Terrain_V6 Optimizer RMSpop-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.RMSprop(learning_rate=0.001), loss='categorical_crossentropy', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Augmentation and Preprocessing\n",
    "This section applies data augmentation techniques using the `ImageDataGenerator` from Keras to generate more training samples and preprocess the images. These operations include rescaling, rotating, shifting, and flipping.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data augmentation and preprocessing for training and validation datasets\n",
    "# The ImageDataGenerator applies various transformations to augment the training data\n",
    "# Data augmentation for training dataset\n",
    "train_datagen = ImageDataGenerator(\n",
    "    rescale=1./255,\n",
    "    shear_range=0.2,\n",
    "    zoom_range=0.2,\n",
    "    horizontal_flip=True,\n",
    "    rotation_range=30,\n",
    "    brightness_range=[0.8, 1.2],\n",
    "    width_shift_range=0.2,\n",
    "    height_shift_range=0.2\n",
    ")\n",
    "\n",
    "# Validation dataset (only rescaling)\n",
    "test_datagen = ImageDataGenerator(rescale=1./255)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading and Preparing the Data\n",
    "In this section, the training and validation data are loaded from the respective directories, and the images are prepared for the model using the augmentation techniques defined earlier.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 10000 images belonging to 5 classes.\n",
      "Found 500 images belonging to 5 classes.\n"
     ]
    }
   ],
   "source": [
    "# Assuming you've created 'train_subset' and 'test_subset' directories\n",
    "training_set = train_datagen.flow_from_directory(\n",
    "    r\"D:\\Jupyter Notebook\\Contribution\\Terrain-Recognition\\Data Main\\Training Data\",\n",
    "    target_size=(64, 64),\n",
    "    batch_size=64,\n",
    "    class_mode='categorical'\n",
    ")\n",
    "\n",
    "test_set = test_datagen.flow_from_directory(\n",
    "    r\"D:\\Jupyter Notebook\\Contribution\\Terrain-Recognition\\Data Main\\Testing Data\",\n",
    "    target_size=(64, 64),\n",
    "    batch_size=64,\n",
    "    class_mode='categorical'\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define learning rate scheduler\n",
    "def scheduler(epoch, lr):\n",
    "    if epoch < 10:\n",
    "        return 1e-2\n",
    "    else:\n",
    "        return 1e-3\n",
    "\n",
    "lr_schedule = LearningRateScheduler(scheduler)\n",
    "lr_reduction = ReduceLROnPlateau(monitor='val_loss', factor=0.5, patience=3, min_lr=1e-6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"sequential\"\u001b[0m\n"
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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[1m20:11\u001b[0m 1s/step - accuracy: 0.4198 - loss: 2.1427"
     ]
    },
    {
     "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[1m263s\u001b[0m 225ms/step - accuracy: 0.4651 - loss: 1.7105 - val_accuracy: 0.2277 - val_loss: 20.4152 - learning_rate: 0.0100\n",
      "Epoch 2/20\n",
      "\u001b[1m1024/1024\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m209s\u001b[0m 195ms/step - accuracy: 0.6027 - loss: 1.0550 - val_accuracy: 0.5769 - val_loss: 2.0419 - learning_rate: 0.0100\n",
      "Epoch 3/20\n",
      "\u001b[1m1024/1024\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m224s\u001b[0m 214ms/step - accuracy: 0.6451 - loss: 0.9274 - val_accuracy: 0.6071 - val_loss: 1.0307 - learning_rate: 0.0100\n",
      "Epoch 4/20\n",
      "\u001b[1m1024/1024\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m245s\u001b[0m 229ms/step - accuracy: 0.6735 - loss: 0.8691 - val_accuracy: 0.5962 - val_loss: 0.9384 - learning_rate: 0.0100\n",
      "Epoch 5/20\n",
      "\u001b[1m1024/1024\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m210s\u001b[0m 200ms/step - accuracy: 0.6746 - loss: 0.8487 - val_accuracy: 0.7054 - val_loss: 0.8074 - learning_rate: 0.0100\n",
      "Epoch 6/20\n",
      "\u001b[1m1024/1024\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m220s\u001b[0m 205ms/step - accuracy: 0.6893 - loss: 0.8319 - val_accuracy: 0.7885 - val_loss: 0.6377 - learning_rate: 0.0100\n",
      "Epoch 7/20\n",
      "\u001b[1m1024/1024\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m163s\u001b[0m 149ms/step - accuracy: 0.7008 - loss: 0.7887 - val_accuracy: 0.7478 - val_loss: 0.6563 - learning_rate: 0.0100\n",
      "Epoch 8/20\n",
      "\u001b[1m1024/1024\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m134s\u001b[0m 127ms/step - accuracy: 0.7154 - loss: 0.7672 - val_accuracy: 0.7308 - val_loss: 0.9700 - learning_rate: 0.0100\n",
      "Epoch 9/20\n",
      "\u001b[1m1024/1024\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m139s\u001b[0m 130ms/step - accuracy: 0.7175 - loss: 0.7561 - val_accuracy: 0.6451 - val_loss: 1.0886 - learning_rate: 0.0100\n",
      "Epoch 10/20\n",
      "\u001b[1m1024/1024\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m138s\u001b[0m 129ms/step - accuracy: 0.7279 - loss: 0.7405 - val_accuracy: 0.5962 - val_loss: 0.9130 - learning_rate: 0.0100\n",
      "Epoch 11/20\n",
      "\u001b[1m1024/1024\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m136s\u001b[0m 127ms/step - accuracy: 0.7561 - loss: 0.6573 - val_accuracy: 0.7924 - val_loss: 0.5197 - learning_rate: 0.0010\n",
      "Epoch 12/20\n",
      "\u001b[1m1024/1024\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m122s\u001b[0m 114ms/step - accuracy: 0.7652 - loss: 0.6308 - val_accuracy: 0.8462 - val_loss: 0.4266 - learning_rate: 0.0010\n",
      "Epoch 13/20\n",
      "\u001b[1m1024/1024\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m96s\u001b[0m 91ms/step - accuracy: 0.7720 - loss: 0.6216 - val_accuracy: 0.8058 - val_loss: 0.5064 - learning_rate: 0.0010\n",
      "Epoch 14/20\n",
      "\u001b[1m1024/1024\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m91s\u001b[0m 85ms/step - accuracy: 0.7689 - loss: 0.6170 - val_accuracy: 0.8077 - val_loss: 0.4961 - learning_rate: 0.0010\n",
      "Epoch 15/20\n",
      "\u001b[1m1024/1024\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m92s\u001b[0m 87ms/step - accuracy: 0.7712 - loss: 0.6198 - val_accuracy: 0.7701 - val_loss: 0.5566 - learning_rate: 0.0010\n",
      "Epoch 16/20\n",
      "\u001b[1m1024/1024\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m92s\u001b[0m 87ms/step - accuracy: 0.7745 - loss: 0.6184 - val_accuracy: 0.9038 - val_loss: 0.4086 - learning_rate: 0.0010\n",
      "Epoch 17/20\n",
      "\u001b[1m1024/1024\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m93s\u001b[0m 88ms/step - accuracy: 0.7770 - loss: 0.6044 - val_accuracy: 0.8036 - val_loss: 0.5076 - learning_rate: 0.0010\n",
      "Epoch 18/20\n",
      "\u001b[1m1024/1024\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m56s\u001b[0m 54ms/step - accuracy: 0.7790 - loss: 0.5893 - val_accuracy: 0.8462 - val_loss: 0.4693 - learning_rate: 0.0010\n",
      "Epoch 19/20\n",
      "\u001b[1m1024/1024\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m58s\u001b[0m 56ms/step - accuracy: 0.7799 - loss: 0.5971 - val_accuracy: 0.8259 - val_loss: 0.4837 - learning_rate: 0.0010\n",
      "Epoch 20/20\n",
      "\u001b[1m1024/1024\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m56s\u001b[0m 53ms/step - accuracy: 0.7846 - loss: 0.5893 - val_accuracy: 0.8077 - val_loss: 0.5353 - 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: 0.7842\n",
      "Final Validation Accuracy: 0.8077\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": []
  }
 ],
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  "language_info": {
   "codemirror_mode": {
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   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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