Terrain_V4-4 layer cnn.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='adam', loss='categorical_crossentropy', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Augmentation and Preprocessing\n",
    "This section applies data augmentation techniques using the `ImageDataGenerator` from Keras to generate more training samples and preprocess the images. These operations include rescaling, rotating, shifting, and flipping.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 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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       "<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[1m157/512\u001b[0m \u001b[32m━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━\u001b[0m \u001b[1m4:03\u001b[0m 685ms/step - accuracy: 0.4655 - loss: 1.7955"
     ]
    },
    {
     "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[1m132s\u001b[0m 219ms/step - accuracy: 0.5078 - loss: 1.4843 - val_accuracy: 0.4665 - val_loss: 2.2783 - learning_rate: 0.0100\n",
      "Epoch 2/20\n",
      "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m116s\u001b[0m 219ms/step - accuracy: 0.5944 - loss: 1.0533 - val_accuracy: 0.5385 - val_loss: 1.0627 - learning_rate: 0.0100\n",
      "Epoch 3/20\n",
      "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m143s\u001b[0m 264ms/step - accuracy: 0.6415 - loss: 0.9350 - val_accuracy: 0.6473 - val_loss: 0.9491 - learning_rate: 0.0100\n",
      "Epoch 4/20\n",
      "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m136s\u001b[0m 256ms/step - accuracy: 0.6596 - loss: 0.8921 - val_accuracy: 0.5962 - val_loss: 1.3253 - learning_rate: 0.0100\n",
      "Epoch 5/20\n",
      "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m138s\u001b[0m 260ms/step - accuracy: 0.6764 - loss: 0.8428 - val_accuracy: 0.7188 - val_loss: 0.8193 - learning_rate: 0.0100\n",
      "Epoch 6/20\n",
      "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m151s\u001b[0m 283ms/step - accuracy: 0.6833 - loss: 0.8383 - val_accuracy: 0.6154 - val_loss: 1.0418 - learning_rate: 0.0100\n",
      "Epoch 7/20\n",
      "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m153s\u001b[0m 287ms/step - accuracy: 0.6966 - loss: 0.7967 - val_accuracy: 0.7433 - val_loss: 0.7064 - learning_rate: 0.0100\n",
      "Epoch 8/20\n",
      "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m153s\u001b[0m 289ms/step - accuracy: 0.6917 - loss: 0.8019 - val_accuracy: 0.5769 - val_loss: 0.9251 - learning_rate: 0.0100\n",
      "Epoch 9/20\n",
      "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m161s\u001b[0m 305ms/step - accuracy: 0.7077 - loss: 0.7607 - val_accuracy: 0.6674 - val_loss: 0.9722 - learning_rate: 0.0100\n",
      "Epoch 10/20\n",
      "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m153s\u001b[0m 284ms/step - accuracy: 0.7118 - loss: 0.7526 - val_accuracy: 0.4808 - val_loss: 1.5232 - learning_rate: 0.0100\n",
      "Epoch 11/20\n",
      "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m157s\u001b[0m 293ms/step - accuracy: 0.7455 - loss: 0.6859 - val_accuracy: 0.8036 - val_loss: 0.5270 - learning_rate: 0.0010\n",
      "Epoch 12/20\n",
      "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m154s\u001b[0m 290ms/step - accuracy: 0.7585 - loss: 0.6439 - val_accuracy: 0.7885 - val_loss: 0.5578 - learning_rate: 0.0010\n",
      "Epoch 13/20\n",
      "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m154s\u001b[0m 290ms/step - accuracy: 0.7623 - loss: 0.6328 - val_accuracy: 0.7924 - val_loss: 0.5452 - learning_rate: 0.0010\n",
      "Epoch 14/20\n",
      "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m153s\u001b[0m 285ms/step - accuracy: 0.7657 - loss: 0.6340 - val_accuracy: 0.8269 - val_loss: 0.4478 - learning_rate: 0.0010\n",
      "Epoch 15/20\n",
      "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m132s\u001b[0m 247ms/step - accuracy: 0.7633 - loss: 0.6228 - val_accuracy: 0.7946 - val_loss: 0.5158 - learning_rate: 0.0010\n",
      "Epoch 16/20\n",
      "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m127s\u001b[0m 240ms/step - accuracy: 0.7647 - loss: 0.6210 - val_accuracy: 0.8462 - val_loss: 0.4261 - learning_rate: 0.0010\n",
      "Epoch 17/20\n",
      "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m130s\u001b[0m 245ms/step - accuracy: 0.7761 - loss: 0.6021 - val_accuracy: 0.8058 - val_loss: 0.5100 - learning_rate: 0.0010\n",
      "Epoch 18/20\n",
      "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m128s\u001b[0m 243ms/step - accuracy: 0.7759 - loss: 0.6073 - val_accuracy: 0.7692 - val_loss: 0.6010 - learning_rate: 0.0010\n",
      "Epoch 19/20\n",
      "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m130s\u001b[0m 245ms/step - accuracy: 0.7762 - loss: 0.6008 - val_accuracy: 0.8214 - val_loss: 0.4476 - learning_rate: 0.0010\n",
      "Epoch 20/20\n",
      "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m127s\u001b[0m 239ms/step - accuracy: 0.7807 - loss: 0.5911 - val_accuracy: 0.7885 - val_loss: 0.4745 - 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: 0.7797\n",
      "Final Validation Accuracy: 0.7885\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
}