\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dropout_6 (\u001b[38;5;33mDropout\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1024\u001b[0m)                │               \u001b[38;5;34m0\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ batch_normalization_15               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1024\u001b[0m)                │           \u001b[38;5;34m4,096\u001b[0m │\n",
       "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)                 │                             │                 │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dense_9 (\u001b[38;5;33mDense\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1024\u001b[0m)                │       \u001b[38;5;34m1,049,600\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dropout_7 (\u001b[38;5;33mDropout\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1024\u001b[0m)                │               \u001b[38;5;34m0\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ batch_normalization_16               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1024\u001b[0m)                │           \u001b[38;5;34m4,096\u001b[0m │\n",
       "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)                 │                             │                 │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dense_10 (\u001b[38;5;33mDense\u001b[0m)                     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m)                 │         \u001b[38;5;34m262,400\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dropout_8 (\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",
       "│ batch_normalization_17               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m)                 │           \u001b[38;5;34m1,024\u001b[0m │\n",
       "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)                 │                             │                 │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dense_11 (\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\">6,136,261</span> (23.41 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m6,136,261\u001b[0m (23.41 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\">6,131,205</span> (23.39 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m6,131,205\u001b[0m (23.39 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\">5,056</span> (19.75 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m5,056\u001b[0m (19.75 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training the CNN Model\n",
    "The model is trained using the training and validation data. The training process is monitored, and a learning rate scheduler is used to adjust the learning rate dynamically during training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "\u001b[1m256/256\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m44s\u001b[0m 166ms/step - accuracy: 0.6662 - loss: 0.8773 - val_accuracy: 0.5670 - val_loss: 1.1633 - learning_rate: 0.0100\n",
      "Epoch 2/20\n",
      "\u001b[1m256/256\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m42s\u001b[0m 162ms/step - accuracy: 0.6876 - loss: 0.8213 - val_accuracy: 0.6731 - val_loss: 0.7837 - learning_rate: 0.0100\n",
      "Epoch 3/20\n",
      "\u001b[1m256/256\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m67s\u001b[0m 259ms/step - accuracy: 0.6984 - loss: 0.7931 - val_accuracy: 0.5692 - val_loss: 1.1659 - learning_rate: 0.0100\n",
      "Epoch 4/20\n",
      "\u001b[1m256/256\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m104s\u001b[0m 397ms/step - accuracy: 0.7149 - loss: 0.7656 - val_accuracy: 0.4808 - val_loss: 1.5558 - learning_rate: 0.0100\n",
      "Epoch 5/20\n",
      "\u001b[1m256/256\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m106s\u001b[0m 396ms/step - accuracy: 0.7121 - loss: 0.7686 - val_accuracy: 0.7411 - val_loss: 0.6265 - learning_rate: 0.0100\n",
      "Epoch 6/20\n",
      "\u001b[1m256/256\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m103s\u001b[0m 387ms/step - accuracy: 0.7327 - loss: 0.7244 - val_accuracy: 0.7115 - val_loss: 0.7391 - learning_rate: 0.0100\n",
      "Epoch 7/20\n",
      "\u001b[1m256/256\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m105s\u001b[0m 396ms/step - accuracy: 0.7207 - loss: 0.7397 - val_accuracy: 0.7277 - val_loss: 0.7334 - learning_rate: 0.0100\n",
      "Epoch 8/20\n",
      "\u001b[1m256/256\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m103s\u001b[0m 385ms/step - accuracy: 0.7278 - loss: 0.7360 - val_accuracy: 0.4808 - val_loss: 1.3144 - learning_rate: 0.0100\n",
      "Epoch 9/20\n",
      "\u001b[1m256/256\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m106s\u001b[0m 396ms/step - accuracy: 0.7188 - loss: 0.7447 - val_accuracy: 0.7790 - val_loss: 0.6228 - learning_rate: 0.0100\n",
      "Epoch 10/20\n",
      "\u001b[1m256/256\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m105s\u001b[0m 390ms/step - accuracy: 0.7334 - loss: 0.7203 - val_accuracy: 0.8462 - val_loss: 0.5549 - learning_rate: 0.0100\n",
      "Epoch 11/20\n",
      "\u001b[1m256/256\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m106s\u001b[0m 398ms/step - accuracy: 0.7467 - loss: 0.6770 - val_accuracy: 0.8304 - val_loss: 0.5045 - learning_rate: 0.0010\n",
      "Epoch 12/20\n",
      "\u001b[1m256/256\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m104s\u001b[0m 389ms/step - accuracy: 0.7643 - loss: 0.6322 - val_accuracy: 0.7885 - val_loss: 0.6222 - learning_rate: 0.0010\n",
      "Epoch 13/20\n",
      "\u001b[1m256/256\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m108s\u001b[0m 406ms/step - accuracy: 0.7657 - loss: 0.6330 - val_accuracy: 0.8371 - val_loss: 0.4806 - learning_rate: 0.0010\n",
      "Epoch 14/20\n",
      "\u001b[1m256/256\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m104s\u001b[0m 391ms/step - accuracy: 0.7717 - loss: 0.6135 - val_accuracy: 0.8077 - val_loss: 0.5787 - learning_rate: 0.0010\n",
      "Epoch 15/20\n",
      "\u001b[1m256/256\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m106s\u001b[0m 397ms/step - accuracy: 0.7790 - loss: 0.5961 - val_accuracy: 0.8304 - val_loss: 0.4824 - learning_rate: 0.0010\n",
      "Epoch 16/20\n",
      "\u001b[1m256/256\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m103s\u001b[0m 385ms/step - accuracy: 0.7815 - loss: 0.5944 - val_accuracy: 0.7885 - val_loss: 0.5299 - learning_rate: 0.0010\n",
      "Epoch 17/20\n",
      "\u001b[1m256/256\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m105s\u001b[0m 395ms/step - accuracy: 0.7859 - loss: 0.5852 - val_accuracy: 0.8482 - val_loss: 0.4389 - learning_rate: 0.0010\n",
      "Epoch 18/20\n",
      "\u001b[1m256/256\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m103s\u001b[0m 387ms/step - accuracy: 0.7806 - loss: 0.6004 - val_accuracy: 0.6346 - val_loss: 0.7584 - learning_rate: 0.0010\n",
      "Epoch 19/20\n",
      "\u001b[1m256/256\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m108s\u001b[0m 405ms/step - accuracy: 0.7790 - loss: 0.5870 - val_accuracy: 0.8371 - val_loss: 0.4646 - learning_rate: 0.0010\n",
      "Epoch 20/20\n",
      "\u001b[1m256/256\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m104s\u001b[0m 389ms/step - accuracy: 0.7897 - loss: 0.5827 - val_accuracy: 0.8462 - val_loss: 0.4137 - learning_rate: 0.0010\n"
     ]
    }
   ],
   "source": [
    "# Train the model\n",
    "modelZ = model.fit(\n",
    "    training_set,\n",
    "    steps_per_epoch=256,\n",
    "    epochs=20,\n",
    "    validation_data=test_set,\n",
    "    validation_steps=7,\n",
    "    callbacks=[lr_schedule, lr_reduction]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting Training and Validation Accuracy\n",
    "This section plots the training and validation accuracy and loss over time to visualize how the model performs during training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Final Training Accuracy: 0.7175\n",
      "Final Validation Accuracy: 0.7115\n"
     ]
    }
   ],
   "source": [
    "# Print the final training and validation accuracy\n",
    "train_accuracy = modelZ.history['accuracy'][-1]\n",
    "val_accuracy = modelZ.history['val_accuracy'][-1]\n",
    "\n",
    "print(f\"Final Training Accuracy: {train_accuracy:.4f}\")\n",
    "print(f\"Final Validation Accuracy: {val_accuracy:.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot training and validation accuracy over epochs\n",
    "plt.figure(figsize=(12, 16))\n",
    "\n",
    "plt.subplot(4, 2, 1)\n",
    "plt.plot(modelZ.history['loss'], label='Loss')\n",
    "plt.plot(modelZ.history['val_loss'], label='val_Loss')\n",
    "plt.title('Loss Function Evolution')\n",
    "plt.legend()\n",
    "\n",
    "plt.subplot(4, 2, 2)\n",
    "plt.plot(modelZ.history['accuracy'], label='Accuracy')\n",
    "plt.plot(modelZ.history['val_accuracy'], label='val_accuracy')\n",
    "plt.title('Accuracy Function Evolution')\n",
    "plt.legend()\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Saving the Trained Model\n",
    "After training, the model is saved to a file for future use.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# model.save('modelZ.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from tensorflow.keras.models import load_model\n",
    "\n",
    "# # Load the model\n",
    "# model = load_model('saved_model/my_model')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# print(\"Num GPUs Available: \", len(tf.config.list_physical_devices('GPU')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}