Merge pull request #20 from deepanshubaghel/main

Enhancing Terrain-v3 as Terrain-v4 for Performance Optimization

.ipynb_checkpoints/Terrain_V3-checkpoint.ipynb

@@ -0,0 +1,351 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Importing necessary Libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "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": null,
+   "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": null,
+   "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"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define the CNN model architecture with residual connections\n",
+    "def create_model():\n",
+    "    model = tf.keras.Sequential([\n",
+    "        tf.keras.layers.Input(shape=(64, 64, 3)),\n",
+    "        tf.keras.layers.Conv2D(32, kernel_size=3, activation='relu'),\n",
+    "        tf.keras.layers.MaxPooling2D(pool_size=2, strides=2),\n",
+    "        tf.keras.layers.BatchNormalization(),\n",
+    "\n",
+    "        tf.keras.layers.Conv2D(64, kernel_size=3, activation='relu'),\n",
+    "        tf.keras.layers.MaxPooling2D(pool_size=2, strides=2),\n",
+    "        tf.keras.layers.BatchNormalization(),\n",
+    "\n",
+    "        tf.keras.layers.Conv2D(128, kernel_size=3, activation='relu'),\n",
+    "        tf.keras.layers.MaxPooling2D(pool_size=2, strides=2),\n",
+    "        tf.keras.layers.BatchNormalization(),\n",
+    "\n",
+    "        tf.keras.layers.Flatten(),\n",
+    "\n",
+    "        tf.keras.layers.Dense(1024, activation='relu'),\n",
+    "        tf.keras.layers.Dropout(0.2),\n",
+    "        tf.keras.layers.BatchNormalization(),\n",
+    "\n",
+    "        tf.keras.layers.Dense(1024, activation='relu'),\n",
+    "        tf.keras.layers.Dropout(0.2),\n",
+    "        tf.keras.layers.BatchNormalization(),\n",
+    "\n",
+    "        tf.keras.layers.Dense(256, activation='relu'),\n",
+    "        tf.keras.layers.Dropout(0.2),\n",
+    "        tf.keras.layers.BatchNormalization(),\n",
+    "\n",
+    "        tf.keras.layers.Dense(5, activation='softmax')\n",
+    "    ])\n",
+    "    return model\n",
+    "\n",
+    "model = create_model()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Compiling the CNN model\n",
+    "The model is compiled using the Adam optimizer, with categorical crossentropy as the loss function, and accuracy as the evaluation metric."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "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": null,
+   "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": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Update the directory paths to point to your local folder\n",
+    "training_set = train_datagen.flow_from_directory(\n",
+    "    r'C:\\Users\\draka\\Downloads\\Terrain_Images\\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'C:\\Users\\draka\\Downloads\\Terrain_Images\\Testing Data',\n",
+    "    target_size=(64, 64),\n",
+    "    batch_size=64,\n",
+    "    class_mode='categorical'\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "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)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "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": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Train the model\n",
+    "modelZ = model.fit(\n",
+    "    training_set,\n",
+    "    steps_per_epoch=2000,\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": null,
+   "metadata": {},
+   "outputs": [],
+   "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": "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": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# model.save('modelZ.h5')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "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": null,
+   "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
+}