Terrain_V4-Copy1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing necessary Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "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": 2,
   "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": 3,
   "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": 4,
   "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(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.Conv2D(256, 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": 5,
   "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=tf.keras.optimizers.Adam(learning_rate=0.001), 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": 6,
   "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": 7,
   "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": 8,
   "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": 9,
   "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\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"sequential\"\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 (<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\">64</span>)          │           <span style=\"color: #00af00; text-decoration-color: #00af00\">1,792</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\">31</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">31</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                  │ (<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\">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_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\">29</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">29</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_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\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</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_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\">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",
       "│ 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\">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\">295,168</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\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</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_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\">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",
       "│ 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\">9216</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\">1024</span>)                │       <span style=\"color: #00af00; text-decoration-color: #00af00\">9,438,208</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_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\">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_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\">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_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 (\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;34m64\u001b[0m)          │           \u001b[38;5;34m1,792\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ max_pooling2d (\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;34m64\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;34m64\u001b[0m)          │             \u001b[38;5;34m256\u001b[0m │\n",
       "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)                 │                             │                 │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ conv2d_1 (\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;34m128\u001b[0m)         │          \u001b[38;5;34m73,856\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ max_pooling2d_1 (\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;34m128\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;34m128\u001b[0m)         │             \u001b[38;5;34m512\u001b[0m │\n",
       "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)                 │                             │                 │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ conv2d_2 (\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;34m256\u001b[0m)         │         \u001b[38;5;34m295,168\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ max_pooling2d_2 (\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;34m256\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;34m256\u001b[0m)           │           \u001b[38;5;34m1,024\u001b[0m │\n",
       "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)                 │                             │                 │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ flatten (\u001b[38;5;33mFlatten\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m9216\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;34m1024\u001b[0m)                │       \u001b[38;5;34m9,438,208\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_1 (\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_2 (\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_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\">11,133,317</span> (42.47 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m11,133,317\u001b[0m (42.47 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\">11,127,813</span> (42.45 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m11,127,813\u001b[0m (42.45 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\">5,504</span> (21.50 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m5,504\u001b[0m (21.50 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": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\DEEPANSHU\\anaconda3\\Lib\\site-packages\\keras\\src\\trainers\\data_adapters\\py_dataset_adapter.py:121: UserWarning: Your `PyDataset` class should call `super().__init__(**kwargs)` in its constructor. `**kwargs` can include `workers`, `use_multiprocessing`, `max_queue_size`. Do not pass these arguments to `fit()`, as they will be ignored.\n",
      "  self._warn_if_super_not_called()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m157/512\u001b[0m \u001b[32m━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━\u001b[0m \u001b[1m2:28\u001b[0m 419ms/step - accuracy: 0.5077 - loss: 1.4332"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\DEEPANSHU\\anaconda3\\Lib\\contextlib.py:155: UserWarning: Your input ran out of data; interrupting training. Make sure that your dataset or generator can generate at least `steps_per_epoch * epochs` batches. You may need to use the `.repeat()` function when building your dataset.\n",
      "  self.gen.throw(typ, value, traceback)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m72s\u001b[0m 130ms/step - accuracy: 0.5473 - loss: 1.2346 - val_accuracy: 0.5469 - val_loss: 1.1331 - learning_rate: 0.0100\n",
      "Epoch 2/20\n",
      "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m64s\u001b[0m 124ms/step - accuracy: 0.6323 - loss: 0.9565 - val_accuracy: 0.5769 - val_loss: 1.0547 - learning_rate: 0.0100\n",
      "Epoch 3/20\n",
      "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m65s\u001b[0m 125ms/step - accuracy: 0.6493 - loss: 0.9014 - val_accuracy: 0.6250 - val_loss: 1.2975 - learning_rate: 0.0100\n",
      "Epoch 4/20\n",
      "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m65s\u001b[0m 125ms/step - accuracy: 0.6716 - loss: 0.8550 - val_accuracy: 0.7692 - val_loss: 0.6250 - learning_rate: 0.0100\n",
      "Epoch 5/20\n",
      "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m68s\u001b[0m 131ms/step - accuracy: 0.6809 - loss: 0.8325 - val_accuracy: 0.5469 - val_loss: 1.2378 - learning_rate: 0.0100\n",
      "Epoch 6/20\n",
      "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m69s\u001b[0m 132ms/step - accuracy: 0.6944 - loss: 0.8136 - val_accuracy: 0.7692 - val_loss: 0.7075 - learning_rate: 0.0100\n",
      "Epoch 7/20\n",
      "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m69s\u001b[0m 132ms/step - accuracy: 0.6911 - loss: 0.7981 - val_accuracy: 0.4442 - val_loss: 1.9784 - learning_rate: 0.0100\n",
      "Epoch 8/20\n",
      "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m67s\u001b[0m 129ms/step - accuracy: 0.6969 - loss: 0.7973 - val_accuracy: 0.4038 - val_loss: 1.8025 - learning_rate: 0.0100\n",
      "Epoch 9/20\n",
      "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m100s\u001b[0m 193ms/step - accuracy: 0.7093 - loss: 0.7852 - val_accuracy: 0.7031 - val_loss: 0.9339 - learning_rate: 0.0100\n",
      "Epoch 10/20\n",
      "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m110s\u001b[0m 207ms/step - accuracy: 0.7181 - loss: 0.7566 - val_accuracy: 0.7115 - val_loss: 0.7876 - learning_rate: 0.0100\n",
      "Epoch 11/20\n",
      "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m145s\u001b[0m 277ms/step - accuracy: 0.7425 - loss: 0.6846 - val_accuracy: 0.7545 - val_loss: 0.6115 - learning_rate: 0.0010\n",
      "Epoch 12/20\n",
      "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m146s\u001b[0m 275ms/step - accuracy: 0.7507 - loss: 0.6655 - val_accuracy: 0.7885 - val_loss: 0.5464 - learning_rate: 0.0010\n",
      "Epoch 13/20\n",
      "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m152s\u001b[0m 287ms/step - accuracy: 0.7497 - loss: 0.6606 - val_accuracy: 0.7857 - val_loss: 0.5490 - learning_rate: 0.0010\n",
      "Epoch 14/20\n",
      "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m186s\u001b[0m 351ms/step - accuracy: 0.7560 - loss: 0.6464 - val_accuracy: 0.7692 - val_loss: 0.5655 - learning_rate: 0.0010\n",
      "Epoch 15/20\n",
      "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m214s\u001b[0m 401ms/step - accuracy: 0.7608 - loss: 0.6395 - val_accuracy: 0.7768 - val_loss: 0.5530 - learning_rate: 0.0010\n",
      "Epoch 16/20\n",
      "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m187s\u001b[0m 348ms/step - accuracy: 0.7591 - loss: 0.6357 - val_accuracy: 0.8462 - val_loss: 0.3674 - learning_rate: 0.0010\n",
      "Epoch 17/20\n",
      "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m195s\u001b[0m 365ms/step - accuracy: 0.7639 - loss: 0.6335 - val_accuracy: 0.8214 - val_loss: 0.5036 - learning_rate: 0.0010\n",
      "Epoch 18/20\n",
      "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m204s\u001b[0m 391ms/step - accuracy: 0.7673 - loss: 0.6164 - val_accuracy: 0.7885 - val_loss: 0.5348 - learning_rate: 0.0010\n",
      "Epoch 19/20\n",
      "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m199s\u001b[0m 373ms/step - accuracy: 0.7724 - loss: 0.6068 - val_accuracy: 0.8147 - val_loss: 0.5197 - learning_rate: 0.0010\n",
      "Epoch 20/20\n",
      "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m215s\u001b[0m 403ms/step - accuracy: 0.7654 - loss: 0.6187 - val_accuracy: 0.8269 - val_loss: 0.4078 - learning_rate: 0.0010\n"
     ]
    }
   ],
   "source": [
    "# Train the model\n",
    "modelZ = model.fit(\n",
    "    training_set,\n",
    "    steps_per_epoch=512,\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": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Final Training Accuracy: 0.7643\n",
      "Final Validation Accuracy: 0.8269\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": 12,
   "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": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# model.save('modelZ.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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": 15,
   "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
}