Terrain_V7 Optimizer Adagrad.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(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": 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.Adagrad(learning_rate=0.01), 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"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                         </span>┃<span style=\"font-weight: bold\"> Output Shape                </span>┃<span style=\"font-weight: bold\">         Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
       "│ conv2d (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">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 (<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_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\">64</span>)          │          <span style=\"color: #00af00; text-decoration-color: #00af00\">18,496</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ max_pooling2d_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">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_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\">128</span>)         │          <span style=\"color: #00af00; text-decoration-color: #00af00\">73,856</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ max_pooling2d_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">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 (<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 (<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_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;34m32\u001b[0m)          │             \u001b[38;5;34m896\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ max_pooling2d (\u001b[38;5;33mMaxPooling2D\u001b[0m)         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;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_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;34m64\u001b[0m)          │          \u001b[38;5;34m18,496\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ max_pooling2d_1 (\u001b[38;5;33mMaxPooling2D\u001b[0m)       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;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_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;34m128\u001b[0m)         │          \u001b[38;5;34m73,856\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ max_pooling2d_2 (\u001b[38;5;33mMaxPooling2D\u001b[0m)       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;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 (\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 (\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_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\">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": 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[1m157/256\u001b[0m \u001b[32m━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━\u001b[0m \u001b[1m1:04\u001b[0m 651ms/step - accuracy: 0.5264 - loss: 1.3535"
     ]
    },
    {
     "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[1m256/256\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m124s\u001b[0m 418ms/step - accuracy: 0.5467 - loss: 1.2821 - val_accuracy: 0.5179 - val_loss: 1.3052 - learning_rate: 0.0100\n",
      "Epoch 2/20\n",
      "\u001b[1m256/256\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m102s\u001b[0m 384ms/step - accuracy: 0.6345 - loss: 0.9802 - val_accuracy: 0.7308 - val_loss: 0.8200 - learning_rate: 0.0100\n",
      "Epoch 3/20\n",
      "\u001b[1m256/256\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m106s\u001b[0m 394ms/step - accuracy: 0.6709 - loss: 0.8821 - val_accuracy: 0.6763 - val_loss: 0.8596 - learning_rate: 0.0100\n",
      "Epoch 4/20\n",
      "\u001b[1m256/256\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m103s\u001b[0m 383ms/step - accuracy: 0.6755 - loss: 0.8636 - val_accuracy: 0.6731 - val_loss: 0.7199 - learning_rate: 0.0100\n",
      "Epoch 5/20\n",
      "\u001b[1m256/256\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m104s\u001b[0m 390ms/step - accuracy: 0.6896 - loss: 0.8146 - val_accuracy: 0.7232 - val_loss: 0.7290 - learning_rate: 0.0100\n",
      "Epoch 6/20\n",
      "\u001b[1m256/256\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m104s\u001b[0m 389ms/step - accuracy: 0.6981 - loss: 0.7953 - val_accuracy: 0.7885 - val_loss: 0.7248 - 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 395ms/step - accuracy: 0.7057 - loss: 0.7830 - val_accuracy: 0.7567 - val_loss: 0.6310 - learning_rate: 0.0100\n",
      "Epoch 8/20\n",
      "\u001b[1m256/256\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m104s\u001b[0m 387ms/step - accuracy: 0.7117 - loss: 0.7677 - val_accuracy: 0.8077 - val_loss: 0.4620 - learning_rate: 0.0100\n",
      "Epoch 9/20\n",
      "\u001b[1m256/256\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m104s\u001b[0m 391ms/step - accuracy: 0.7104 - loss: 0.7556 - val_accuracy: 0.7768 - val_loss: 0.6346 - learning_rate: 0.0100\n",
      "Epoch 10/20\n",
      "\u001b[1m256/256\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m102s\u001b[0m 385ms/step - accuracy: 0.7195 - loss: 0.7292 - val_accuracy: 0.7692 - val_loss: 0.4764 - learning_rate: 0.0100\n",
      "Epoch 11/20\n",
      "\u001b[1m256/256\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m102s\u001b[0m 383ms/step - accuracy: 0.7326 - loss: 0.7125 - val_accuracy: 0.7902 - val_loss: 0.5590 - learning_rate: 0.0010\n",
      "Epoch 12/20\n",
      "\u001b[1m256/256\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m102s\u001b[0m 382ms/step - accuracy: 0.7330 - loss: 0.7010 - val_accuracy: 0.7885 - val_loss: 0.5082 - learning_rate: 0.0010\n",
      "Epoch 13/20\n",
      "\u001b[1m256/256\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m105s\u001b[0m 391ms/step - accuracy: 0.7387 - loss: 0.6946 - val_accuracy: 0.7902 - val_loss: 0.5516 - learning_rate: 0.0010\n",
      "Epoch 14/20\n",
      "\u001b[1m256/256\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m103s\u001b[0m 386ms/step - accuracy: 0.7348 - loss: 0.6986 - val_accuracy: 0.8077 - val_loss: 0.5521 - learning_rate: 0.0010\n",
      "Epoch 15/20\n",
      "\u001b[1m256/256\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m105s\u001b[0m 395ms/step - accuracy: 0.7415 - loss: 0.6876 - val_accuracy: 0.7946 - val_loss: 0.5503 - 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: 0.7414 - loss: 0.6864 - val_accuracy: 0.8077 - val_loss: 0.5322 - 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 392ms/step - accuracy: 0.7325 - loss: 0.7018 - val_accuracy: 0.7969 - val_loss: 0.5588 - learning_rate: 0.0010\n",
      "Epoch 18/20\n",
      "\u001b[1m256/256\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m81s\u001b[0m 301ms/step - accuracy: 0.7341 - loss: 0.6869 - val_accuracy: 0.7885 - val_loss: 0.4976 - learning_rate: 0.0010\n",
      "Epoch 19/20\n",
      "\u001b[1m256/256\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m79s\u001b[0m 299ms/step - accuracy: 0.7314 - loss: 0.6996 - val_accuracy: 0.8058 - val_loss: 0.5465 - learning_rate: 0.0010\n",
      "Epoch 20/20\n",
      "\u001b[1m256/256\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m59s\u001b[0m 218ms/step - accuracy: 0.7437 - loss: 0.6892 - val_accuracy: 0.7692 - val_loss: 0.5833 - 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": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Final Training Accuracy: 0.7439\n",
      "Final Validation Accuracy: 0.7692\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
}