"
+ ]
+ },
+ "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
+}
diff --git a/.ipynb_checkpoints/Terrain_V6 Optimizer RMSpop-checkpoint.ipynb b/.ipynb_checkpoints/Terrain_V6 Optimizer RMSpop-checkpoint.ipynb
new file mode 100644
index 00000000..23e0267c
--- /dev/null
+++ b/.ipynb_checkpoints/Terrain_V6 Optimizer RMSpop-checkpoint.ipynb
@@ -0,0 +1,594 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Importing necessary Libraries"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Install necessary packages and import libraries\n",
+ "# The first few lines ensure that TensorFlow, Scipy, and Matplotlib are installed.\n",
+ "# pip install tensorflow\n",
+ "# pip install scipy\n",
+ "# pip install matplotlib\n",
+ "import tensorflow as tf\n",
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Inline plotting with Matplotlib"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# %matplotlib inline\n",
+ "# this command ensures that plots are displayed directly in the notebook after being generated."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Building a Convolutional Neural Network (CNN) model for terrain classification\n",
+ "# Importing layers from Keras for building the model architecture\n",
+ "from keras.preprocessing import image\n",
+ "from keras.models import Sequential\n",
+ "from keras.layers import Conv2D\n",
+ "from keras.layers import MaxPooling2D\n",
+ "from keras.layers import Flatten\n",
+ "from keras.layers import Dense\n",
+ "from keras.layers import Dropout\n",
+ "from keras.layers import BatchNormalization\n",
+ "from keras.models import load_model\n",
+ "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
+ "from tensorflow.keras.callbacks import LearningRateScheduler, ReduceLROnPlateau\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Defining the CNN model\n",
+ "The following code defines a CNN model using TensorFlow's Keras API. The model is designed for terrain classification with a series of convolutional layers, pooling layers, and fully connected layers.\n",
+ "\n",
+ "* **Convolutional Layers:** These layers apply filters to the input image, helping to detect various features (like edges, textures, etc.).\n",
+ "* **Max Pooling:** This operation reduces the spatial dimensions (width and height) of the output from the convolutional layers, which helps in reducing computational complexity and controlling overfitting.\n",
+ "* **Batch Normalization:** This technique normalizes the outputs of a previous activation layer, which helps stabilize learning and can accelerate training.\n",
+ "* **Dropout Layers:** These layers randomly set a fraction of the input units to zero during training to help prevent overfitting.\n",
+ "* **Softmax Layer:** This is used in the output layer for multi-class classification problems, providing probabilities for each class.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Define the CNN model architecture with residual connections\n",
+ "def create_model():\n",
+ " model = tf.keras.Sequential([\n",
+ " tf.keras.layers.Input(shape=(64, 64, 3)),\n",
+ " tf.keras.layers.Conv2D(32, kernel_size=3, activation='relu'),\n",
+ " tf.keras.layers.MaxPooling2D(pool_size=2, strides=2),\n",
+ " tf.keras.layers.BatchNormalization(),\n",
+ "\n",
+ " tf.keras.layers.Conv2D(64, kernel_size=3, activation='relu'),\n",
+ " tf.keras.layers.MaxPooling2D(pool_size=2, strides=2),\n",
+ " tf.keras.layers.BatchNormalization(),\n",
+ "\n",
+ " tf.keras.layers.Conv2D(128, kernel_size=3, activation='relu'),\n",
+ " tf.keras.layers.MaxPooling2D(pool_size=2, strides=2),\n",
+ " tf.keras.layers.BatchNormalization(),\n",
+ "\n",
+ " tf.keras.layers.Flatten(),\n",
+ "\n",
+ " tf.keras.layers.Dense(1024, activation='relu'),\n",
+ " tf.keras.layers.Dropout(0.2),\n",
+ " tf.keras.layers.BatchNormalization(),\n",
+ "\n",
+ " tf.keras.layers.Dense(1024, activation='relu'),\n",
+ " tf.keras.layers.Dropout(0.2),\n",
+ " tf.keras.layers.BatchNormalization(),\n",
+ "\n",
+ " tf.keras.layers.Dense(256, activation='relu'),\n",
+ " tf.keras.layers.Dropout(0.2),\n",
+ " tf.keras.layers.BatchNormalization(),\n",
+ "\n",
+ " tf.keras.layers.Dense(5, activation='softmax')\n",
+ " ])\n",
+ " return model\n",
+ "\n",
+ "model = create_model()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Compiling the CNN model\n",
+ "The model is compiled using the Adam optimizer, with categorical crossentropy as the loss function, and accuracy as the evaluation metric."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Compile the model\n",
+ "# Categorical crossentropy is used since the labels are one-hot encoded.\n",
+ "# Adam optimizer is used for its adaptive learning rate.\n",
+ "\n",
+ "model.compile(optimizer=tf.keras.optimizers.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": [
+ {
+ "data": {
+ "text/html": [
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+ "
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+ "│ (BatchNormalization) │ │ │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ dense_3 (Dense) │ (None, 5) │ 1,285 │\n",
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+ "┃\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",
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+ "│ conv2d (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m62\u001b[0m, \u001b[38;5;34m62\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m896\u001b[0m │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ max_pooling2d (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m31\u001b[0m, \u001b[38;5;34m31\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ batch_normalization │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m31\u001b[0m, \u001b[38;5;34m31\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m128\u001b[0m │\n",
+ "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ conv2d_1 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m29\u001b[0m, \u001b[38;5;34m29\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m18,496\u001b[0m │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ max_pooling2d_1 (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ batch_normalization_1 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m256\u001b[0m │\n",
+ "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ conv2d_2 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m73,856\u001b[0m │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ max_pooling2d_2 (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ batch_normalization_2 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m512\u001b[0m │\n",
+ "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ flatten (\u001b[38;5;33mFlatten\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4608\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ dense (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1024\u001b[0m) │ \u001b[38;5;34m4,719,616\u001b[0m │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ dropout (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1024\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ batch_normalization_3 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1024\u001b[0m) │ \u001b[38;5;34m4,096\u001b[0m │\n",
+ "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ dense_1 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1024\u001b[0m) │ \u001b[38;5;34m1,049,600\u001b[0m │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ dropout_1 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1024\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ batch_normalization_4 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1024\u001b[0m) │ \u001b[38;5;34m4,096\u001b[0m │\n",
+ "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ dense_2 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m262,400\u001b[0m │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ dropout_2 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ batch_normalization_5 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m1,024\u001b[0m │\n",
+ "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ dense_3 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m) │ \u001b[38;5;34m1,285\u001b[0m │\n",
+ "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ " Total params: 6,136,261 (23.41 MB)\n",
+ "
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+ ],
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+ "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m6,136,261\u001b[0m (23.41 MB)\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ " Trainable params: 6,131,205 (23.39 MB)\n",
+ "
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+ ],
+ "text/plain": [
+ "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m6,131,205\u001b[0m (23.39 MB)\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ " Non-trainable params: 5,056 (19.75 KB)\n",
+ "
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+ ],
+ "text/plain": [
+ "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m5,056\u001b[0m (19.75 KB)\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "model.summary()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Training the CNN Model\n",
+ "The model is trained using the training and validation data. The training process is monitored, and a learning rate scheduler is used to adjust the learning rate dynamically during training."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "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[1m64/64\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m23s\u001b[0m 291ms/step - accuracy: 0.4302 - loss: 1.8535 - val_accuracy: 0.5156 - val_loss: 3.1140 - learning_rate: 0.0100\n",
+ "Epoch 2/20\n",
+ "\u001b[1m64/64\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m17s\u001b[0m 273ms/step - accuracy: 0.5881 - loss: 1.0641 - val_accuracy: 0.5000 - val_loss: 1.2787 - learning_rate: 0.0100\n",
+ "Epoch 3/20\n"
+ ]
+ },
+ {
+ "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[1m64/64\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 119ms/step - accuracy: 0.5983 - loss: 1.0520 - val_accuracy: 0.4688 - val_loss: 1.2819 - learning_rate: 0.0100\n",
+ "Epoch 4/20\n",
+ "\u001b[1m64/64\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 271ms/step - accuracy: 0.6494 - loss: 0.9601 - val_accuracy: 0.5192 - val_loss: 1.8879 - learning_rate: 0.0100\n",
+ "Epoch 5/20\n",
+ "\u001b[1m38/64\u001b[0m \u001b[32m━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━\u001b[0m \u001b[1m6s\u001b[0m 264ms/step - accuracy: 0.6408 - loss: 0.9553"
+ ]
+ }
+ ],
+ "source": [
+ "# Train the model\n",
+ "modelZ = model.fit(\n",
+ " training_set,\n",
+ " steps_per_epoch=64,\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": [
+ "# 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": 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": "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": 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
+}
diff --git a/.ipynb_checkpoints/Terrain_V7 Optimizer Adagrad-checkpoint.ipynb b/.ipynb_checkpoints/Terrain_V7 Optimizer Adagrad-checkpoint.ipynb
new file mode 100644
index 00000000..23e0267c
--- /dev/null
+++ b/.ipynb_checkpoints/Terrain_V7 Optimizer Adagrad-checkpoint.ipynb
@@ -0,0 +1,594 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Importing necessary Libraries"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Install necessary packages and import libraries\n",
+ "# The first few lines ensure that TensorFlow, Scipy, and Matplotlib are installed.\n",
+ "# pip install tensorflow\n",
+ "# pip install scipy\n",
+ "# pip install matplotlib\n",
+ "import tensorflow as tf\n",
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Inline plotting with Matplotlib"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# %matplotlib inline\n",
+ "# this command ensures that plots are displayed directly in the notebook after being generated."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Building a Convolutional Neural Network (CNN) model for terrain classification\n",
+ "# Importing layers from Keras for building the model architecture\n",
+ "from keras.preprocessing import image\n",
+ "from keras.models import Sequential\n",
+ "from keras.layers import Conv2D\n",
+ "from keras.layers import MaxPooling2D\n",
+ "from keras.layers import Flatten\n",
+ "from keras.layers import Dense\n",
+ "from keras.layers import Dropout\n",
+ "from keras.layers import BatchNormalization\n",
+ "from keras.models import load_model\n",
+ "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
+ "from tensorflow.keras.callbacks import LearningRateScheduler, ReduceLROnPlateau\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Defining the CNN model\n",
+ "The following code defines a CNN model using TensorFlow's Keras API. The model is designed for terrain classification with a series of convolutional layers, pooling layers, and fully connected layers.\n",
+ "\n",
+ "* **Convolutional Layers:** These layers apply filters to the input image, helping to detect various features (like edges, textures, etc.).\n",
+ "* **Max Pooling:** This operation reduces the spatial dimensions (width and height) of the output from the convolutional layers, which helps in reducing computational complexity and controlling overfitting.\n",
+ "* **Batch Normalization:** This technique normalizes the outputs of a previous activation layer, which helps stabilize learning and can accelerate training.\n",
+ "* **Dropout Layers:** These layers randomly set a fraction of the input units to zero during training to help prevent overfitting.\n",
+ "* **Softmax Layer:** This is used in the output layer for multi-class classification problems, providing probabilities for each class.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Define the CNN model architecture with residual connections\n",
+ "def create_model():\n",
+ " model = tf.keras.Sequential([\n",
+ " tf.keras.layers.Input(shape=(64, 64, 3)),\n",
+ " tf.keras.layers.Conv2D(32, kernel_size=3, activation='relu'),\n",
+ " tf.keras.layers.MaxPooling2D(pool_size=2, strides=2),\n",
+ " tf.keras.layers.BatchNormalization(),\n",
+ "\n",
+ " tf.keras.layers.Conv2D(64, kernel_size=3, activation='relu'),\n",
+ " tf.keras.layers.MaxPooling2D(pool_size=2, strides=2),\n",
+ " tf.keras.layers.BatchNormalization(),\n",
+ "\n",
+ " tf.keras.layers.Conv2D(128, kernel_size=3, activation='relu'),\n",
+ " tf.keras.layers.MaxPooling2D(pool_size=2, strides=2),\n",
+ " tf.keras.layers.BatchNormalization(),\n",
+ "\n",
+ " tf.keras.layers.Flatten(),\n",
+ "\n",
+ " tf.keras.layers.Dense(1024, activation='relu'),\n",
+ " tf.keras.layers.Dropout(0.2),\n",
+ " tf.keras.layers.BatchNormalization(),\n",
+ "\n",
+ " tf.keras.layers.Dense(1024, activation='relu'),\n",
+ " tf.keras.layers.Dropout(0.2),\n",
+ " tf.keras.layers.BatchNormalization(),\n",
+ "\n",
+ " tf.keras.layers.Dense(256, activation='relu'),\n",
+ " tf.keras.layers.Dropout(0.2),\n",
+ " tf.keras.layers.BatchNormalization(),\n",
+ "\n",
+ " tf.keras.layers.Dense(5, activation='softmax')\n",
+ " ])\n",
+ " return model\n",
+ "\n",
+ "model = create_model()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Compiling the CNN model\n",
+ "The model is compiled using the Adam optimizer, with categorical crossentropy as the loss function, and accuracy as the evaluation metric."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Compile the model\n",
+ "# Categorical crossentropy is used since the labels are one-hot encoded.\n",
+ "# Adam optimizer is used for its adaptive learning rate.\n",
+ "\n",
+ "model.compile(optimizer=tf.keras.optimizers.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": [
+ {
+ "data": {
+ "text/html": [
+ "Model: \"sequential\"\n",
+ "
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+ "│ conv2d_1 (Conv2D) │ (None, 29, 29, 64) │ 18,496 │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ max_pooling2d_1 (MaxPooling2D) │ (None, 14, 14, 64) │ 0 │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ batch_normalization_1 │ (None, 14, 14, 64) │ 256 │\n",
+ "│ (BatchNormalization) │ │ │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ conv2d_2 (Conv2D) │ (None, 12, 12, 128) │ 73,856 │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ max_pooling2d_2 (MaxPooling2D) │ (None, 6, 6, 128) │ 0 │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ batch_normalization_2 │ (None, 6, 6, 128) │ 512 │\n",
+ "│ (BatchNormalization) │ │ │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ flatten (Flatten) │ (None, 4608) │ 0 │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ dense (Dense) │ (None, 1024) │ 4,719,616 │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ dropout (Dropout) │ (None, 1024) │ 0 │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ batch_normalization_3 │ (None, 1024) │ 4,096 │\n",
+ "│ (BatchNormalization) │ │ │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ dense_1 (Dense) │ (None, 1024) │ 1,049,600 │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ dropout_1 (Dropout) │ (None, 1024) │ 0 │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ batch_normalization_4 │ (None, 1024) │ 4,096 │\n",
+ "│ (BatchNormalization) │ │ │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ dense_2 (Dense) │ (None, 256) │ 262,400 │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ dropout_2 (Dropout) │ (None, 256) │ 0 │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ batch_normalization_5 │ (None, 256) │ 1,024 │\n",
+ "│ (BatchNormalization) │ │ │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ dense_3 (Dense) │ (None, 5) │ 1,285 │\n",
+ "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
+ "┃\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
+ "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
+ "│ conv2d (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m62\u001b[0m, \u001b[38;5;34m62\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m896\u001b[0m │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ max_pooling2d (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m31\u001b[0m, \u001b[38;5;34m31\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ batch_normalization │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m31\u001b[0m, \u001b[38;5;34m31\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m128\u001b[0m │\n",
+ "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ conv2d_1 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m29\u001b[0m, \u001b[38;5;34m29\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m18,496\u001b[0m │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ max_pooling2d_1 (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ batch_normalization_1 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m256\u001b[0m │\n",
+ "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ conv2d_2 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m73,856\u001b[0m │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ max_pooling2d_2 (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ batch_normalization_2 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m512\u001b[0m │\n",
+ "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ flatten (\u001b[38;5;33mFlatten\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4608\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ dense (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1024\u001b[0m) │ \u001b[38;5;34m4,719,616\u001b[0m │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ dropout (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1024\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ batch_normalization_3 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1024\u001b[0m) │ \u001b[38;5;34m4,096\u001b[0m │\n",
+ "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ dense_1 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1024\u001b[0m) │ \u001b[38;5;34m1,049,600\u001b[0m │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ dropout_1 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1024\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ batch_normalization_4 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1024\u001b[0m) │ \u001b[38;5;34m4,096\u001b[0m │\n",
+ "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ dense_2 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m262,400\u001b[0m │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ dropout_2 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ batch_normalization_5 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m1,024\u001b[0m │\n",
+ "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ dense_3 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m) │ \u001b[38;5;34m1,285\u001b[0m │\n",
+ "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ " Total params: 6,136,261 (23.41 MB)\n",
+ "
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+ ],
+ "text/plain": [
+ "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m6,136,261\u001b[0m (23.41 MB)\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ " Trainable params: 6,131,205 (23.39 MB)\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m6,131,205\u001b[0m (23.39 MB)\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ " Non-trainable params: 5,056 (19.75 KB)\n",
+ "
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+ ],
+ "text/plain": [
+ "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m5,056\u001b[0m (19.75 KB)\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "model.summary()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Training the CNN Model\n",
+ "The model is trained using the training and validation data. The training process is monitored, and a learning rate scheduler is used to adjust the learning rate dynamically during training."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "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[1m64/64\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m23s\u001b[0m 291ms/step - accuracy: 0.4302 - loss: 1.8535 - val_accuracy: 0.5156 - val_loss: 3.1140 - learning_rate: 0.0100\n",
+ "Epoch 2/20\n",
+ "\u001b[1m64/64\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m17s\u001b[0m 273ms/step - accuracy: 0.5881 - loss: 1.0641 - val_accuracy: 0.5000 - val_loss: 1.2787 - learning_rate: 0.0100\n",
+ "Epoch 3/20\n"
+ ]
+ },
+ {
+ "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[1m64/64\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 119ms/step - accuracy: 0.5983 - loss: 1.0520 - val_accuracy: 0.4688 - val_loss: 1.2819 - learning_rate: 0.0100\n",
+ "Epoch 4/20\n",
+ "\u001b[1m64/64\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 271ms/step - accuracy: 0.6494 - loss: 0.9601 - val_accuracy: 0.5192 - val_loss: 1.8879 - learning_rate: 0.0100\n",
+ "Epoch 5/20\n",
+ "\u001b[1m38/64\u001b[0m \u001b[32m━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━\u001b[0m \u001b[1m6s\u001b[0m 264ms/step - accuracy: 0.6408 - loss: 0.9553"
+ ]
+ }
+ ],
+ "source": [
+ "# Train the model\n",
+ "modelZ = model.fit(\n",
+ " training_set,\n",
+ " steps_per_epoch=64,\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": [
+ "# 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": 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": "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": 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
+}
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--- /dev/null
+++ b/Readme1.md
@@ -0,0 +1,101 @@
+### **Terrain Classification Using CNN**
+This project implements a Convolutional Neural Network (CNN) for terrain classification using various optimizers. The aim is to explore the impact of different optimization strategies on model performance. The dataset comprises images representing five different terrain types, and the model was trained using Keras and TensorFlow.
+
+
+### **Project Overview**
+The primary goal of this project is to classify images of terrain into one of five categories. The CNN model leverages multiple convolutional, pooling, and fully connected layers to extract features from the images and make predictions.
+
+### **Model Architecture**
+The architecture used for all versions of the model includes:
+
+- **Input Layer:** 64x64 RGB images.
+- **Convolutional Layers:** Three convolutional layers with ReLU activation, consisting of 32, 64, and 128 filters, respectively.
+- **Pooling Layers:** MaxPooling layers after each convolution to reduce spatial dimensions.
+- **Fully Connected Layers:** Two fully connected layers with 1024 units each, followed by a final dense layer with 5 output units for classification.
+- **Batch Normalization:** Applied after each convolution and dense layer to stabilize learning.
+- **Dropout:** Introduced between fully connected layers to prevent overfitting.
+
+
+### **Optimizers Tested**
+The following optimizers were used to compile the models:
+
+
+**RMSprop (Terrain_V6)**
+
+**Parameters:** Learning Rate = 0.001
+**Results:**
+
+- Final Training Accuracy: 0.7767
+- Final Validation Accuracy: 0.8846
+
+
+
+
+**RMSprop (Terrain_V6-Copy 1)**
+
+**Parameters:** Learning Rate = 0.001
+**Results:**
+
+- Final Training Accuracy: 0.7842
+- Final Validation Accuracy: 0.8077
+
+
+
+
+**ADAM (Terrain_V4-Copy 1)**
+
+**Results:**
+
+- Final Training Accuracy: 0.7643
+- Final Validation Accuracy: 0.8269
+
+
+
+
+
+
+**SGD with Momentum (Terrain_V5)**
+
+**Parameters:** Learning Rate = 0.01, Momentum = 0.9
+**Results:**
+
+- Final Training Accuracy: 0.7583
+- Final Validation Accuracy: 0.8077
+
+
+
+
+
+**Adagrad (Terrain_V7)**
+
+**Parameters:** Learning Rate = 0.01
+**Results:**
+
+- Training Accuracy: 0.7439
+- Validation Accuracy: 0.7692
+
+
+
+
+### **Findings and Unique Insights**
+**1. SGD with Momentum (Terrain_V5)**
+**Observation:**
+Steady but slower improvement in accuracy. The model showed a tendency to plateau after a certain number of epochs, indicating a need for better learning rate tuning or decay scheduling.
+
+**2. RMSprop (Terrain_V6)**
+**Observation:**
+Achieved the best validation accuracy among the optimizers. RMSprop adapts learning rates effectively, particularly useful in non-stationary data scenarios like high-variance image datasets. Its quick convergence enhances both training and validation accuracy, though it may risk overfitting.
+
+**3. Adagrad (Terrain_V7)**
+**Observation:**
+Moderate performance with an early convergence tendency due to its adaptive learning rate mechanism. While beneficial for sparse data, this optimizer struggled in this task, indicating a plateau in performance.
+
+### Key Learnings
+
+**1. Optimizer Choice Matters:** The optimizer significantly impacts training speed and final accuracy. RMSprop yielded the best results for this dataset due to its adaptive learning capabilities.
+**2. Effect of Batch Normalization:** It stabilizes and accelerates training, especially in deeper layers, preventing gradient explosion or vanishing.
+**3. Dropout and Overfitting:** Introducing Dropout layers at 20% effectively reduced overfitting. Without it, training accuracy was disproportionately higher than validation accuracy.
+**4. Learning Rate Scheduling:** Dynamic scheduling with optimizers like SGD and RMSprop enhanced performance, while Adagrad's built-in adaptation sufficed for convergence.
+
+**Conclusion**
+This project underscores the importance of selecting the appropriate optimizer based on the dataset and problem type. RMSprop emerged as the best performer for terrain classification, whereas Adagrad exhibited limited effectiveness. SGD with Momentum may improve with further training epochs or refined learning rate scheduling.
diff --git a/Terrain_V3.ipynb b/Terrain_V3.ipynb
index 291ff549..9cb19451 100644
--- a/Terrain_V3.ipynb
+++ b/Terrain_V3.ipynb
@@ -1,597 +1,598 @@
{
- "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": 15,
- "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": 16,
- "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": 17,
- "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(32, 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(64, 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(128, kernel_size=3, activation='relu'),\n",
- " tf.keras.layers.MaxPool2D(pool_size=2, strides=2),\n",
- " tf.keras.layers.BatchNormalization(),\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(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"
- ]
- },
- {
- "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": 18,
- "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",
- "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": 19,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Data augmentation and preprocessing for training and validation datasets\n",
- "# The ImageDataGenerator applies various transformations to augment the training data\n",
- "train_datagen = ImageDataGenerator(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",
- "# For the validation set, we only rescale the pixel values\n",
- "test_datagen = ImageDataGenerator(rescale=1./255)"
- ]
- },
+ "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": 15,
+ "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": 16,
+ "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": 17,
+ "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(32, 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(64, 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(128, kernel_size=3, activation='relu'),\n",
+ " tf.keras.layers.MaxPool2D(pool_size=2, strides=2),\n",
+ " tf.keras.layers.BatchNormalization(),\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(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"
+ ]
+ },
+ {
+ "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": 18,
+ "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",
+ "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": 19,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Data augmentation and preprocessing for training and validation datasets\n",
+ "# The ImageDataGenerator applies various transformations to augment the training data\n",
+ "train_datagen = ImageDataGenerator(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",
+ "# For the validation set, we only rescale the pixel values\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": 20,
+ "metadata": {},
+ "outputs": [
{
- "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"
- ]
- },
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Found 10000 images belonging to 5 classes.\n",
+ "Found 500 images belonging to 5 classes.\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Update the directory paths to point to your local Downloads folder\n",
+ "training_set = train_datagen.flow_from_directory(r'C:\\Users\\draka\\Downloads\\Terrain_Images\\Training Data',\n",
+ " target_size=(64, 64),\n",
+ " batch_size=64,\n",
+ " class_mode='categorical')\n",
+ "\n",
+ "test_set = test_datagen.flow_from_directory(r'C:\\Users\\draka\\Downloads\\Terrain_Images\\Testing Data',\n",
+ " target_size=(64, 64),\n",
+ " batch_size=64,\n",
+ " class_mode='categorical')\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Define a learning rate scheduler and reduce the learning rate when the accuracy plateaus\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": 22,
+ "metadata": {},
+ "outputs": [
{
- "cell_type": "code",
- "execution_count": 20,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Found 10000 images belonging to 5 classes.\n",
- "Found 500 images belonging to 5 classes.\n"
- ]
- }
+ "data": {
+ "text/html": [
+ "Model: \"sequential_1\"\n",
+ "
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],
- "source": [
- "# Update the directory paths to point to your local Downloads folder\n",
- "training_set = train_datagen.flow_from_directory(r'C:\\Users\\draka\\Downloads\\Terrain_Images\\Training Data',\n",
- " target_size=(64, 64),\n",
- " batch_size=64,\n",
- " class_mode='categorical')\n",
- "\n",
- "test_set = test_datagen.flow_from_directory(r'C:\\Users\\draka\\Downloads\\Terrain_Images\\Testing Data',\n",
- " target_size=(64, 64),\n",
- " batch_size=64,\n",
- " class_mode='categorical')\n"
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- "cell_type": "code",
- "execution_count": 21,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Define a learning rate scheduler and reduce the learning rate when the accuracy plateaus\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",
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- "│ dense_5 (Dense) │ (None, 256) │ 262,400 │\n",
- "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
- "│ dropout_2 (Dropout) │ (None, 256) │ 0 │\n",
- "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
- "│ batch_normalization_5 │ (None, 256) │ 1,024 │\n",
- "│ (BatchNormalization) │ │ │\n",
- "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
- "│ dense_6 (Dense) │ (None, 5) │ 1,285 │\n",
- "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
- "
\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_2 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m62\u001b[0m, \u001b[38;5;34m62\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m896\u001b[0m │\n",
- "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
- "│ max_pooling2d_2 (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m31\u001b[0m, \u001b[38;5;34m31\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
- "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
- "│ batch_normalization │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m31\u001b[0m, \u001b[38;5;34m31\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m128\u001b[0m │\n",
- "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n",
- "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
- "│ conv2d_3 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m29\u001b[0m, \u001b[38;5;34m29\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m18,496\u001b[0m │\n",
- "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
- "│ max_pooling2d_3 (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
- "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
- "│ batch_normalization_1 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m256\u001b[0m │\n",
- "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n",
- "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
- "│ conv2d_4 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m73,856\u001b[0m │\n",
- "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
- "│ max_pooling2d_4 (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
- "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
- "│ batch_normalization_2 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m512\u001b[0m │\n",
- "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n",
- "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
- "│ flatten_1 (\u001b[38;5;33mFlatten\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4608\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
- "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
- "│ dense_3 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1024\u001b[0m) │ \u001b[38;5;34m4,719,616\u001b[0m │\n",
- "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
- "│ dropout (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1024\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
- "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
- "│ batch_normalization_3 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1024\u001b[0m) │ \u001b[38;5;34m4,096\u001b[0m │\n",
- "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n",
- "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
- "│ dense_4 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1024\u001b[0m) │ \u001b[38;5;34m1,049,600\u001b[0m │\n",
- "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
- "│ dropout_1 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1024\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
- "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
- "│ batch_normalization_4 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1024\u001b[0m) │ \u001b[38;5;34m4,096\u001b[0m │\n",
- "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n",
- "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
- "│ dense_5 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m262,400\u001b[0m │\n",
- "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
- "│ dropout_2 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
- "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
- "│ batch_normalization_5 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m1,024\u001b[0m │\n",
- "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n",
- "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
- "│ dense_6 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m) │ \u001b[38;5;34m1,285\u001b[0m │\n",
- "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
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- " Total params: 6,136,261 (23.41 MB)\n",
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- },
- "metadata": {},
- "output_type": "display_data"
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- "data": {
- "text/html": [
- " Trainable params: 6,131,205 (23.39 MB)\n",
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- },
- "metadata": {},
- "output_type": "display_data"
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- "data": {
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- " Non-trainable params: 5,056 (19.75 KB)\n",
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- "text/plain": [
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- ]
- },
- "metadata": {},
- "output_type": "display_data"
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+ "data": {
+ "text/html": [
+ "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+ "┃ Layer (type) ┃ Output Shape ┃ Param # ┃\n",
+ "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+ "│ conv2d_2 (Conv2D) │ (None, 62, 62, 32) │ 896 │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ max_pooling2d_2 (MaxPooling2D) │ (None, 31, 31, 32) │ 0 │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ batch_normalization │ (None, 31, 31, 32) │ 128 │\n",
+ "│ (BatchNormalization) │ │ │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ conv2d_3 (Conv2D) │ (None, 29, 29, 64) │ 18,496 │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ max_pooling2d_3 (MaxPooling2D) │ (None, 14, 14, 64) │ 0 │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ batch_normalization_1 │ (None, 14, 14, 64) │ 256 │\n",
+ "│ (BatchNormalization) │ │ │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ conv2d_4 (Conv2D) │ (None, 12, 12, 128) │ 73,856 │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ max_pooling2d_4 (MaxPooling2D) │ (None, 6, 6, 128) │ 0 │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ batch_normalization_2 │ (None, 6, 6, 128) │ 512 │\n",
+ "│ (BatchNormalization) │ │ │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ flatten_1 (Flatten) │ (None, 4608) │ 0 │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ dense_3 (Dense) │ (None, 1024) │ 4,719,616 │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ dropout (Dropout) │ (None, 1024) │ 0 │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ batch_normalization_3 │ (None, 1024) │ 4,096 │\n",
+ "│ (BatchNormalization) │ │ │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ dense_4 (Dense) │ (None, 1024) │ 1,049,600 │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ dropout_1 (Dropout) │ (None, 1024) │ 0 │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ batch_normalization_4 │ (None, 1024) │ 4,096 │\n",
+ "│ (BatchNormalization) │ │ │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ dense_5 (Dense) │ (None, 256) │ 262,400 │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ dropout_2 (Dropout) │ (None, 256) │ 0 │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ batch_normalization_5 │ (None, 256) │ 1,024 │\n",
+ "│ (BatchNormalization) │ │ │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ dense_6 (Dense) │ (None, 5) │ 1,285 │\n",
+ "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
+ "
\n"
],
- "source": [
- "model.summary()"
+ "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_2 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m62\u001b[0m, \u001b[38;5;34m62\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m896\u001b[0m │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ max_pooling2d_2 (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m31\u001b[0m, \u001b[38;5;34m31\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ batch_normalization │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m31\u001b[0m, \u001b[38;5;34m31\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m128\u001b[0m │\n",
+ "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ conv2d_3 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m29\u001b[0m, \u001b[38;5;34m29\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m18,496\u001b[0m │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ max_pooling2d_3 (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ batch_normalization_1 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m256\u001b[0m │\n",
+ "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ conv2d_4 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m73,856\u001b[0m │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ max_pooling2d_4 (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ batch_normalization_2 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m512\u001b[0m │\n",
+ "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ flatten_1 (\u001b[38;5;33mFlatten\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4608\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ dense_3 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1024\u001b[0m) │ \u001b[38;5;34m4,719,616\u001b[0m │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ dropout (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1024\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ batch_normalization_3 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1024\u001b[0m) │ \u001b[38;5;34m4,096\u001b[0m │\n",
+ "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ dense_4 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1024\u001b[0m) │ \u001b[38;5;34m1,049,600\u001b[0m │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ dropout_1 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1024\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ batch_normalization_4 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1024\u001b[0m) │ \u001b[38;5;34m4,096\u001b[0m │\n",
+ "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ dense_5 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m262,400\u001b[0m │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ dropout_2 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ batch_normalization_5 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m1,024\u001b[0m │\n",
+ "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ dense_6 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m) │ \u001b[38;5;34m1,285\u001b[0m │\n",
+ "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
]
+ },
+ "metadata": {},
+ "output_type": "display_data"
},
{
- "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": 24,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Epoch 1/20\n",
- "\u001b[1m2000/2000\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m31s\u001b[0m 15ms/step - accuracy: 0.7851 - loss: 0.5746 - val_accuracy: 0.8058 - val_loss: 0.5299\n",
- "Epoch 2/20\n",
- "\u001b[1m2000/2000\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m30s\u001b[0m 15ms/step - accuracy: 0.7895 - loss: 0.5640 - val_accuracy: 0.7885 - val_loss: 0.5749\n",
- "Epoch 3/20\n",
- "\u001b[1m2000/2000\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m31s\u001b[0m 15ms/step - accuracy: 0.7849 - loss: 0.5761 - val_accuracy: 0.7656 - val_loss: 0.6844\n",
- "Epoch 4/20\n",
- "\u001b[1m2000/2000\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m30s\u001b[0m 15ms/step - accuracy: 0.7969 - loss: 0.5446 - val_accuracy: 0.7692 - val_loss: 0.4869\n",
- "Epoch 5/20\n",
- "\u001b[1m2000/2000\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m31s\u001b[0m 15ms/step - accuracy: 0.7997 - loss: 0.5350 - val_accuracy: 0.8393 - val_loss: 0.4124\n",
- "Epoch 6/20\n",
- "\u001b[1m2000/2000\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m30s\u001b[0m 15ms/step - accuracy: 0.7984 - loss: 0.5400 - val_accuracy: 0.7308 - val_loss: 0.8300\n",
- "Epoch 7/20\n",
- "\u001b[1m2000/2000\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m30s\u001b[0m 15ms/step - accuracy: 0.8022 - loss: 0.5343 - val_accuracy: 0.8348 - val_loss: 0.4604\n",
- "Epoch 8/20\n",
- "\u001b[1m2000/2000\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m30s\u001b[0m 15ms/step - accuracy: 0.7989 - loss: 0.5368 - val_accuracy: 0.7308 - val_loss: 0.6008\n",
- "Epoch 9/20\n",
- "\u001b[1m2000/2000\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m30s\u001b[0m 15ms/step - accuracy: 0.8116 - loss: 0.5150 - val_accuracy: 0.7790 - val_loss: 0.6313\n",
- "Epoch 10/20\n",
- "\u001b[1m2000/2000\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m30s\u001b[0m 15ms/step - accuracy: 0.8069 - loss: 0.5171 - val_accuracy: 0.7885 - val_loss: 0.4614\n",
- "Epoch 11/20\n",
- "\u001b[1m2000/2000\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m30s\u001b[0m 15ms/step - accuracy: 0.8067 - loss: 0.5171 - val_accuracy: 0.7879 - val_loss: 0.5566\n",
- "Epoch 12/20\n",
- "\u001b[1m2000/2000\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m30s\u001b[0m 15ms/step - accuracy: 0.8171 - loss: 0.4964 - val_accuracy: 0.8077 - val_loss: 0.5560\n",
- "Epoch 13/20\n",
- "\u001b[1m2000/2000\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m31s\u001b[0m 15ms/step - accuracy: 0.8144 - loss: 0.5001 - val_accuracy: 0.8080 - val_loss: 0.5437\n",
- "Epoch 14/20\n",
- "\u001b[1m2000/2000\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m30s\u001b[0m 15ms/step - accuracy: 0.8193 - loss: 0.4887 - val_accuracy: 0.8077 - val_loss: 0.4929\n",
- "Epoch 15/20\n",
- "\u001b[1m2000/2000\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m31s\u001b[0m 15ms/step - accuracy: 0.8198 - loss: 0.4781 - val_accuracy: 0.8170 - val_loss: 0.4683\n",
- "Epoch 16/20\n",
- "\u001b[1m2000/2000\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m30s\u001b[0m 15ms/step - accuracy: 0.8242 - loss: 0.4783 - val_accuracy: 0.7500 - val_loss: 0.7382\n",
- "Epoch 17/20\n",
- "\u001b[1m2000/2000\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m31s\u001b[0m 15ms/step - accuracy: 0.8233 - loss: 0.4869 - val_accuracy: 0.7835 - val_loss: 0.6065\n",
- "Epoch 18/20\n",
- "\u001b[1m2000/2000\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m30s\u001b[0m 15ms/step - accuracy: 0.8259 - loss: 0.4707 - val_accuracy: 0.8462 - val_loss: 0.4422\n",
- "Epoch 19/20\n",
- "\u001b[1m2000/2000\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m31s\u001b[0m 15ms/step - accuracy: 0.8265 - loss: 0.4711 - val_accuracy: 0.8036 - val_loss: 0.5277\n",
- "Epoch 20/20\n",
- "\u001b[1m2000/2000\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m30s\u001b[0m 15ms/step - accuracy: 0.8305 - loss: 0.4483 - val_accuracy: 0.8462 - val_loss: 0.4460\n"
- ]
- }
+ "data": {
+ "text/html": [
+ " Total params: 6,136,261 (23.41 MB)\n",
+ "
\n"
],
- "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",
- ")"
- ]
- },
- {
- "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."
+ "text/plain": [
+ "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m6,136,261\u001b[0m (23.41 MB)\n"
]
+ },
+ "metadata": {},
+ "output_type": "display_data"
},
{
- "cell_type": "code",
- "execution_count": 27,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- ""
- ]
- },
- "execution_count": 27,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
+ "data": {
+ "text/html": [
+ " Trainable params: 6,131,205 (23.39 MB)\n",
+ "
\n"
],
- "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"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Saving the Trained Model\n",
- "After training, the model is saved to a file for future use.\n"
+ "text/plain": [
+ "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m6,131,205\u001b[0m (23.39 MB)\n"
]
+ },
+ "metadata": {},
+ "output_type": "display_data"
},
{
- "cell_type": "code",
- "execution_count": 29,
- "metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
- ]
- }
+ "data": {
+ "text/html": [
+ " Non-trainable params: 5,056 (19.75 KB)\n",
+ "
\n"
],
- "source": [
- "model.save('modelZ.h5')"
+ "text/plain": [
+ "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m5,056\u001b[0m (19.75 KB)\n"
]
- },
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "model.summary()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Training the CNN Model\n",
+ "The model is trained using the training and validation data. The training process is monitored, and a learning rate scheduler is used to adjust the learning rate dynamically during training."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 1/20\n",
+ "\u001b[1m2000/2000\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m31s\u001b[0m 15ms/step - accuracy: 0.7851 - loss: 0.5746 - val_accuracy: 0.8058 - val_loss: 0.5299\n",
+ "Epoch 2/20\n",
+ "\u001b[1m2000/2000\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m30s\u001b[0m 15ms/step - accuracy: 0.7895 - loss: 0.5640 - val_accuracy: 0.7885 - val_loss: 0.5749\n",
+ "Epoch 3/20\n",
+ "\u001b[1m2000/2000\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m31s\u001b[0m 15ms/step - accuracy: 0.7849 - loss: 0.5761 - val_accuracy: 0.7656 - val_loss: 0.6844\n",
+ "Epoch 4/20\n",
+ "\u001b[1m2000/2000\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m30s\u001b[0m 15ms/step - accuracy: 0.7969 - loss: 0.5446 - val_accuracy: 0.7692 - val_loss: 0.4869\n",
+ "Epoch 5/20\n",
+ "\u001b[1m2000/2000\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m31s\u001b[0m 15ms/step - accuracy: 0.7997 - loss: 0.5350 - val_accuracy: 0.8393 - val_loss: 0.4124\n",
+ "Epoch 6/20\n",
+ "\u001b[1m2000/2000\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m30s\u001b[0m 15ms/step - accuracy: 0.7984 - loss: 0.5400 - val_accuracy: 0.7308 - val_loss: 0.8300\n",
+ "Epoch 7/20\n",
+ "\u001b[1m2000/2000\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m30s\u001b[0m 15ms/step - accuracy: 0.8022 - loss: 0.5343 - val_accuracy: 0.8348 - val_loss: 0.4604\n",
+ "Epoch 8/20\n",
+ "\u001b[1m2000/2000\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m30s\u001b[0m 15ms/step - accuracy: 0.7989 - loss: 0.5368 - val_accuracy: 0.7308 - val_loss: 0.6008\n",
+ "Epoch 9/20\n",
+ "\u001b[1m2000/2000\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m30s\u001b[0m 15ms/step - accuracy: 0.8116 - loss: 0.5150 - val_accuracy: 0.7790 - val_loss: 0.6313\n",
+ "Epoch 10/20\n",
+ "\u001b[1m2000/2000\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m30s\u001b[0m 15ms/step - accuracy: 0.8069 - loss: 0.5171 - val_accuracy: 0.7885 - val_loss: 0.4614\n",
+ "Epoch 11/20\n",
+ "\u001b[1m2000/2000\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m30s\u001b[0m 15ms/step - accuracy: 0.8067 - loss: 0.5171 - val_accuracy: 0.7879 - val_loss: 0.5566\n",
+ "Epoch 12/20\n",
+ "\u001b[1m2000/2000\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m30s\u001b[0m 15ms/step - accuracy: 0.8171 - loss: 0.4964 - val_accuracy: 0.8077 - val_loss: 0.5560\n",
+ "Epoch 13/20\n",
+ "\u001b[1m2000/2000\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m31s\u001b[0m 15ms/step - accuracy: 0.8144 - loss: 0.5001 - val_accuracy: 0.8080 - val_loss: 0.5437\n",
+ "Epoch 14/20\n",
+ "\u001b[1m2000/2000\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m30s\u001b[0m 15ms/step - accuracy: 0.8193 - loss: 0.4887 - val_accuracy: 0.8077 - val_loss: 0.4929\n",
+ "Epoch 15/20\n",
+ "\u001b[1m2000/2000\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m31s\u001b[0m 15ms/step - accuracy: 0.8198 - loss: 0.4781 - val_accuracy: 0.8170 - val_loss: 0.4683\n",
+ "Epoch 16/20\n",
+ "\u001b[1m2000/2000\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m30s\u001b[0m 15ms/step - accuracy: 0.8242 - loss: 0.4783 - val_accuracy: 0.7500 - val_loss: 0.7382\n",
+ "Epoch 17/20\n",
+ "\u001b[1m2000/2000\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m31s\u001b[0m 15ms/step - accuracy: 0.8233 - loss: 0.4869 - val_accuracy: 0.7835 - val_loss: 0.6065\n",
+ "Epoch 18/20\n",
+ "\u001b[1m2000/2000\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m30s\u001b[0m 15ms/step - accuracy: 0.8259 - loss: 0.4707 - val_accuracy: 0.8462 - val_loss: 0.4422\n",
+ "Epoch 19/20\n",
+ "\u001b[1m2000/2000\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m31s\u001b[0m 15ms/step - accuracy: 0.8265 - loss: 0.4711 - val_accuracy: 0.8036 - val_loss: 0.5277\n",
+ "Epoch 20/20\n",
+ "\u001b[1m2000/2000\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m30s\u001b[0m 15ms/step - accuracy: 0.8305 - loss: 0.4483 - val_accuracy: 0.8462 - val_loss: 0.4460\n"
+ ]
+ }
+ ],
+ "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",
+ ")"
+ ]
+ },
+ {
+ "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": 27,
+ "metadata": {},
+ "outputs": [
{
- "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')"
+ "data": {
+ "text/plain": [
+ ""
]
+ },
+ "execution_count": 27,
+ "metadata": {},
+ "output_type": "execute_result"
},
{
- "cell_type": "code",
- "execution_count": 26,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Num GPUs Available: 0\n"
- ]
- }
- ],
- "source": [
- "print(\"Num GPUs Available: \", len(tf.config.list_physical_devices('GPU')))"
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
]
- },
+ },
+ "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"
+ ]
+ },
+ {
+ "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": 29,
+ "metadata": {},
+ "outputs": [
{
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
+ ]
}
- ],
- "metadata": {
- "colab": {
- "provenance": []
- },
- "kernelspec": {
- "display_name": "Python 3",
- "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": "3.11.1"
+ ],
+ "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": 26,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Num GPUs Available: 0\n"
+ ]
}
+ ],
+ "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"
},
- "nbformat": 4,
- "nbformat_minor": 0
+ "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
}
diff --git a/Terrain_V4-4 layer cnn.ipynb b/Terrain_V4-4 layer cnn.ipynb
new file mode 100644
index 00000000..75af04c4
--- /dev/null
+++ b/Terrain_V4-4 layer cnn.ipynb
@@ -0,0 +1,668 @@
+{
+ "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": [
+ {
+ "data": {
+ "text/html": [
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+ "│ conv2d_3 (Conv2D) │ (None, 4, 4, 512) │ 1,180,160 │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ max_pooling2d_3 (MaxPooling2D) │ (None, 2, 2, 512) │ 0 │\n",
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+ "│ (BatchNormalization) │ │ │\n",
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+ "│ dropout_1 (Dropout) │ (None, 1024) │ 0 │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ batch_normalization_5 │ (None, 1024) │ 4,096 │\n",
+ "│ (BatchNormalization) │ │ │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ dense_2 (Dense) │ (None, 512) │ 524,800 │\n",
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+ "│ dropout_2 (Dropout) │ (None, 512) │ 0 │\n",
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+ "┃\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",
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+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\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"
+ ]
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+ "metadata": {},
+ "output_type": "display_data"
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+ "metadata": {},
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+ "data": {
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+ " Trainable params: 5,233,157 (19.96 MB)\n",
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+ ],
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+ "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": [
+ ""
+ ]
+ },
+ "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
+}
diff --git a/Terrain_V4-Copy1.ipynb b/Terrain_V4-Copy1.ipynb
new file mode 100644
index 00000000..f7be11b2
--- /dev/null
+++ b/Terrain_V4-Copy1.ipynb
@@ -0,0 +1,645 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Importing necessary Libraries"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Install necessary packages and import libraries\n",
+ "# The first few lines ensure that TensorFlow, Scipy, and Matplotlib are installed.\n",
+ "# pip install tensorflow\n",
+ "# pip install scipy\n",
+ "# pip install matplotlib\n",
+ "import tensorflow as tf\n",
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Inline plotting with Matplotlib"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# %matplotlib inline\n",
+ "# this command ensures that plots are displayed directly in the notebook after being generated."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Building a Convolutional Neural Network (CNN) model for terrain classification\n",
+ "# Importing layers from Keras for building the model architecture\n",
+ "from keras.preprocessing import image\n",
+ "from keras.models import Sequential\n",
+ "from keras.layers import Conv2D\n",
+ "from keras.layers import MaxPooling2D\n",
+ "from keras.layers import Flatten\n",
+ "from keras.layers import Dense\n",
+ "from keras.layers import Dropout\n",
+ "from keras.layers import BatchNormalization\n",
+ "from keras.models import load_model\n",
+ "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
+ "from tensorflow.keras.callbacks import LearningRateScheduler, ReduceLROnPlateau\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Defining the CNN model\n",
+ "The following code defines a CNN model using TensorFlow's Keras API. The model is designed for terrain classification with a series of convolutional layers, pooling layers, and fully connected layers.\n",
+ "\n",
+ "* **Convolutional Layers:** These layers apply filters to the input image, helping to detect various features (like edges, textures, etc.).\n",
+ "* **Max Pooling:** This operation reduces the spatial dimensions (width and height) of the output from the convolutional layers, which helps in reducing computational complexity and controlling overfitting.\n",
+ "* **Batch Normalization:** This technique normalizes the outputs of a previous activation layer, which helps stabilize learning and can accelerate training.\n",
+ "* **Dropout Layers:** These layers randomly set a fraction of the input units to zero during training to help prevent overfitting.\n",
+ "* **Softmax Layer:** This is used in the output layer for multi-class classification problems, providing probabilities for each class.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Define the CNN model architecture with residual connections\n",
+ "def create_model():\n",
+ " model = tf.keras.Sequential([\n",
+ " tf.keras.layers.Input(shape=(64, 64, 3)),\n",
+ " tf.keras.layers.Conv2D(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.Conv2D(256, kernel_size=3, activation='relu'),\n",
+ " tf.keras.layers.MaxPooling2D(pool_size=2, strides=2),\n",
+ " tf.keras.layers.BatchNormalization(),\n",
+ "\n",
+ " tf.keras.layers.Flatten(),\n",
+ "\n",
+ " tf.keras.layers.Dense(1024, activation='relu'),\n",
+ " tf.keras.layers.Dropout(0.2),\n",
+ " tf.keras.layers.BatchNormalization(),\n",
+ "\n",
+ " tf.keras.layers.Dense(1024, activation='relu'),\n",
+ " tf.keras.layers.Dropout(0.2),\n",
+ " tf.keras.layers.BatchNormalization(),\n",
+ "\n",
+ " tf.keras.layers.Dense(256, activation='relu'),\n",
+ " tf.keras.layers.Dropout(0.2),\n",
+ " tf.keras.layers.BatchNormalization(),\n",
+ "\n",
+ " tf.keras.layers.Dense(5, activation='softmax')\n",
+ " ])\n",
+ " return model\n",
+ "\n",
+ "model = create_model()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Compiling the CNN model\n",
+ "The model is compiled using the Adam optimizer, with categorical crossentropy as the loss function, and accuracy as the evaluation metric."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Compile the model\n",
+ "# Categorical crossentropy is used since the labels are one-hot encoded.\n",
+ "# Adam optimizer is used for its adaptive learning rate.\n",
+ "\n",
+ "model.compile(optimizer=tf.keras.optimizers.Adam(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": [
+ {
+ "data": {
+ "text/html": [
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+ "│ batch_normalization_3 │ (None, 1024) │ 4,096 │\n",
+ "│ (BatchNormalization) │ │ │\n",
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+ "│ 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",
+ "│ flatten (\u001b[38;5;33mFlatten\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m9216\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;34m9,438,208\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",
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+ "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ dense_1 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1024\u001b[0m) │ \u001b[38;5;34m1,049,600\u001b[0m │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ dropout_1 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1024\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ batch_normalization_4 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1024\u001b[0m) │ \u001b[38;5;34m4,096\u001b[0m │\n",
+ "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
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+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ dropout_2 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ batch_normalization_5 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m1,024\u001b[0m │\n",
+ "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ dense_3 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m) │ \u001b[38;5;34m1,285\u001b[0m │\n",
+ "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n"
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+ "metadata": {},
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+ }
+ ],
+ "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[1m2:28\u001b[0m 419ms/step - accuracy: 0.5077 - loss: 1.4332"
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+ "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[1m72s\u001b[0m 130ms/step - accuracy: 0.5473 - loss: 1.2346 - val_accuracy: 0.5469 - val_loss: 1.1331 - learning_rate: 0.0100\n",
+ "Epoch 2/20\n",
+ "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m64s\u001b[0m 124ms/step - accuracy: 0.6323 - loss: 0.9565 - val_accuracy: 0.5769 - val_loss: 1.0547 - learning_rate: 0.0100\n",
+ "Epoch 3/20\n",
+ "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m65s\u001b[0m 125ms/step - accuracy: 0.6493 - loss: 0.9014 - val_accuracy: 0.6250 - val_loss: 1.2975 - learning_rate: 0.0100\n",
+ "Epoch 4/20\n",
+ "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m65s\u001b[0m 125ms/step - accuracy: 0.6716 - loss: 0.8550 - val_accuracy: 0.7692 - val_loss: 0.6250 - learning_rate: 0.0100\n",
+ "Epoch 5/20\n",
+ "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m68s\u001b[0m 131ms/step - accuracy: 0.6809 - loss: 0.8325 - val_accuracy: 0.5469 - val_loss: 1.2378 - learning_rate: 0.0100\n",
+ "Epoch 6/20\n",
+ "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m69s\u001b[0m 132ms/step - accuracy: 0.6944 - loss: 0.8136 - val_accuracy: 0.7692 - val_loss: 0.7075 - learning_rate: 0.0100\n",
+ "Epoch 7/20\n",
+ "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m69s\u001b[0m 132ms/step - accuracy: 0.6911 - loss: 0.7981 - val_accuracy: 0.4442 - val_loss: 1.9784 - learning_rate: 0.0100\n",
+ "Epoch 8/20\n",
+ "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m67s\u001b[0m 129ms/step - accuracy: 0.6969 - loss: 0.7973 - val_accuracy: 0.4038 - val_loss: 1.8025 - learning_rate: 0.0100\n",
+ "Epoch 9/20\n",
+ "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m100s\u001b[0m 193ms/step - accuracy: 0.7093 - loss: 0.7852 - val_accuracy: 0.7031 - val_loss: 0.9339 - learning_rate: 0.0100\n",
+ "Epoch 10/20\n",
+ "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m110s\u001b[0m 207ms/step - accuracy: 0.7181 - loss: 0.7566 - val_accuracy: 0.7115 - val_loss: 0.7876 - learning_rate: 0.0100\n",
+ "Epoch 11/20\n",
+ "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m145s\u001b[0m 277ms/step - accuracy: 0.7425 - loss: 0.6846 - val_accuracy: 0.7545 - val_loss: 0.6115 - learning_rate: 0.0010\n",
+ "Epoch 12/20\n",
+ "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m146s\u001b[0m 275ms/step - accuracy: 0.7507 - loss: 0.6655 - val_accuracy: 0.7885 - val_loss: 0.5464 - learning_rate: 0.0010\n",
+ "Epoch 13/20\n",
+ "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m152s\u001b[0m 287ms/step - accuracy: 0.7497 - loss: 0.6606 - val_accuracy: 0.7857 - val_loss: 0.5490 - learning_rate: 0.0010\n",
+ "Epoch 14/20\n",
+ "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m186s\u001b[0m 351ms/step - accuracy: 0.7560 - loss: 0.6464 - val_accuracy: 0.7692 - val_loss: 0.5655 - learning_rate: 0.0010\n",
+ "Epoch 15/20\n",
+ "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m214s\u001b[0m 401ms/step - accuracy: 0.7608 - loss: 0.6395 - val_accuracy: 0.7768 - val_loss: 0.5530 - learning_rate: 0.0010\n",
+ "Epoch 16/20\n",
+ "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m187s\u001b[0m 348ms/step - accuracy: 0.7591 - loss: 0.6357 - val_accuracy: 0.8462 - val_loss: 0.3674 - learning_rate: 0.0010\n",
+ "Epoch 17/20\n",
+ "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m195s\u001b[0m 365ms/step - accuracy: 0.7639 - loss: 0.6335 - val_accuracy: 0.8214 - val_loss: 0.5036 - learning_rate: 0.0010\n",
+ "Epoch 18/20\n",
+ "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m204s\u001b[0m 391ms/step - accuracy: 0.7673 - loss: 0.6164 - val_accuracy: 0.7885 - val_loss: 0.5348 - learning_rate: 0.0010\n",
+ "Epoch 19/20\n",
+ "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m199s\u001b[0m 373ms/step - accuracy: 0.7724 - loss: 0.6068 - val_accuracy: 0.8147 - val_loss: 0.5197 - learning_rate: 0.0010\n",
+ "Epoch 20/20\n",
+ "\u001b[1m512/512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m215s\u001b[0m 403ms/step - accuracy: 0.7654 - loss: 0.6187 - val_accuracy: 0.8269 - val_loss: 0.4078 - 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: 0.7643\n",
+ "Final Validation Accuracy: 0.8269\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": [
+ ""
+ ]
+ },
+ "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
+}
diff --git a/Terrain_V4.ipynb b/Terrain_V4.ipynb
new file mode 100644
index 00000000..66862db3
--- /dev/null
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+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Importing necessary Libraries"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "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": 14,
+ "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": 15,
+ "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": 16,
+ "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": 17,
+ "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": 18,
+ "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": 19,
+ "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": 20,
+ "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": 21,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
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+ "
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+ "│ (BatchNormalization) │ │ │\n",
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+ "│ (BatchNormalization) │ │ │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
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+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ dropout_8 (Dropout) │ (None, 256) │ 0 │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ batch_normalization_17 │ (None, 256) │ 1,024 │\n",
+ "│ (BatchNormalization) │ │ │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ dense_11 (Dense) │ (None, 5) │ 1,285 │\n",
+ "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n",
+ "
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+ "┃\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_6 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m62\u001b[0m, \u001b[38;5;34m62\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m896\u001b[0m │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ max_pooling2d_6 (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m31\u001b[0m, \u001b[38;5;34m31\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ batch_normalization_12 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m31\u001b[0m, \u001b[38;5;34m31\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m128\u001b[0m │\n",
+ "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ conv2d_7 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m29\u001b[0m, \u001b[38;5;34m29\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m18,496\u001b[0m │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ max_pooling2d_7 (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ batch_normalization_13 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m256\u001b[0m │\n",
+ "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ conv2d_8 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m73,856\u001b[0m │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ max_pooling2d_8 (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ batch_normalization_14 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m512\u001b[0m │\n",
+ "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ flatten_2 (\u001b[38;5;33mFlatten\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4608\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ dense_8 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1024\u001b[0m) │ \u001b[38;5;34m4,719,616\u001b[0m │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ dropout_6 (\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_15 │ (\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_9 (\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_7 (\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_16 │ (\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_10 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m262,400\u001b[0m │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ dropout_8 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ batch_normalization_17 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m1,024\u001b[0m │\n",
+ "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
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+ "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n"
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+ "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": 29,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 1/20\n",
+ "\u001b[1m256/256\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m44s\u001b[0m 166ms/step - accuracy: 0.6662 - loss: 0.8773 - val_accuracy: 0.5670 - val_loss: 1.1633 - learning_rate: 0.0100\n",
+ "Epoch 2/20\n",
+ "\u001b[1m256/256\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m42s\u001b[0m 162ms/step - accuracy: 0.6876 - loss: 0.8213 - val_accuracy: 0.6731 - val_loss: 0.7837 - learning_rate: 0.0100\n",
+ "Epoch 3/20\n",
+ "\u001b[1m256/256\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m67s\u001b[0m 259ms/step - accuracy: 0.6984 - loss: 0.7931 - val_accuracy: 0.5692 - val_loss: 1.1659 - learning_rate: 0.0100\n",
+ "Epoch 4/20\n",
+ "\u001b[1m256/256\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m104s\u001b[0m 397ms/step - accuracy: 0.7149 - loss: 0.7656 - val_accuracy: 0.4808 - val_loss: 1.5558 - learning_rate: 0.0100\n",
+ "Epoch 5/20\n",
+ "\u001b[1m256/256\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m106s\u001b[0m 396ms/step - accuracy: 0.7121 - loss: 0.7686 - val_accuracy: 0.7411 - val_loss: 0.6265 - learning_rate: 0.0100\n",
+ "Epoch 6/20\n",
+ "\u001b[1m256/256\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m103s\u001b[0m 387ms/step - accuracy: 0.7327 - loss: 0.7244 - val_accuracy: 0.7115 - val_loss: 0.7391 - learning_rate: 0.0100\n",
+ "Epoch 7/20\n",
+ "\u001b[1m256/256\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m105s\u001b[0m 396ms/step - accuracy: 0.7207 - loss: 0.7397 - val_accuracy: 0.7277 - val_loss: 0.7334 - learning_rate: 0.0100\n",
+ "Epoch 8/20\n",
+ "\u001b[1m256/256\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m103s\u001b[0m 385ms/step - accuracy: 0.7278 - loss: 0.7360 - val_accuracy: 0.4808 - val_loss: 1.3144 - learning_rate: 0.0100\n",
+ "Epoch 9/20\n",
+ "\u001b[1m256/256\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m106s\u001b[0m 396ms/step - accuracy: 0.7188 - loss: 0.7447 - val_accuracy: 0.7790 - val_loss: 0.6228 - learning_rate: 0.0100\n",
+ "Epoch 10/20\n",
+ "\u001b[1m256/256\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m105s\u001b[0m 390ms/step - accuracy: 0.7334 - loss: 0.7203 - val_accuracy: 0.8462 - val_loss: 0.5549 - learning_rate: 0.0100\n",
+ "Epoch 11/20\n",
+ "\u001b[1m256/256\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m106s\u001b[0m 398ms/step - accuracy: 0.7467 - loss: 0.6770 - val_accuracy: 0.8304 - val_loss: 0.5045 - learning_rate: 0.0010\n",
+ "Epoch 12/20\n",
+ "\u001b[1m256/256\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m104s\u001b[0m 389ms/step - accuracy: 0.7643 - loss: 0.6322 - val_accuracy: 0.7885 - val_loss: 0.6222 - learning_rate: 0.0010\n",
+ "Epoch 13/20\n",
+ "\u001b[1m256/256\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m108s\u001b[0m 406ms/step - accuracy: 0.7657 - loss: 0.6330 - val_accuracy: 0.8371 - val_loss: 0.4806 - learning_rate: 0.0010\n",
+ "Epoch 14/20\n",
+ "\u001b[1m256/256\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m104s\u001b[0m 391ms/step - accuracy: 0.7717 - loss: 0.6135 - val_accuracy: 0.8077 - val_loss: 0.5787 - learning_rate: 0.0010\n",
+ "Epoch 15/20\n",
+ "\u001b[1m256/256\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m106s\u001b[0m 397ms/step - accuracy: 0.7790 - loss: 0.5961 - val_accuracy: 0.8304 - val_loss: 0.4824 - learning_rate: 0.0010\n",
+ "Epoch 16/20\n",
+ "\u001b[1m256/256\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m103s\u001b[0m 385ms/step - accuracy: 0.7815 - loss: 0.5944 - val_accuracy: 0.7885 - val_loss: 0.5299 - learning_rate: 0.0010\n",
+ "Epoch 17/20\n",
+ "\u001b[1m256/256\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m105s\u001b[0m 395ms/step - accuracy: 0.7859 - loss: 0.5852 - val_accuracy: 0.8482 - val_loss: 0.4389 - learning_rate: 0.0010\n",
+ "Epoch 18/20\n",
+ "\u001b[1m256/256\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m103s\u001b[0m 387ms/step - accuracy: 0.7806 - loss: 0.6004 - val_accuracy: 0.6346 - val_loss: 0.7584 - learning_rate: 0.0010\n",
+ "Epoch 19/20\n",
+ "\u001b[1m256/256\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m108s\u001b[0m 405ms/step - accuracy: 0.7790 - loss: 0.5870 - val_accuracy: 0.8371 - val_loss: 0.4646 - learning_rate: 0.0010\n",
+ "Epoch 20/20\n",
+ "\u001b[1m256/256\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m104s\u001b[0m 389ms/step - accuracy: 0.7897 - loss: 0.5827 - val_accuracy: 0.8462 - val_loss: 0.4137 - learning_rate: 0.0010\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Train the model\n",
+ "modelZ = model.fit(\n",
+ " training_set,\n",
+ " steps_per_epoch=256,\n",
+ " epochs=20,\n",
+ " validation_data=test_set,\n",
+ " validation_steps=7,\n",
+ " callbacks=[lr_schedule, lr_reduction]\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Plotting Training and Validation Accuracy\n",
+ "This section plots the training and validation accuracy and loss over time to visualize how the model performs during training."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Final Training Accuracy: 0.7175\n",
+ "Final Validation Accuracy: 0.7115\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Print the final training and validation accuracy\n",
+ "train_accuracy = modelZ.history['accuracy'][-1]\n",
+ "val_accuracy = modelZ.history['val_accuracy'][-1]\n",
+ "\n",
+ "print(f\"Final Training Accuracy: {train_accuracy:.4f}\")\n",
+ "print(f\"Final Validation Accuracy: {val_accuracy:.4f}\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "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": 25,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# model.save('modelZ.h5')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "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": 27,
+ "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
+}
diff --git a/Terrain_V5 Optimizer SGD with Momentum-Copy1.ipynb b/Terrain_V5 Optimizer SGD with Momentum-Copy1.ipynb
new file mode 100644
index 00000000..c3be67e8
--- /dev/null
+++ b/Terrain_V5 Optimizer SGD with Momentum-Copy1.ipynb
@@ -0,0 +1,668 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Importing necessary Libraries"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Install necessary packages and import libraries\n",
+ "# The first few lines ensure that TensorFlow, Scipy, and Matplotlib are installed.\n",
+ "# pip install tensorflow\n",
+ "# pip install scipy\n",
+ "# pip install matplotlib\n",
+ "import tensorflow as tf\n",
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Inline plotting with Matplotlib"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# %matplotlib inline\n",
+ "# this command ensures that plots are displayed directly in the notebook after being generated."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Building a Convolutional Neural Network (CNN) model for terrain classification\n",
+ "# Importing layers from Keras for building the model architecture\n",
+ "from keras.preprocessing import image\n",
+ "from keras.models import Sequential\n",
+ "from keras.layers import Conv2D\n",
+ "from keras.layers import MaxPooling2D\n",
+ "from keras.layers import Flatten\n",
+ "from keras.layers import Dense\n",
+ "from keras.layers import Dropout\n",
+ "from keras.layers import BatchNormalization\n",
+ "from keras.models import load_model\n",
+ "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
+ "from tensorflow.keras.callbacks import LearningRateScheduler, ReduceLROnPlateau\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Defining the CNN model\n",
+ "The following code defines a CNN model using TensorFlow's Keras API. The model is designed for terrain classification with a series of convolutional layers, pooling layers, and fully connected layers.\n",
+ "\n",
+ "* **Convolutional Layers:** These layers apply filters to the input image, helping to detect various features (like edges, textures, etc.).\n",
+ "* **Max Pooling:** This operation reduces the spatial dimensions (width and height) of the output from the convolutional layers, which helps in reducing computational complexity and controlling overfitting.\n",
+ "* **Batch Normalization:** This technique normalizes the outputs of a previous activation layer, which helps stabilize learning and can accelerate training.\n",
+ "* **Dropout Layers:** These layers randomly set a fraction of the input units to zero during training to help prevent overfitting.\n",
+ "* **Softmax Layer:** This is used in the output layer for multi-class classification problems, providing probabilities for each class.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Define the CNN model architecture\n",
+ "model = tf.keras.Sequential([\n",
+ "\n",
+ " # Input layer\n",
+ " tf.keras.layers.Input(shape=(64, 64, 3)),\n",
+ " \n",
+ " # First convolutional layer\n",
+ " tf.keras.layers.Conv2D(64, kernel_size=3, activation='relu'),\n",
+ " tf.keras.layers.MaxPool2D(pool_size=2, strides=2),\n",
+ " tf.keras.layers.BatchNormalization(),\n",
+ " \n",
+ " # Second convolutional layer\n",
+ " tf.keras.layers.Conv2D(128, kernel_size=3, activation='relu'),\n",
+ " tf.keras.layers.MaxPool2D(pool_size=2, strides=2),\n",
+ " tf.keras.layers.BatchNormalization(),\n",
+ "\n",
+ " # Third convolutional layer\n",
+ " tf.keras.layers.Conv2D(256, kernel_size=3, activation='relu'),\n",
+ " tf.keras.layers.MaxPool2D(pool_size=2, strides=2),\n",
+ " tf.keras.layers.BatchNormalization(),\n",
+ " \n",
+ " # Fourth convolutional layer\n",
+ " tf.keras.layers.Conv2D(512, kernel_size=3, activation='relu'),\n",
+ " tf.keras.layers.MaxPool2D(pool_size=2, strides=2),\n",
+ " tf.keras.layers.BatchNormalization(),\n",
+ " \n",
+ " \n",
+ " # Fully connected classifier\n",
+ " tf.keras.layers.Flatten(),\n",
+ "\n",
+ " tf.keras.layers.Dense(1024, activation='relu'),\n",
+ " tf.keras.layers.Dropout(0.2),\n",
+ " tf.keras.layers.BatchNormalization(),\n",
+ "\n",
+ " tf.keras.layers.Dense(1024, activation='relu'),\n",
+ " tf.keras.layers.Dropout(0.2),\n",
+ " tf.keras.layers.BatchNormalization(),\n",
+ "\n",
+ " tf.keras.layers.Dense(512, activation='relu'),\n",
+ " tf.keras.layers.Dropout(0.2),\n",
+ " tf.keras.layers.BatchNormalization(),\n",
+ "\n",
+ " tf.keras.layers.Dense(5, activation='softmax')\n",
+ "])\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Compiling the CNN model\n",
+ "The model is compiled using the Adam optimizer, with categorical crossentropy as the loss function, and accuracy as the evaluation metric."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Compile the model\n",
+ "# Categorical crossentropy is used since the labels are one-hot encoded.\n",
+ "# Adam optimizer is used for its adaptive learning rate.\n",
+ "\n",
+ "model.compile(optimizer=tf.keras.optimizers.SGD(learning_rate=0.01, momentum=0.9), loss='categorical_crossentropy', metrics=['accuracy'])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Data Augmentation and Preprocessing\n",
+ "This section applies data augmentation techniques using the `ImageDataGenerator` from Keras to generate more training samples and preprocess the images. These operations include rescaling, rotating, shifting, and flipping.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Data augmentation and preprocessing for training and validation datasets\n",
+ "# The ImageDataGenerator applies various transformations to augment the training data\n",
+ "# Data augmentation for training dataset\n",
+ "train_datagen = ImageDataGenerator(\n",
+ " rescale=1./255,\n",
+ " shear_range=0.2,\n",
+ " zoom_range=0.2,\n",
+ " horizontal_flip=True,\n",
+ " rotation_range=30,\n",
+ " brightness_range=[0.8, 1.2],\n",
+ " width_shift_range=0.2,\n",
+ " height_shift_range=0.2\n",
+ ")\n",
+ "\n",
+ "# Validation dataset (only rescaling)\n",
+ "test_datagen = ImageDataGenerator(rescale=1./255)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Loading and Preparing the Data\n",
+ "In this section, the training and validation data are loaded from the respective directories, and the images are prepared for the model using the augmentation techniques defined earlier.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Found 10000 images belonging to 5 classes.\n",
+ "Found 500 images belonging to 5 classes.\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Assuming you've created 'train_subset' and 'test_subset' directories\n",
+ "training_set = train_datagen.flow_from_directory(\n",
+ " r\"D:\\Jupyter Notebook\\Contribution\\Terrain-Recognition\\Data Main\\Training Data\",\n",
+ " target_size=(64, 64),\n",
+ " batch_size=64,\n",
+ " class_mode='categorical'\n",
+ ")\n",
+ "\n",
+ "test_set = test_datagen.flow_from_directory(\n",
+ " r\"D:\\Jupyter Notebook\\Contribution\\Terrain-Recognition\\Data Main\\Testing Data\",\n",
+ " target_size=(64, 64),\n",
+ " batch_size=64,\n",
+ " class_mode='categorical'\n",
+ ")\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Define learning rate scheduler\n",
+ "def scheduler(epoch, lr):\n",
+ " if epoch < 10:\n",
+ " return 1e-2\n",
+ " else:\n",
+ " return 1e-3\n",
+ "\n",
+ "lr_schedule = LearningRateScheduler(scheduler)\n",
+ "lr_reduction = ReduceLROnPlateau(monitor='val_loss', factor=0.5, patience=3, min_lr=1e-6)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "Model: \"sequential\"\n",
+ "
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+ ],
+ "text/plain": [
+ "\u001b[1mModel: \"sequential\"\u001b[0m\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
+ "┃ Layer (type) ┃ Output Shape ┃ Param # ┃\n",
+ "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
+ "│ conv2d (Conv2D) │ (None, 62, 62, 64) │ 1,792 │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ max_pooling2d (MaxPooling2D) │ (None, 31, 31, 64) │ 0 │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ batch_normalization │ (None, 31, 31, 64) │ 256 │\n",
+ "│ (BatchNormalization) │ │ │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ conv2d_1 (Conv2D) │ (None, 29, 29, 128) │ 73,856 │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
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+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
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+ "│ (BatchNormalization) │ │ │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ conv2d_2 (Conv2D) │ (None, 12, 12, 256) │ 295,168 │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ max_pooling2d_2 (MaxPooling2D) │ (None, 6, 6, 256) │ 0 │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
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+ "│ (BatchNormalization) │ │ │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ conv2d_3 (Conv2D) │ (None, 4, 4, 512) │ 1,180,160 │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ max_pooling2d_3 (MaxPooling2D) │ (None, 2, 2, 512) │ 0 │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ batch_normalization_3 │ (None, 2, 2, 512) │ 2,048 │\n",
+ "│ (BatchNormalization) │ │ │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ flatten (Flatten) │ (None, 2048) │ 0 │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
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+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ dropout (Dropout) │ (None, 1024) │ 0 │\n",
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+ "│ batch_normalization_4 │ (None, 1024) │ 4,096 │\n",
+ "│ (BatchNormalization) │ │ │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
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+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ dropout_1 (Dropout) │ (None, 1024) │ 0 │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ batch_normalization_5 │ (None, 1024) │ 4,096 │\n",
+ "│ (BatchNormalization) │ │ │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ dense_2 (Dense) │ (None, 512) │ 524,800 │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ dropout_2 (Dropout) │ (None, 512) │ 0 │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ batch_normalization_6 │ (None, 512) │ 2,048 │\n",
+ "│ (BatchNormalization) │ │ │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ dense_3 (Dense) │ (None, 5) │ 2,565 │\n",
+ "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n",
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+ "┃\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": [
+ " Total params: 5,240,197 (19.99 MB)\n",
+ "
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+ ],
+ "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": [
+ " Trainable params: 5,233,157 (19.96 MB)\n",
+ "
\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": [
+ " Non-trainable params: 7,040 (27.50 KB)\n",
+ "
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+ ],
+ "text/plain": [
+ "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m7,040\u001b[0m (27.50 KB)\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "model.summary()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Training the CNN Model\n",
+ "The model is trained using the training and validation data. The training process is monitored, and a learning rate scheduler is used to adjust the learning rate dynamically during training."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 1/20\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "C:\\Users\\DEEPANSHU\\anaconda3\\Lib\\site-packages\\keras\\src\\trainers\\data_adapters\\py_dataset_adapter.py:121: UserWarning: Your `PyDataset` class should call `super().__init__(**kwargs)` in its constructor. `**kwargs` can include `workers`, `use_multiprocessing`, `max_queue_size`. Do not pass these arguments to `fit()`, as they will be ignored.\n",
+ " self._warn_if_super_not_called()\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\u001b[1m 157/1024\u001b[0m \u001b[32m━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m14:17\u001b[0m 989ms/step - accuracy: 0.4626 - loss: 1.5149"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "C:\\Users\\DEEPANSHU\\anaconda3\\Lib\\contextlib.py:155: UserWarning: Your input ran out of data; interrupting training. Make sure that your dataset or generator can generate at least `steps_per_epoch * epochs` batches. You may need to use the `.repeat()` function when building your dataset.\n",
+ " self.gen.throw(typ, value, traceback)\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\u001b[1m1024/1024\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m188s\u001b[0m 157ms/step - accuracy: 0.5140 - loss: 1.3314 - val_accuracy: 0.4308 - val_loss: 1.3102 - learning_rate: 0.0100\n",
+ "Epoch 2/20\n",
+ "\u001b[1m1024/1024\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m152s\u001b[0m 143ms/step - accuracy: 0.6109 - loss: 1.0252 - val_accuracy: 0.5000 - val_loss: 1.1684 - learning_rate: 0.0100\n",
+ "Epoch 3/20\n",
+ "\u001b[1m1024/1024\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m194s\u001b[0m 183ms/step - accuracy: 0.6317 - loss: 0.9710 - val_accuracy: 0.6897 - val_loss: 0.7787 - learning_rate: 0.0100\n",
+ "Epoch 4/20\n",
+ "\u001b[1m1024/1024\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m183s\u001b[0m 169ms/step - accuracy: 0.6497 - loss: 0.9245 - val_accuracy: 0.6731 - val_loss: 0.7455 - learning_rate: 0.0100\n",
+ "Epoch 5/20\n",
+ "\u001b[1m1024/1024\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m195s\u001b[0m 182ms/step - accuracy: 0.6599 - loss: 0.8963 - val_accuracy: 0.7321 - val_loss: 0.7603 - learning_rate: 0.0100\n",
+ "Epoch 6/20\n",
+ "\u001b[1m1024/1024\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m228s\u001b[0m 215ms/step - accuracy: 0.6779 - loss: 0.8503 - val_accuracy: 0.6923 - val_loss: 0.8339 - learning_rate: 0.0100\n",
+ "Epoch 7/20\n",
+ "\u001b[1m1024/1024\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m186s\u001b[0m 174ms/step - accuracy: 0.6769 - loss: 0.8563 - val_accuracy: 0.5871 - val_loss: 1.0309 - learning_rate: 0.0100\n",
+ "Epoch 8/20\n",
+ "\u001b[1m1024/1024\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m220s\u001b[0m 206ms/step - accuracy: 0.6814 - loss: 0.8313 - val_accuracy: 0.6538 - val_loss: 0.9094 - learning_rate: 0.0100\n",
+ "Epoch 9/20\n",
+ "\u001b[1m1024/1024\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m229s\u001b[0m 212ms/step - accuracy: 0.6997 - loss: 0.8143 - val_accuracy: 0.6004 - val_loss: 1.0904 - learning_rate: 0.0100\n",
+ "Epoch 10/20\n",
+ "\u001b[1m1024/1024\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m172s\u001b[0m 157ms/step - accuracy: 0.7021 - loss: 0.7924 - val_accuracy: 0.6731 - val_loss: 0.7978 - learning_rate: 0.0100\n",
+ "Epoch 11/20\n",
+ "\u001b[1m1024/1024\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m142s\u001b[0m 132ms/step - accuracy: 0.7170 - loss: 0.7424 - val_accuracy: 0.8103 - val_loss: 0.5171 - learning_rate: 0.0010\n",
+ "Epoch 12/20\n",
+ "\u001b[1m1024/1024\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m136s\u001b[0m 127ms/step - accuracy: 0.7380 - loss: 0.6932 - val_accuracy: 0.6346 - val_loss: 0.7559 - learning_rate: 0.0010\n",
+ "Epoch 13/20\n",
+ "\u001b[1m1024/1024\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m135s\u001b[0m 127ms/step - accuracy: 0.7417 - loss: 0.6944 - val_accuracy: 0.7679 - val_loss: 0.5687 - learning_rate: 0.0010\n",
+ "Epoch 14/20\n",
+ "\u001b[1m1024/1024\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m137s\u001b[0m 128ms/step - accuracy: 0.7487 - loss: 0.6717 - val_accuracy: 0.8462 - val_loss: 0.4664 - learning_rate: 0.0010\n",
+ "Epoch 15/20\n",
+ "\u001b[1m1024/1024\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m133s\u001b[0m 124ms/step - accuracy: 0.7470 - loss: 0.6727 - val_accuracy: 0.8103 - val_loss: 0.5160 - learning_rate: 0.0010\n",
+ "Epoch 16/20\n",
+ "\u001b[1m1024/1024\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m96s\u001b[0m 90ms/step - accuracy: 0.7480 - loss: 0.6764 - val_accuracy: 0.8077 - val_loss: 0.3926 - learning_rate: 0.0010\n",
+ "Epoch 17/20\n",
+ "\u001b[1m1024/1024\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m94s\u001b[0m 89ms/step - accuracy: 0.7533 - loss: 0.6591 - val_accuracy: 0.8036 - val_loss: 0.5209 - learning_rate: 0.0010\n",
+ "Epoch 18/20\n",
+ "\u001b[1m1024/1024\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m93s\u001b[0m 88ms/step - accuracy: 0.7544 - loss: 0.6637 - val_accuracy: 0.7885 - val_loss: 0.5641 - learning_rate: 0.0010\n",
+ "Epoch 19/20\n",
+ "\u001b[1m1024/1024\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m95s\u001b[0m 88ms/step - accuracy: 0.7539 - loss: 0.6637 - val_accuracy: 0.8058 - val_loss: 0.5181 - learning_rate: 0.0010\n",
+ "Epoch 20/20\n",
+ "\u001b[1m1024/1024\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m93s\u001b[0m 88ms/step - accuracy: 0.7600 - loss: 0.6468 - val_accuracy: 0.7115 - val_loss: 0.5805 - learning_rate: 0.0010\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Train the model\n",
+ "modelZ = model.fit(\n",
+ " training_set,\n",
+ " steps_per_epoch=1024,\n",
+ " epochs=20,\n",
+ " validation_data=test_set,\n",
+ " validation_steps=7,\n",
+ " callbacks=[lr_schedule, lr_reduction]\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Plotting Training and Validation Accuracy\n",
+ "This section plots the training and validation accuracy and loss over time to visualize how the model performs during training."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Final Training Accuracy: 0.7609\n",
+ "Final Validation Accuracy: 0.7115\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Print the final training and validation accuracy\n",
+ "train_accuracy = modelZ.history['accuracy'][-1]\n",
+ "val_accuracy = modelZ.history['val_accuracy'][-1]\n",
+ "\n",
+ "print(f\"Final Training Accuracy: {train_accuracy:.4f}\")\n",
+ "print(f\"Final Validation Accuracy: {val_accuracy:.4f}\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "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
+}
diff --git a/Terrain_V5 Optimizer SGD with Momentum.ipynb b/Terrain_V5 Optimizer SGD with Momentum.ipynb
new file mode 100644
index 00000000..2242a6a1
--- /dev/null
+++ b/Terrain_V5 Optimizer SGD with Momentum.ipynb
@@ -0,0 +1,648 @@
+{
+ "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(32, 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(64, 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(128, kernel_size=3, activation='relu'),\n",
+ " tf.keras.layers.MaxPool2D(pool_size=2, strides=2),\n",
+ " tf.keras.layers.BatchNormalization(),\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(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"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Compiling the CNN model\n",
+ "The model is compiled using the Adam optimizer, with categorical crossentropy as the loss function, and accuracy as the evaluation metric."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Compile the model\n",
+ "# Categorical crossentropy is used since the labels are one-hot encoded.\n",
+ "# Adam optimizer is used for its adaptive learning rate.\n",
+ "\n",
+ "model.compile(optimizer=tf.keras.optimizers.SGD(learning_rate=0.01, momentum=0.9), loss='categorical_crossentropy', metrics=['accuracy'])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Data Augmentation and Preprocessing\n",
+ "This section applies data augmentation techniques using the `ImageDataGenerator` from Keras to generate more training samples and preprocess the images. These operations include rescaling, rotating, shifting, and flipping.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Data augmentation and preprocessing for training and validation datasets\n",
+ "# The ImageDataGenerator applies various transformations to augment the training data\n",
+ "# Data augmentation for training dataset\n",
+ "train_datagen = ImageDataGenerator(\n",
+ " rescale=1./255,\n",
+ " shear_range=0.2,\n",
+ " zoom_range=0.2,\n",
+ " horizontal_flip=True,\n",
+ " rotation_range=30,\n",
+ " brightness_range=[0.8, 1.2],\n",
+ " width_shift_range=0.2,\n",
+ " height_shift_range=0.2\n",
+ ")\n",
+ "\n",
+ "# Validation dataset (only rescaling)\n",
+ "test_datagen = ImageDataGenerator(rescale=1./255)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Loading and Preparing the Data\n",
+ "In this section, the training and validation data are loaded from the respective directories, and the images are prepared for the model using the augmentation techniques defined earlier.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Found 10000 images belonging to 5 classes.\n",
+ "Found 500 images belonging to 5 classes.\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Assuming you've created 'train_subset' and 'test_subset' directories\n",
+ "training_set = train_datagen.flow_from_directory(\n",
+ " r\"D:\\Jupyter Notebook\\Contribution\\Terrain-Recognition\\Data Main\\Training Data\",\n",
+ " target_size=(64, 64),\n",
+ " batch_size=64,\n",
+ " class_mode='categorical'\n",
+ ")\n",
+ "\n",
+ "test_set = test_datagen.flow_from_directory(\n",
+ " r\"D:\\Jupyter Notebook\\Contribution\\Terrain-Recognition\\Data Main\\Testing Data\",\n",
+ " target_size=(64, 64),\n",
+ " batch_size=64,\n",
+ " class_mode='categorical'\n",
+ ")\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Define learning rate scheduler\n",
+ "def scheduler(epoch, lr):\n",
+ " if epoch < 10:\n",
+ " return 1e-2\n",
+ " else:\n",
+ " return 1e-3\n",
+ "\n",
+ "lr_schedule = LearningRateScheduler(scheduler)\n",
+ "lr_reduction = ReduceLROnPlateau(monitor='val_loss', factor=0.5, patience=3, min_lr=1e-6)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "Model: \"sequential\"\n",
+ "
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+ "┃ Layer (type) ┃ Output Shape ┃ Param # ┃\n",
+ "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
+ "│ conv2d (Conv2D) │ (None, 62, 62, 32) │ 896 │\n",
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+ "│ (BatchNormalization) │ │ │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
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+ "│ (BatchNormalization) │ │ │\n",
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+ "│ batch_normalization_2 │ (None, 6, 6, 128) │ 512 │\n",
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+ "│ flatten (Flatten) │ (None, 4608) │ 0 │\n",
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+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ dropout (Dropout) │ (None, 1024) │ 0 │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ batch_normalization_3 │ (None, 1024) │ 4,096 │\n",
+ "│ (BatchNormalization) │ │ │\n",
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+ "│ dense_1 (Dense) │ (None, 1024) │ 1,049,600 │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ dropout_1 (Dropout) │ (None, 1024) │ 0 │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ batch_normalization_4 │ (None, 1024) │ 4,096 │\n",
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+ "│ batch_normalization_5 │ (None, 256) │ 1,024 │\n",
+ "│ (BatchNormalization) │ │ │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ dense_3 (Dense) │ (None, 5) │ 1,285 │\n",
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+ "┃\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",
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+ "│ conv2d (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m62\u001b[0m, \u001b[38;5;34m62\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m896\u001b[0m │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ max_pooling2d (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m31\u001b[0m, \u001b[38;5;34m31\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ batch_normalization │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m31\u001b[0m, \u001b[38;5;34m31\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m128\u001b[0m │\n",
+ "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ conv2d_1 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m29\u001b[0m, \u001b[38;5;34m29\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m18,496\u001b[0m │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ max_pooling2d_1 (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ batch_normalization_1 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m256\u001b[0m │\n",
+ "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ conv2d_2 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m73,856\u001b[0m │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ max_pooling2d_2 (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ batch_normalization_2 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m512\u001b[0m │\n",
+ "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ flatten (\u001b[38;5;33mFlatten\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4608\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ dense (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1024\u001b[0m) │ \u001b[38;5;34m4,719,616\u001b[0m │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ dropout (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1024\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ batch_normalization_3 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1024\u001b[0m) │ \u001b[38;5;34m4,096\u001b[0m │\n",
+ "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ dense_1 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1024\u001b[0m) │ \u001b[38;5;34m1,049,600\u001b[0m │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ dropout_1 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1024\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ batch_normalization_4 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1024\u001b[0m) │ \u001b[38;5;34m4,096\u001b[0m │\n",
+ "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ dense_2 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m262,400\u001b[0m │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ dropout_2 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ batch_normalization_5 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m1,024\u001b[0m │\n",
+ "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ dense_3 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m) │ \u001b[38;5;34m1,285\u001b[0m │\n",
+ "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
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+ "
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+ ],
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+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ " Trainable params: 6,131,205 (23.39 MB)\n",
+ "
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+ ],
+ "text/plain": [
+ "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m6,131,205\u001b[0m (23.39 MB)\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
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+ {
+ "data": {
+ "text/html": [
+ " Non-trainable params: 5,056 (19.75 KB)\n",
+ "
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+ ],
+ "text/plain": [
+ "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m5,056\u001b[0m (19.75 KB)\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "model.summary()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Training the CNN Model\n",
+ "The model is trained using the training and validation data. The training process is monitored, and a learning rate scheduler is used to adjust the learning rate dynamically during training."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 1/20\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "C:\\Users\\DEEPANSHU\\anaconda3\\Lib\\site-packages\\keras\\src\\trainers\\data_adapters\\py_dataset_adapter.py:121: UserWarning: Your `PyDataset` class should call `super().__init__(**kwargs)` in its constructor. `**kwargs` can include `workers`, `use_multiprocessing`, `max_queue_size`. Do not pass these arguments to `fit()`, as they will be ignored.\n",
+ " self._warn_if_super_not_called()\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\u001b[1m157/256\u001b[0m \u001b[32m━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━\u001b[0m \u001b[1m49s\u001b[0m 500ms/step - accuracy: 0.5229 - loss: 1.3377"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "C:\\Users\\DEEPANSHU\\anaconda3\\Lib\\contextlib.py:155: UserWarning: Your input ran out of data; interrupting training. Make sure that your dataset or generator can generate at least `steps_per_epoch * epochs` batches. You may need to use the `.repeat()` function when building your dataset.\n",
+ " self.gen.throw(typ, value, traceback)\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\u001b[1m256/256\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m91s\u001b[0m 324ms/step - accuracy: 0.5454 - loss: 1.2619 - val_accuracy: 0.6116 - val_loss: 1.0092 - learning_rate: 0.0100\n",
+ "Epoch 2/20\n",
+ "\u001b[1m256/256\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m103s\u001b[0m 384ms/step - accuracy: 0.6499 - loss: 0.9221 - val_accuracy: 0.5385 - val_loss: 1.0488 - learning_rate: 0.0100\n",
+ "Epoch 3/20\n",
+ "\u001b[1m256/256\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m103s\u001b[0m 386ms/step - accuracy: 0.6694 - loss: 0.8682 - val_accuracy: 0.6964 - val_loss: 0.9067 - learning_rate: 0.0100\n",
+ "Epoch 4/20\n",
+ "\u001b[1m256/256\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m103s\u001b[0m 383ms/step - accuracy: 0.6761 - loss: 0.8556 - val_accuracy: 0.5769 - val_loss: 0.8990 - learning_rate: 0.0100\n",
+ "Epoch 5/20\n",
+ "\u001b[1m256/256\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m106s\u001b[0m 395ms/step - accuracy: 0.6960 - loss: 0.8154 - val_accuracy: 0.4799 - val_loss: 1.3479 - learning_rate: 0.0100\n",
+ "Epoch 6/20\n",
+ "\u001b[1m256/256\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m102s\u001b[0m 381ms/step - accuracy: 0.6957 - loss: 0.8010 - val_accuracy: 0.5577 - val_loss: 1.2049 - learning_rate: 0.0100\n",
+ "Epoch 7/20\n",
+ "\u001b[1m256/256\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m104s\u001b[0m 389ms/step - accuracy: 0.6986 - loss: 0.7900 - val_accuracy: 0.6942 - val_loss: 0.8291 - learning_rate: 0.0100\n",
+ "Epoch 8/20\n",
+ "\u001b[1m256/256\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m103s\u001b[0m 385ms/step - accuracy: 0.7016 - loss: 0.7891 - val_accuracy: 0.6346 - val_loss: 0.9920 - learning_rate: 0.0100\n",
+ "Epoch 9/20\n",
+ "\u001b[1m256/256\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m105s\u001b[0m 394ms/step - accuracy: 0.7109 - loss: 0.7606 - val_accuracy: 0.7121 - val_loss: 0.6805 - learning_rate: 0.0100\n",
+ "Epoch 10/20\n",
+ "\u001b[1m256/256\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m103s\u001b[0m 387ms/step - accuracy: 0.7222 - loss: 0.7268 - val_accuracy: 0.7115 - val_loss: 0.8132 - learning_rate: 0.0100\n",
+ "Epoch 11/20\n",
+ "\u001b[1m256/256\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m106s\u001b[0m 396ms/step - accuracy: 0.7127 - loss: 0.7529 - val_accuracy: 0.7969 - val_loss: 0.5509 - learning_rate: 0.0010\n",
+ "Epoch 12/20\n",
+ "\u001b[1m256/256\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m103s\u001b[0m 385ms/step - accuracy: 0.7466 - loss: 0.6743 - val_accuracy: 0.8462 - val_loss: 0.4400 - learning_rate: 0.0010\n",
+ "Epoch 13/20\n",
+ "\u001b[1m256/256\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m105s\u001b[0m 392ms/step - accuracy: 0.7516 - loss: 0.6700 - val_accuracy: 0.8080 - val_loss: 0.5094 - learning_rate: 0.0010\n",
+ "Epoch 14/20\n",
+ "\u001b[1m256/256\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m103s\u001b[0m 384ms/step - accuracy: 0.7515 - loss: 0.6670 - val_accuracy: 0.8077 - val_loss: 0.4937 - learning_rate: 0.0010\n",
+ "Epoch 15/20\n",
+ "\u001b[1m256/256\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m105s\u001b[0m 393ms/step - accuracy: 0.7529 - loss: 0.6555 - val_accuracy: 0.8237 - val_loss: 0.4842 - learning_rate: 0.0010\n",
+ "Epoch 16/20\n",
+ "\u001b[1m256/256\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m102s\u001b[0m 381ms/step - accuracy: 0.7646 - loss: 0.6375 - val_accuracy: 0.7308 - val_loss: 0.6430 - learning_rate: 0.0010\n",
+ "Epoch 17/20\n",
+ "\u001b[1m256/256\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m105s\u001b[0m 394ms/step - accuracy: 0.7601 - loss: 0.6366 - val_accuracy: 0.8348 - val_loss: 0.4645 - learning_rate: 0.0010\n",
+ "Epoch 18/20\n",
+ "\u001b[1m256/256\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m100s\u001b[0m 375ms/step - accuracy: 0.7570 - loss: 0.6484 - val_accuracy: 0.7885 - val_loss: 0.6484 - learning_rate: 0.0010\n",
+ "Epoch 19/20\n",
+ "\u001b[1m256/256\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m80s\u001b[0m 302ms/step - accuracy: 0.7581 - loss: 0.6354 - val_accuracy: 0.8080 - val_loss: 0.4724 - learning_rate: 0.0010\n",
+ "Epoch 20/20\n",
+ "\u001b[1m256/256\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m78s\u001b[0m 295ms/step - accuracy: 0.7629 - loss: 0.6385 - val_accuracy: 0.8077 - val_loss: 0.5854 - learning_rate: 0.0010\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Train the model\n",
+ "modelZ = model.fit(\n",
+ " training_set,\n",
+ " steps_per_epoch=256,\n",
+ " epochs=20,\n",
+ " validation_data=test_set,\n",
+ " validation_steps=7,\n",
+ " callbacks=[lr_schedule, lr_reduction]\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Plotting Training and Validation Accuracy\n",
+ "This section plots the training and validation accuracy and loss over time to visualize how the model performs during training."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Final Training Accuracy: 0.7583\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": [
+ ""
+ ]
+ },
+ "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
+}
diff --git a/Terrain_V6 Optimizer RMSpop-Copy1.ipynb b/Terrain_V6 Optimizer RMSpop-Copy1.ipynb
new file mode 100644
index 00000000..695ad86e
--- /dev/null
+++ b/Terrain_V6 Optimizer RMSpop-Copy1.ipynb
@@ -0,0 +1,668 @@
+{
+ "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": [
+ {
+ "data": {
+ "text/html": [
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+ "│ conv2d_3 (Conv2D) │ (None, 4, 4, 512) │ 1,180,160 │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ max_pooling2d_3 (MaxPooling2D) │ (None, 2, 2, 512) │ 0 │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
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+ "│ (BatchNormalization) │ │ │\n",
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+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ dropout_1 (Dropout) │ (None, 1024) │ 0 │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ batch_normalization_5 │ (None, 1024) │ 4,096 │\n",
+ "│ (BatchNormalization) │ │ │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ dense_2 (Dense) │ (None, 512) │ 524,800 │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ dropout_2 (Dropout) │ (None, 512) │ 0 │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ batch_normalization_6 │ (None, 512) │ 2,048 │\n",
+ "│ (BatchNormalization) │ │ │\n",
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+ "┃\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"
+ ]
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+ "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": [
+ ""
+ ]
+ },
+ "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
+}
diff --git a/Terrain_V6 Optimizer RMSpop.ipynb b/Terrain_V6 Optimizer RMSpop.ipynb
new file mode 100644
index 00000000..ae29e02d
--- /dev/null
+++ b/Terrain_V6 Optimizer RMSpop.ipynb
@@ -0,0 +1,645 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Importing necessary Libraries"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Install necessary packages and import libraries\n",
+ "# The first few lines ensure that TensorFlow, Scipy, and Matplotlib are installed.\n",
+ "# pip install tensorflow\n",
+ "# pip install scipy\n",
+ "# pip install matplotlib\n",
+ "import tensorflow as tf\n",
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Inline plotting with Matplotlib"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# %matplotlib inline\n",
+ "# this command ensures that plots are displayed directly in the notebook after being generated."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Building a Convolutional Neural Network (CNN) model for terrain classification\n",
+ "# Importing layers from Keras for building the model architecture\n",
+ "from keras.preprocessing import image\n",
+ "from keras.models import Sequential\n",
+ "from keras.layers import Conv2D\n",
+ "from keras.layers import MaxPooling2D\n",
+ "from keras.layers import Flatten\n",
+ "from keras.layers import Dense\n",
+ "from keras.layers import Dropout\n",
+ "from keras.layers import BatchNormalization\n",
+ "from keras.models import load_model\n",
+ "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
+ "from tensorflow.keras.callbacks import LearningRateScheduler, ReduceLROnPlateau\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Defining the CNN model\n",
+ "The following code defines a CNN model using TensorFlow's Keras API. The model is designed for terrain classification with a series of convolutional layers, pooling layers, and fully connected layers.\n",
+ "\n",
+ "* **Convolutional Layers:** These layers apply filters to the input image, helping to detect various features (like edges, textures, etc.).\n",
+ "* **Max Pooling:** This operation reduces the spatial dimensions (width and height) of the output from the convolutional layers, which helps in reducing computational complexity and controlling overfitting.\n",
+ "* **Batch Normalization:** This technique normalizes the outputs of a previous activation layer, which helps stabilize learning and can accelerate training.\n",
+ "* **Dropout Layers:** These layers randomly set a fraction of the input units to zero during training to help prevent overfitting.\n",
+ "* **Softmax Layer:** This is used in the output layer for multi-class classification problems, providing probabilities for each class.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Define the CNN model architecture with residual connections\n",
+ "def create_model():\n",
+ " model = tf.keras.Sequential([\n",
+ " tf.keras.layers.Input(shape=(64, 64, 3)),\n",
+ " tf.keras.layers.Conv2D(32, kernel_size=3, activation='relu'),\n",
+ " tf.keras.layers.MaxPooling2D(pool_size=2, strides=2),\n",
+ " tf.keras.layers.BatchNormalization(),\n",
+ "\n",
+ " tf.keras.layers.Conv2D(64, kernel_size=3, activation='relu'),\n",
+ " tf.keras.layers.MaxPooling2D(pool_size=2, strides=2),\n",
+ " tf.keras.layers.BatchNormalization(),\n",
+ "\n",
+ " tf.keras.layers.Conv2D(128, kernel_size=3, activation='relu'),\n",
+ " tf.keras.layers.MaxPooling2D(pool_size=2, strides=2),\n",
+ " tf.keras.layers.BatchNormalization(),\n",
+ "\n",
+ " tf.keras.layers.Flatten(),\n",
+ "\n",
+ " tf.keras.layers.Dense(1024, activation='relu'),\n",
+ " tf.keras.layers.Dropout(0.2),\n",
+ " tf.keras.layers.BatchNormalization(),\n",
+ "\n",
+ " tf.keras.layers.Dense(1024, activation='relu'),\n",
+ " tf.keras.layers.Dropout(0.2),\n",
+ " tf.keras.layers.BatchNormalization(),\n",
+ "\n",
+ " tf.keras.layers.Dense(256, activation='relu'),\n",
+ " tf.keras.layers.Dropout(0.2),\n",
+ " tf.keras.layers.BatchNormalization(),\n",
+ "\n",
+ " tf.keras.layers.Dense(5, activation='softmax')\n",
+ " ])\n",
+ " return model\n",
+ "\n",
+ "model = create_model()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Compiling the CNN model\n",
+ "The model is compiled using the Adam optimizer, with categorical crossentropy as the loss function, and accuracy as the evaluation metric."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Compile the model\n",
+ "# Categorical crossentropy is used since the labels are one-hot encoded.\n",
+ "# Adam optimizer is used for its adaptive learning rate.\n",
+ "\n",
+ "model.compile(optimizer=tf.keras.optimizers.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": [
+ {
+ "data": {
+ "text/html": [
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+ "│ (BatchNormalization) │ │ │\n",
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+ "│ flatten (Flatten) │ (None, 4608) │ 0 │\n",
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+ "│ dropout (Dropout) │ (None, 1024) │ 0 │\n",
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+ "│ (BatchNormalization) │ │ │\n",
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+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ batch_normalization_5 │ (None, 256) │ 1,024 │\n",
+ "│ (BatchNormalization) │ │ │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ dense_3 (Dense) │ (None, 5) │ 1,285 │\n",
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+ "┃\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",
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+ "│ conv2d (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m62\u001b[0m, \u001b[38;5;34m62\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m896\u001b[0m │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ max_pooling2d (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m31\u001b[0m, \u001b[38;5;34m31\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ batch_normalization │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m31\u001b[0m, \u001b[38;5;34m31\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m128\u001b[0m │\n",
+ "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ conv2d_1 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m29\u001b[0m, \u001b[38;5;34m29\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m18,496\u001b[0m │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ max_pooling2d_1 (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ batch_normalization_1 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m256\u001b[0m │\n",
+ "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ conv2d_2 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m73,856\u001b[0m │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ max_pooling2d_2 (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ batch_normalization_2 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m512\u001b[0m │\n",
+ "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ flatten (\u001b[38;5;33mFlatten\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4608\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ dense (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1024\u001b[0m) │ \u001b[38;5;34m4,719,616\u001b[0m │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ dropout (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1024\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ batch_normalization_3 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1024\u001b[0m) │ \u001b[38;5;34m4,096\u001b[0m │\n",
+ "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ dense_1 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1024\u001b[0m) │ \u001b[38;5;34m1,049,600\u001b[0m │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ dropout_1 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1024\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ batch_normalization_4 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1024\u001b[0m) │ \u001b[38;5;34m4,096\u001b[0m │\n",
+ "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
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+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "model.summary()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Training the CNN Model\n",
+ "The model is trained using the training and validation data. The training process is monitored, and a learning rate scheduler is used to adjust the learning rate dynamically during training."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 1/20\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "C:\\Users\\DEEPANSHU\\anaconda3\\Lib\\site-packages\\keras\\src\\trainers\\data_adapters\\py_dataset_adapter.py:121: UserWarning: Your `PyDataset` class should call `super().__init__(**kwargs)` in its constructor. `**kwargs` can include `workers`, `use_multiprocessing`, `max_queue_size`. Do not pass these arguments to `fit()`, as they will be ignored.\n",
+ " self._warn_if_super_not_called()\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\u001b[1m157/256\u001b[0m \u001b[32m━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━\u001b[0m \u001b[1m59s\u001b[0m 598ms/step - accuracy: 0.4809 - loss: 1.6547 "
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "C:\\Users\\DEEPANSHU\\anaconda3\\Lib\\contextlib.py:155: UserWarning: Your input ran out of data; interrupting training. Make sure that your dataset or generator can generate at least `steps_per_epoch * epochs` batches. You may need to use the `.repeat()` function when building your dataset.\n",
+ " self.gen.throw(typ, value, traceback)\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\u001b[1m256/256\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m112s\u001b[0m 383ms/step - accuracy: 0.5007 - loss: 1.5152 - val_accuracy: 0.4576 - val_loss: 3.1644 - learning_rate: 0.0100\n",
+ "Epoch 2/20\n",
+ "\u001b[1m256/256\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m106s\u001b[0m 394ms/step - accuracy: 0.6165 - loss: 1.0032 - val_accuracy: 0.5385 - val_loss: 1.2318 - learning_rate: 0.0100\n",
+ "Epoch 3/20\n",
+ "\u001b[1m256/256\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m104s\u001b[0m 392ms/step - accuracy: 0.6501 - loss: 0.9081 - val_accuracy: 0.5045 - val_loss: 1.5537 - learning_rate: 0.0100\n",
+ "Epoch 4/20\n",
+ "\u001b[1m256/256\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m104s\u001b[0m 390ms/step - accuracy: 0.6743 - loss: 0.8713 - val_accuracy: 0.6346 - val_loss: 0.8987 - learning_rate: 0.0100\n",
+ "Epoch 5/20\n",
+ "\u001b[1m256/256\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m107s\u001b[0m 401ms/step - accuracy: 0.6839 - loss: 0.8343 - val_accuracy: 0.7165 - val_loss: 0.7236 - learning_rate: 0.0100\n",
+ "Epoch 6/20\n",
+ "\u001b[1m256/256\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m105s\u001b[0m 392ms/step - accuracy: 0.6954 - loss: 0.8058 - val_accuracy: 0.6346 - val_loss: 0.9649 - learning_rate: 0.0100\n",
+ "Epoch 7/20\n",
+ "\u001b[1m256/256\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m105s\u001b[0m 393ms/step - accuracy: 0.7064 - loss: 0.7798 - val_accuracy: 0.6562 - val_loss: 1.0304 - learning_rate: 0.0100\n",
+ "Epoch 8/20\n",
+ "\u001b[1m256/256\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m104s\u001b[0m 389ms/step - accuracy: 0.7156 - loss: 0.7612 - val_accuracy: 0.7308 - val_loss: 0.7392 - learning_rate: 0.0100\n",
+ "Epoch 9/20\n",
+ "\u001b[1m256/256\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m107s\u001b[0m 401ms/step - accuracy: 0.7262 - loss: 0.7382 - val_accuracy: 0.6964 - val_loss: 0.8319 - learning_rate: 0.0100\n",
+ "Epoch 10/20\n",
+ "\u001b[1m256/256\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m104s\u001b[0m 389ms/step - accuracy: 0.7194 - loss: 0.7499 - val_accuracy: 0.7115 - val_loss: 0.7380 - learning_rate: 0.0100\n",
+ "Epoch 11/20\n",
+ "\u001b[1m256/256\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m106s\u001b[0m 397ms/step - accuracy: 0.7384 - loss: 0.6969 - val_accuracy: 0.7969 - val_loss: 0.5023 - learning_rate: 0.0010\n",
+ "Epoch 12/20\n",
+ "\u001b[1m256/256\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m105s\u001b[0m 395ms/step - accuracy: 0.7638 - loss: 0.6394 - val_accuracy: 0.7885 - val_loss: 0.5396 - learning_rate: 0.0010\n",
+ "Epoch 13/20\n",
+ "\u001b[1m256/256\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m106s\u001b[0m 400ms/step - accuracy: 0.7623 - loss: 0.6412 - val_accuracy: 0.7879 - val_loss: 0.5226 - learning_rate: 0.0010\n",
+ "Epoch 14/20\n",
+ "\u001b[1m256/256\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m104s\u001b[0m 390ms/step - accuracy: 0.7662 - loss: 0.6334 - val_accuracy: 0.8462 - val_loss: 0.4046 - learning_rate: 0.0010\n",
+ "Epoch 15/20\n",
+ "\u001b[1m256/256\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m105s\u001b[0m 394ms/step - accuracy: 0.7693 - loss: 0.6308 - val_accuracy: 0.8348 - val_loss: 0.4446 - learning_rate: 0.0010\n",
+ "Epoch 16/20\n",
+ "\u001b[1m256/256\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m103s\u001b[0m 387ms/step - accuracy: 0.7666 - loss: 0.6179 - val_accuracy: 0.7308 - val_loss: 0.7355 - learning_rate: 0.0010\n",
+ "Epoch 17/20\n",
+ "\u001b[1m256/256\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m106s\u001b[0m 396ms/step - accuracy: 0.7636 - loss: 0.6286 - val_accuracy: 0.8371 - val_loss: 0.4924 - learning_rate: 0.0010\n",
+ "Epoch 18/20\n",
+ "\u001b[1m256/256\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m86s\u001b[0m 320ms/step - accuracy: 0.7753 - loss: 0.6065 - val_accuracy: 0.8846 - val_loss: 0.3848 - learning_rate: 0.0010\n",
+ "Epoch 19/20\n",
+ "\u001b[1m256/256\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m78s\u001b[0m 293ms/step - accuracy: 0.7759 - loss: 0.6039 - val_accuracy: 0.8192 - val_loss: 0.5040 - learning_rate: 0.0010\n",
+ "Epoch 20/20\n",
+ "\u001b[1m256/256\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m69s\u001b[0m 258ms/step - accuracy: 0.7771 - loss: 0.6035 - val_accuracy: 0.8846 - val_loss: 0.3493 - learning_rate: 0.0010\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Train the model\n",
+ "modelZ = model.fit(\n",
+ " training_set,\n",
+ " steps_per_epoch=256,\n",
+ " epochs=20,\n",
+ " validation_data=test_set,\n",
+ " validation_steps=7,\n",
+ " callbacks=[lr_schedule, lr_reduction]\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Plotting Training and Validation Accuracy\n",
+ "This section plots the training and validation accuracy and loss over time to visualize how the model performs during training."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Final Training Accuracy: 0.7767\n",
+ "Final Validation Accuracy: 0.8846\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": [
+ ""
+ ]
+ },
+ "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
+}
diff --git a/Terrain_V7 Optimizer Adagrad.ipynb b/Terrain_V7 Optimizer Adagrad.ipynb
new file mode 100644
index 00000000..89360bd3
--- /dev/null
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+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Importing necessary Libraries"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Install necessary packages and import libraries\n",
+ "# The first few lines ensure that TensorFlow, Scipy, and Matplotlib are installed.\n",
+ "# pip install tensorflow\n",
+ "# pip install scipy\n",
+ "# pip install matplotlib\n",
+ "import tensorflow as tf\n",
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Inline plotting with Matplotlib"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# %matplotlib inline\n",
+ "# this command ensures that plots are displayed directly in the notebook after being generated."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Building a Convolutional Neural Network (CNN) model for terrain classification\n",
+ "# Importing layers from Keras for building the model architecture\n",
+ "from keras.preprocessing import image\n",
+ "from keras.models import Sequential\n",
+ "from keras.layers import Conv2D\n",
+ "from keras.layers import MaxPooling2D\n",
+ "from keras.layers import Flatten\n",
+ "from keras.layers import Dense\n",
+ "from keras.layers import Dropout\n",
+ "from keras.layers import BatchNormalization\n",
+ "from keras.models import load_model\n",
+ "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
+ "from tensorflow.keras.callbacks import LearningRateScheduler, ReduceLROnPlateau\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Defining the CNN model\n",
+ "The following code defines a CNN model using TensorFlow's Keras API. The model is designed for terrain classification with a series of convolutional layers, pooling layers, and fully connected layers.\n",
+ "\n",
+ "* **Convolutional Layers:** These layers apply filters to the input image, helping to detect various features (like edges, textures, etc.).\n",
+ "* **Max Pooling:** This operation reduces the spatial dimensions (width and height) of the output from the convolutional layers, which helps in reducing computational complexity and controlling overfitting.\n",
+ "* **Batch Normalization:** This technique normalizes the outputs of a previous activation layer, which helps stabilize learning and can accelerate training.\n",
+ "* **Dropout Layers:** These layers randomly set a fraction of the input units to zero during training to help prevent overfitting.\n",
+ "* **Softmax Layer:** This is used in the output layer for multi-class classification problems, providing probabilities for each class.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Define the CNN model architecture with residual connections\n",
+ "def create_model():\n",
+ " model = tf.keras.Sequential([\n",
+ " tf.keras.layers.Input(shape=(64, 64, 3)),\n",
+ " tf.keras.layers.Conv2D(32, kernel_size=3, activation='relu'),\n",
+ " tf.keras.layers.MaxPooling2D(pool_size=2, strides=2),\n",
+ " tf.keras.layers.BatchNormalization(),\n",
+ "\n",
+ " tf.keras.layers.Conv2D(64, kernel_size=3, activation='relu'),\n",
+ " tf.keras.layers.MaxPooling2D(pool_size=2, strides=2),\n",
+ " tf.keras.layers.BatchNormalization(),\n",
+ "\n",
+ " tf.keras.layers.Conv2D(128, kernel_size=3, activation='relu'),\n",
+ " tf.keras.layers.MaxPooling2D(pool_size=2, strides=2),\n",
+ " tf.keras.layers.BatchNormalization(),\n",
+ "\n",
+ " tf.keras.layers.Flatten(),\n",
+ "\n",
+ " tf.keras.layers.Dense(1024, activation='relu'),\n",
+ " tf.keras.layers.Dropout(0.2),\n",
+ " tf.keras.layers.BatchNormalization(),\n",
+ "\n",
+ " tf.keras.layers.Dense(1024, activation='relu'),\n",
+ " tf.keras.layers.Dropout(0.2),\n",
+ " tf.keras.layers.BatchNormalization(),\n",
+ "\n",
+ " tf.keras.layers.Dense(256, activation='relu'),\n",
+ " tf.keras.layers.Dropout(0.2),\n",
+ " tf.keras.layers.BatchNormalization(),\n",
+ "\n",
+ " tf.keras.layers.Dense(5, activation='softmax')\n",
+ " ])\n",
+ " return model\n",
+ "\n",
+ "model = create_model()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Compiling the CNN model\n",
+ "The model is compiled using the Adam optimizer, with categorical crossentropy as the loss function, and accuracy as the evaluation metric."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Compile the model\n",
+ "# Categorical crossentropy is used since the labels are one-hot encoded.\n",
+ "# Adam optimizer is used for its adaptive learning rate.\n",
+ "\n",
+ "model.compile(optimizer=tf.keras.optimizers.Adagrad(learning_rate=0.01), loss='categorical_crossentropy', metrics=['accuracy'])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Data Augmentation and Preprocessing\n",
+ "This section applies data augmentation techniques using the `ImageDataGenerator` from Keras to generate more training samples and preprocess the images. These operations include rescaling, rotating, shifting, and flipping.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Data augmentation and preprocessing for training and validation datasets\n",
+ "# The ImageDataGenerator applies various transformations to augment the training data\n",
+ "# Data augmentation for training dataset\n",
+ "train_datagen = ImageDataGenerator(\n",
+ " rescale=1./255,\n",
+ " shear_range=0.2,\n",
+ " zoom_range=0.2,\n",
+ " horizontal_flip=True,\n",
+ " rotation_range=30,\n",
+ " brightness_range=[0.8, 1.2],\n",
+ " width_shift_range=0.2,\n",
+ " height_shift_range=0.2\n",
+ ")\n",
+ "\n",
+ "# Validation dataset (only rescaling)\n",
+ "test_datagen = ImageDataGenerator(rescale=1./255)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Loading and Preparing the Data\n",
+ "In this section, the training and validation data are loaded from the respective directories, and the images are prepared for the model using the augmentation techniques defined earlier.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Found 10000 images belonging to 5 classes.\n",
+ "Found 500 images belonging to 5 classes.\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Assuming you've created 'train_subset' and 'test_subset' directories\n",
+ "training_set = train_datagen.flow_from_directory(\n",
+ " r\"D:\\Jupyter Notebook\\Contribution\\Terrain-Recognition\\Data Main\\Training Data\",\n",
+ " target_size=(64, 64),\n",
+ " batch_size=64,\n",
+ " class_mode='categorical'\n",
+ ")\n",
+ "\n",
+ "test_set = test_datagen.flow_from_directory(\n",
+ " r\"D:\\Jupyter Notebook\\Contribution\\Terrain-Recognition\\Data Main\\Testing Data\",\n",
+ " target_size=(64, 64),\n",
+ " batch_size=64,\n",
+ " class_mode='categorical'\n",
+ ")\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Define learning rate scheduler\n",
+ "def scheduler(epoch, lr):\n",
+ " if epoch < 10:\n",
+ " return 1e-2\n",
+ " else:\n",
+ " return 1e-3\n",
+ "\n",
+ "lr_schedule = LearningRateScheduler(scheduler)\n",
+ "lr_reduction = ReduceLROnPlateau(monitor='val_loss', factor=0.5, patience=3, min_lr=1e-6)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "Model: \"sequential\"\n",
+ "
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+ "text/plain": [
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+ },
+ "metadata": {},
+ "output_type": "display_data"
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+ "data": {
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+ "┃ Layer (type) ┃ Output Shape ┃ Param # ┃\n",
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+ "│ (BatchNormalization) │ │ │\n",
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+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ max_pooling2d_1 (MaxPooling2D) │ (None, 14, 14, 64) │ 0 │\n",
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+ "│ (BatchNormalization) │ │ │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ conv2d_2 (Conv2D) │ (None, 12, 12, 128) │ 73,856 │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ max_pooling2d_2 (MaxPooling2D) │ (None, 6, 6, 128) │ 0 │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ batch_normalization_2 │ (None, 6, 6, 128) │ 512 │\n",
+ "│ (BatchNormalization) │ │ │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ flatten (Flatten) │ (None, 4608) │ 0 │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ dense (Dense) │ (None, 1024) │ 4,719,616 │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ dropout (Dropout) │ (None, 1024) │ 0 │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ batch_normalization_3 │ (None, 1024) │ 4,096 │\n",
+ "│ (BatchNormalization) │ │ │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ dense_1 (Dense) │ (None, 1024) │ 1,049,600 │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ dropout_1 (Dropout) │ (None, 1024) │ 0 │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ batch_normalization_4 │ (None, 1024) │ 4,096 │\n",
+ "│ (BatchNormalization) │ │ │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ dense_2 (Dense) │ (None, 256) │ 262,400 │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ dropout_2 (Dropout) │ (None, 256) │ 0 │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ batch_normalization_5 │ (None, 256) │ 1,024 │\n",
+ "│ (BatchNormalization) │ │ │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ dense_3 (Dense) │ (None, 5) │ 1,285 │\n",
+ "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n",
+ "
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+ ],
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+ "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
+ "┃\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
+ "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
+ "│ conv2d (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m62\u001b[0m, \u001b[38;5;34m62\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m896\u001b[0m │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ max_pooling2d (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m31\u001b[0m, \u001b[38;5;34m31\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ batch_normalization │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m31\u001b[0m, \u001b[38;5;34m31\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m128\u001b[0m │\n",
+ "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ conv2d_1 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m29\u001b[0m, \u001b[38;5;34m29\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m18,496\u001b[0m │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ max_pooling2d_1 (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ batch_normalization_1 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m256\u001b[0m │\n",
+ "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ conv2d_2 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m73,856\u001b[0m │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ max_pooling2d_2 (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ batch_normalization_2 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m512\u001b[0m │\n",
+ "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ flatten (\u001b[38;5;33mFlatten\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4608\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ dense (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1024\u001b[0m) │ \u001b[38;5;34m4,719,616\u001b[0m │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ dropout (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1024\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ batch_normalization_3 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1024\u001b[0m) │ \u001b[38;5;34m4,096\u001b[0m │\n",
+ "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ dense_1 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1024\u001b[0m) │ \u001b[38;5;34m1,049,600\u001b[0m │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ dropout_1 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1024\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ batch_normalization_4 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1024\u001b[0m) │ \u001b[38;5;34m4,096\u001b[0m │\n",
+ "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ dense_2 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m262,400\u001b[0m │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ dropout_2 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ batch_normalization_5 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m1,024\u001b[0m │\n",
+ "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
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+ "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n"
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+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "model.summary()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Training the CNN Model\n",
+ "The model is trained using the training and validation data. The training process is monitored, and a learning rate scheduler is used to adjust the learning rate dynamically during training."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 1/20\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "C:\\Users\\DEEPANSHU\\anaconda3\\Lib\\site-packages\\keras\\src\\trainers\\data_adapters\\py_dataset_adapter.py:121: UserWarning: Your `PyDataset` class should call `super().__init__(**kwargs)` in its constructor. `**kwargs` can include `workers`, `use_multiprocessing`, `max_queue_size`. Do not pass these arguments to `fit()`, as they will be ignored.\n",
+ " self._warn_if_super_not_called()\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\u001b[1m157/256\u001b[0m \u001b[32m━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━\u001b[0m \u001b[1m1:04\u001b[0m 651ms/step - accuracy: 0.5264 - loss: 1.3535"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "C:\\Users\\DEEPANSHU\\anaconda3\\Lib\\contextlib.py:155: UserWarning: Your input ran out of data; interrupting training. Make sure that your dataset or generator can generate at least `steps_per_epoch * epochs` batches. You may need to use the `.repeat()` function when building your dataset.\n",
+ " self.gen.throw(typ, value, traceback)\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\u001b[1m256/256\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m124s\u001b[0m 418ms/step - accuracy: 0.5467 - loss: 1.2821 - val_accuracy: 0.5179 - val_loss: 1.3052 - learning_rate: 0.0100\n",
+ "Epoch 2/20\n",
+ "\u001b[1m256/256\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m102s\u001b[0m 384ms/step - accuracy: 0.6345 - loss: 0.9802 - val_accuracy: 0.7308 - val_loss: 0.8200 - learning_rate: 0.0100\n",
+ "Epoch 3/20\n",
+ "\u001b[1m256/256\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m106s\u001b[0m 394ms/step - accuracy: 0.6709 - loss: 0.8821 - val_accuracy: 0.6763 - val_loss: 0.8596 - learning_rate: 0.0100\n",
+ "Epoch 4/20\n",
+ "\u001b[1m256/256\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m103s\u001b[0m 383ms/step - accuracy: 0.6755 - loss: 0.8636 - val_accuracy: 0.6731 - val_loss: 0.7199 - learning_rate: 0.0100\n",
+ "Epoch 5/20\n",
+ "\u001b[1m256/256\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m104s\u001b[0m 390ms/step - accuracy: 0.6896 - loss: 0.8146 - val_accuracy: 0.7232 - val_loss: 0.7290 - learning_rate: 0.0100\n",
+ "Epoch 6/20\n",
+ "\u001b[1m256/256\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m104s\u001b[0m 389ms/step - accuracy: 0.6981 - loss: 0.7953 - val_accuracy: 0.7885 - val_loss: 0.7248 - learning_rate: 0.0100\n",
+ "Epoch 7/20\n",
+ "\u001b[1m256/256\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m105s\u001b[0m 395ms/step - accuracy: 0.7057 - loss: 0.7830 - val_accuracy: 0.7567 - val_loss: 0.6310 - learning_rate: 0.0100\n",
+ "Epoch 8/20\n",
+ "\u001b[1m256/256\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m104s\u001b[0m 387ms/step - accuracy: 0.7117 - loss: 0.7677 - val_accuracy: 0.8077 - val_loss: 0.4620 - learning_rate: 0.0100\n",
+ "Epoch 9/20\n",
+ "\u001b[1m256/256\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m104s\u001b[0m 391ms/step - accuracy: 0.7104 - loss: 0.7556 - val_accuracy: 0.7768 - val_loss: 0.6346 - learning_rate: 0.0100\n",
+ "Epoch 10/20\n",
+ "\u001b[1m256/256\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m102s\u001b[0m 385ms/step - accuracy: 0.7195 - loss: 0.7292 - val_accuracy: 0.7692 - val_loss: 0.4764 - learning_rate: 0.0100\n",
+ "Epoch 11/20\n",
+ "\u001b[1m256/256\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m102s\u001b[0m 383ms/step - accuracy: 0.7326 - loss: 0.7125 - val_accuracy: 0.7902 - val_loss: 0.5590 - learning_rate: 0.0010\n",
+ "Epoch 12/20\n",
+ "\u001b[1m256/256\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m102s\u001b[0m 382ms/step - accuracy: 0.7330 - loss: 0.7010 - val_accuracy: 0.7885 - val_loss: 0.5082 - learning_rate: 0.0010\n",
+ "Epoch 13/20\n",
+ "\u001b[1m256/256\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m105s\u001b[0m 391ms/step - accuracy: 0.7387 - loss: 0.6946 - val_accuracy: 0.7902 - val_loss: 0.5516 - learning_rate: 0.0010\n",
+ "Epoch 14/20\n",
+ "\u001b[1m256/256\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m103s\u001b[0m 386ms/step - accuracy: 0.7348 - loss: 0.6986 - val_accuracy: 0.8077 - val_loss: 0.5521 - learning_rate: 0.0010\n",
+ "Epoch 15/20\n",
+ "\u001b[1m256/256\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m105s\u001b[0m 395ms/step - accuracy: 0.7415 - loss: 0.6876 - val_accuracy: 0.7946 - val_loss: 0.5503 - learning_rate: 0.0010\n",
+ "Epoch 16/20\n",
+ "\u001b[1m256/256\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m103s\u001b[0m 385ms/step - accuracy: 0.7414 - loss: 0.6864 - val_accuracy: 0.8077 - val_loss: 0.5322 - learning_rate: 0.0010\n",
+ "Epoch 17/20\n",
+ "\u001b[1m256/256\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m105s\u001b[0m 392ms/step - accuracy: 0.7325 - loss: 0.7018 - val_accuracy: 0.7969 - val_loss: 0.5588 - learning_rate: 0.0010\n",
+ "Epoch 18/20\n",
+ "\u001b[1m256/256\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m81s\u001b[0m 301ms/step - accuracy: 0.7341 - loss: 0.6869 - val_accuracy: 0.7885 - val_loss: 0.4976 - learning_rate: 0.0010\n",
+ "Epoch 19/20\n",
+ "\u001b[1m256/256\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m79s\u001b[0m 299ms/step - accuracy: 0.7314 - loss: 0.6996 - val_accuracy: 0.8058 - val_loss: 0.5465 - learning_rate: 0.0010\n",
+ "Epoch 20/20\n",
+ "\u001b[1m256/256\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m59s\u001b[0m 218ms/step - accuracy: 0.7437 - loss: 0.6892 - val_accuracy: 0.7692 - val_loss: 0.5833 - learning_rate: 0.0010\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Train the model\n",
+ "modelZ = model.fit(\n",
+ " training_set,\n",
+ " steps_per_epoch=256,\n",
+ " epochs=20,\n",
+ " validation_data=test_set,\n",
+ " validation_steps=7,\n",
+ " callbacks=[lr_schedule, lr_reduction]\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Plotting Training and Validation Accuracy\n",
+ "This section plots the training and validation accuracy and loss over time to visualize how the model performs during training."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Final Training Accuracy: 0.7439\n",
+ "Final Validation Accuracy: 0.7692\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Print the final training and validation accuracy\n",
+ "train_accuracy = modelZ.history['accuracy'][-1]\n",
+ "val_accuracy = modelZ.history['val_accuracy'][-1]\n",
+ "\n",
+ "print(f\"Final Training Accuracy: {train_accuracy:.4f}\")\n",
+ "print(f\"Final Validation Accuracy: {val_accuracy:.4f}\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "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
+}