Terrain_V3.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing necessary Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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": 15,
   "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": 16,
   "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the CNN model architecture\n",
    "model = tf.keras.Sequential([\n",
    "\n",
    "    # Input layer\n",
    "    tf.keras.layers.Input(shape=(64, 64, 3)),\n",
    "    \n",
    "    # First convolutional layer\n",
    "    tf.keras.layers.Conv2D(32, kernel_size=3, activation='relu'),\n",
    "    tf.keras.layers.MaxPool2D(pool_size=2, strides=2),\n",
    "    tf.keras.layers.BatchNormalization(),\n",
    "    \n",
    "    # Second convolutional layer\n",
    "    tf.keras.layers.Conv2D(64, kernel_size=3, activation='relu'),\n",
    "    tf.keras.layers.MaxPool2D(pool_size=2, strides=2),\n",
    "    tf.keras.layers.BatchNormalization(),\n",
    "\n",
    "    # Third convolutional layer\n",
    "    tf.keras.layers.Conv2D(128, kernel_size=3, activation='relu'),\n",
    "    tf.keras.layers.MaxPool2D(pool_size=2, strides=2),\n",
    "    tf.keras.layers.BatchNormalization(),\n",
    "    \n",
    "    # Fully connected classifier\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"
   ]
  },
  {
   "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": 18,
   "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",
    "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": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data augmentation and preprocessing for training and validation datasets\n",
    "# The ImageDataGenerator applies various transformations to augment the training data\n",
    "train_datagen = ImageDataGenerator(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",
    "# For the validation set, we only rescale the pixel values\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": 20,
   "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": [
    "# Update the directory paths to point to your local Downloads folder\n",
    "training_set = train_datagen.flow_from_directory(r'C:\\Users\\draka\\Downloads\\Terrain_Images\\Training Data',\n",
    "                                                 target_size=(64, 64),\n",
    "                                                 batch_size=64,\n",
    "                                                 class_mode='categorical')\n",
    "\n",
    "test_set = test_datagen.flow_from_directory(r'C:\\Users\\draka\\Downloads\\Terrain_Images\\Testing Data',\n",
    "                                            target_size=(64, 64),\n",
    "                                            batch_size=64,\n",
    "                                            class_mode='categorical')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define a learning rate scheduler and reduce the learning rate when the accuracy plateaus\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)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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_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\">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_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\">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             │ (<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_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\">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_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\">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_1           │ (<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_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\">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_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\">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_2           │ (<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_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\">4608</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\">1024</span>)           │     <span style=\"color: #00af00; text-decoration-color: #00af00\">4,719,616</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\">1024</span>)           │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ batch_normalization_3           │ (<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_4 (<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_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\">1024</span>)           │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ batch_normalization_4           │ (<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_5 (<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_2 (<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_5           │ (<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_6 (<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_2 (\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_2 (\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             │ (\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_3 (\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_3 (\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_1           │ (\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_4 (\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_4 (\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_2           │ (\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_1 (\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_3 (\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 (\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_3           │ (\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_4 (\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_1 (\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_4           │ (\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_5 (\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_2 (\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_5           │ (\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_6 (\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": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "\u001b[1m2000/2000\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m31s\u001b[0m 15ms/step - accuracy: 0.7851 - loss: 0.5746 - val_accuracy: 0.8058 - val_loss: 0.5299\n",
      "Epoch 2/20\n",
      "\u001b[1m2000/2000\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m30s\u001b[0m 15ms/step - accuracy: 0.7895 - loss: 0.5640 - val_accuracy: 0.7885 - val_loss: 0.5749\n",
      "Epoch 3/20\n",
      "\u001b[1m2000/2000\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m31s\u001b[0m 15ms/step - accuracy: 0.7849 - loss: 0.5761 - val_accuracy: 0.7656 - val_loss: 0.6844\n",
      "Epoch 4/20\n",
      "\u001b[1m2000/2000\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m30s\u001b[0m 15ms/step - accuracy: 0.7969 - loss: 0.5446 - val_accuracy: 0.7692 - val_loss: 0.4869\n",
      "Epoch 5/20\n",
      "\u001b[1m2000/2000\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m31s\u001b[0m 15ms/step - accuracy: 0.7997 - loss: 0.5350 - val_accuracy: 0.8393 - val_loss: 0.4124\n",
      "Epoch 6/20\n",
      "\u001b[1m2000/2000\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m30s\u001b[0m 15ms/step - accuracy: 0.7984 - loss: 0.5400 - val_accuracy: 0.7308 - val_loss: 0.8300\n",
      "Epoch 7/20\n",
      "\u001b[1m2000/2000\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m30s\u001b[0m 15ms/step - accuracy: 0.8022 - loss: 0.5343 - val_accuracy: 0.8348 - val_loss: 0.4604\n",
      "Epoch 8/20\n",
      "\u001b[1m2000/2000\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m30s\u001b[0m 15ms/step - accuracy: 0.7989 - loss: 0.5368 - val_accuracy: 0.7308 - val_loss: 0.6008\n",
      "Epoch 9/20\n",
      "\u001b[1m2000/2000\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m30s\u001b[0m 15ms/step - accuracy: 0.8116 - loss: 0.5150 - val_accuracy: 0.7790 - val_loss: 0.6313\n",
      "Epoch 10/20\n",
      "\u001b[1m2000/2000\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m30s\u001b[0m 15ms/step - accuracy: 0.8069 - loss: 0.5171 - val_accuracy: 0.7885 - val_loss: 0.4614\n",
      "Epoch 11/20\n",
      "\u001b[1m2000/2000\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m30s\u001b[0m 15ms/step - accuracy: 0.8067 - loss: 0.5171 - val_accuracy: 0.7879 - val_loss: 0.5566\n",
      "Epoch 12/20\n",
      "\u001b[1m2000/2000\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m30s\u001b[0m 15ms/step - accuracy: 0.8171 - loss: 0.4964 - val_accuracy: 0.8077 - val_loss: 0.5560\n",
      "Epoch 13/20\n",
      "\u001b[1m2000/2000\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m31s\u001b[0m 15ms/step - accuracy: 0.8144 - loss: 0.5001 - val_accuracy: 0.8080 - val_loss: 0.5437\n",
      "Epoch 14/20\n",
      "\u001b[1m2000/2000\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m30s\u001b[0m 15ms/step - accuracy: 0.8193 - loss: 0.4887 - val_accuracy: 0.8077 - val_loss: 0.4929\n",
      "Epoch 15/20\n",
      "\u001b[1m2000/2000\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m31s\u001b[0m 15ms/step - accuracy: 0.8198 - loss: 0.4781 - val_accuracy: 0.8170 - val_loss: 0.4683\n",
      "Epoch 16/20\n",
      "\u001b[1m2000/2000\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m30s\u001b[0m 15ms/step - accuracy: 0.8242 - loss: 0.4783 - val_accuracy: 0.7500 - val_loss: 0.7382\n",
      "Epoch 17/20\n",
      "\u001b[1m2000/2000\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m31s\u001b[0m 15ms/step - accuracy: 0.8233 - loss: 0.4869 - val_accuracy: 0.7835 - val_loss: 0.6065\n",
      "Epoch 18/20\n",
      "\u001b[1m2000/2000\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m30s\u001b[0m 15ms/step - accuracy: 0.8259 - loss: 0.4707 - val_accuracy: 0.8462 - val_loss: 0.4422\n",
      "Epoch 19/20\n",
      "\u001b[1m2000/2000\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m31s\u001b[0m 15ms/step - accuracy: 0.8265 - loss: 0.4711 - val_accuracy: 0.8036 - val_loss: 0.5277\n",
      "Epoch 20/20\n",
      "\u001b[1m2000/2000\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m30s\u001b[0m 15ms/step - accuracy: 0.8305 - loss: 0.4483 - val_accuracy: 0.8462 - val_loss: 0.4460\n"
     ]
    }
   ],
   "source": [
    "# Train the model \n",
    "modelZ = model.fit(\n",
    "    training_set,\n",
    "    steps_per_epoch=2000,\n",
    "    epochs=20,\n",
    "    validation_data=test_set,\n",
    "    validation_steps=7\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": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x18c9410b450>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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"
   ]
  },
  {
   "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": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
     ]
    }
   ],
   "source": [
    "model.save('modelZ.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Num GPUs Available:  0\n"
     ]
    }
   ],
   "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
}