Terrain_V5 Optimizer SGD with Momentum-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\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(64, 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(128, 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(256, kernel_size=3, activation='relu'),\n",
    "    tf.keras.layers.MaxPool2D(pool_size=2, strides=2),\n",
    "    tf.keras.layers.BatchNormalization(),\n",
    "    \n",
    "    # Fourth convolutional layer\n",
    "    tf.keras.layers.Conv2D(512, kernel_size=3, activation='relu'),\n",
    "    tf.keras.layers.MaxPool2D(pool_size=2, strides=2),\n",
    "    tf.keras.layers.BatchNormalization(),\n",
    "    \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(512, 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": 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.SGD(learning_rate=0.01, momentum=0.9), 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"
      ]
     },
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     "output_type": "display_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",
       "│ 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\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)           │       <span style=\"color: #00af00; text-decoration-color: #00af00\">1,180,160</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\">2</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</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\">2</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)           │           <span style=\"color: #00af00; text-decoration-color: #00af00\">2,048</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\">2048</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\">2,098,176</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_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_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_5                │ (<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\">512</span>)                 │         <span style=\"color: #00af00; text-decoration-color: #00af00\">524,800</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\">512</span>)                 │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ batch_normalization_6                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)                 │           <span style=\"color: #00af00; text-decoration-color: #00af00\">2,048</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\">2,565</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",
       "│ conv2d_3 (\u001b[38;5;33mConv2D\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m512\u001b[0m)           │       \u001b[38;5;34m1,180,160\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ max_pooling2d_3 (\u001b[38;5;33mMaxPooling2D\u001b[0m)       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m512\u001b[0m)           │               \u001b[38;5;34m0\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ batch_normalization_3                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m512\u001b[0m)           │           \u001b[38;5;34m2,048\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;34m2048\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;34m2,098,176\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_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_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_5                │ (\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;34m512\u001b[0m)                 │         \u001b[38;5;34m524,800\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dropout_2 (\u001b[38;5;33mDropout\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m512\u001b[0m)                 │               \u001b[38;5;34m0\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ batch_normalization_6                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m512\u001b[0m)                 │           \u001b[38;5;34m2,048\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;34m2,565\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\">5,240,197</span> (19.99 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m5,240,197\u001b[0m (19.99 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\">5,233,157</span> (19.96 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m5,233,157\u001b[0m (19.96 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\">7,040</span> (27.50 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m7,040\u001b[0m (27.50 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[1m 157/1024\u001b[0m \u001b[32m━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m14:17\u001b[0m 989ms/step - accuracy: 0.4626 - loss: 1.5149"
     ]
    },
    {
     "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[1m1024/1024\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m188s\u001b[0m 157ms/step - accuracy: 0.5140 - loss: 1.3314 - val_accuracy: 0.4308 - val_loss: 1.3102 - learning_rate: 0.0100\n",
      "Epoch 2/20\n",
      "\u001b[1m1024/1024\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m152s\u001b[0m 143ms/step - accuracy: 0.6109 - loss: 1.0252 - val_accuracy: 0.5000 - val_loss: 1.1684 - learning_rate: 0.0100\n",
      "Epoch 3/20\n",
      "\u001b[1m1024/1024\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m194s\u001b[0m 183ms/step - accuracy: 0.6317 - loss: 0.9710 - val_accuracy: 0.6897 - val_loss: 0.7787 - learning_rate: 0.0100\n",
      "Epoch 4/20\n",
      "\u001b[1m1024/1024\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m183s\u001b[0m 169ms/step - accuracy: 0.6497 - loss: 0.9245 - val_accuracy: 0.6731 - val_loss: 0.7455 - learning_rate: 0.0100\n",
      "Epoch 5/20\n",
      "\u001b[1m1024/1024\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m195s\u001b[0m 182ms/step - accuracy: 0.6599 - loss: 0.8963 - val_accuracy: 0.7321 - val_loss: 0.7603 - learning_rate: 0.0100\n",
      "Epoch 6/20\n",
      "\u001b[1m1024/1024\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m228s\u001b[0m 215ms/step - accuracy: 0.6779 - loss: 0.8503 - val_accuracy: 0.6923 - val_loss: 0.8339 - learning_rate: 0.0100\n",
      "Epoch 7/20\n",
      "\u001b[1m1024/1024\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m186s\u001b[0m 174ms/step - accuracy: 0.6769 - loss: 0.8563 - val_accuracy: 0.5871 - val_loss: 1.0309 - learning_rate: 0.0100\n",
      "Epoch 8/20\n",
      "\u001b[1m1024/1024\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m220s\u001b[0m 206ms/step - accuracy: 0.6814 - loss: 0.8313 - val_accuracy: 0.6538 - val_loss: 0.9094 - learning_rate: 0.0100\n",
      "Epoch 9/20\n",
      "\u001b[1m1024/1024\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m229s\u001b[0m 212ms/step - accuracy: 0.6997 - loss: 0.8143 - val_accuracy: 0.6004 - val_loss: 1.0904 - learning_rate: 0.0100\n",
      "Epoch 10/20\n",
      "\u001b[1m1024/1024\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m172s\u001b[0m 157ms/step - accuracy: 0.7021 - loss: 0.7924 - val_accuracy: 0.6731 - val_loss: 0.7978 - learning_rate: 0.0100\n",
      "Epoch 11/20\n",
      "\u001b[1m1024/1024\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m142s\u001b[0m 132ms/step - accuracy: 0.7170 - loss: 0.7424 - val_accuracy: 0.8103 - val_loss: 0.5171 - learning_rate: 0.0010\n",
      "Epoch 12/20\n",
      "\u001b[1m1024/1024\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m136s\u001b[0m 127ms/step - accuracy: 0.7380 - loss: 0.6932 - val_accuracy: 0.6346 - val_loss: 0.7559 - learning_rate: 0.0010\n",
      "Epoch 13/20\n",
      "\u001b[1m1024/1024\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m135s\u001b[0m 127ms/step - accuracy: 0.7417 - loss: 0.6944 - val_accuracy: 0.7679 - val_loss: 0.5687 - learning_rate: 0.0010\n",
      "Epoch 14/20\n",
      "\u001b[1m1024/1024\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m137s\u001b[0m 128ms/step - accuracy: 0.7487 - loss: 0.6717 - val_accuracy: 0.8462 - val_loss: 0.4664 - learning_rate: 0.0010\n",
      "Epoch 15/20\n",
      "\u001b[1m1024/1024\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m133s\u001b[0m 124ms/step - accuracy: 0.7470 - loss: 0.6727 - val_accuracy: 0.8103 - val_loss: 0.5160 - learning_rate: 0.0010\n",
      "Epoch 16/20\n",
      "\u001b[1m1024/1024\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m96s\u001b[0m 90ms/step - accuracy: 0.7480 - loss: 0.6764 - val_accuracy: 0.8077 - val_loss: 0.3926 - learning_rate: 0.0010\n",
      "Epoch 17/20\n",
      "\u001b[1m1024/1024\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m94s\u001b[0m 89ms/step - accuracy: 0.7533 - loss: 0.6591 - val_accuracy: 0.8036 - val_loss: 0.5209 - learning_rate: 0.0010\n",
      "Epoch 18/20\n",
      "\u001b[1m1024/1024\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m93s\u001b[0m 88ms/step - accuracy: 0.7544 - loss: 0.6637 - val_accuracy: 0.7885 - val_loss: 0.5641 - learning_rate: 0.0010\n",
      "Epoch 19/20\n",
      "\u001b[1m1024/1024\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m95s\u001b[0m 88ms/step - accuracy: 0.7539 - loss: 0.6637 - val_accuracy: 0.8058 - val_loss: 0.5181 - learning_rate: 0.0010\n",
      "Epoch 20/20\n",
      "\u001b[1m1024/1024\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m93s\u001b[0m 88ms/step - accuracy: 0.7600 - loss: 0.6468 - val_accuracy: 0.7115 - val_loss: 0.5805 - learning_rate: 0.0010\n"
     ]
    }
   ],
   "source": [
    "# Train the model\n",
    "modelZ = model.fit(\n",
    "    training_set,\n",
    "    steps_per_epoch=1024,\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: 0.7609\n",
      "Final Validation Accuracy: 0.7115\n"
     ]
    }
   ],
   "source": [
    "# Print the final training and validation accuracy\n",
    "train_accuracy = modelZ.history['accuracy'][-1]\n",
    "val_accuracy = modelZ.history['val_accuracy'][-1]\n",
    "\n",
    "print(f\"Final Training Accuracy: {train_accuracy:.4f}\")\n",
    "print(f\"Final Validation Accuracy: {val_accuracy:.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 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
}