Changes made

machine_learning/Cnn.py

@@ -0,0 +1,89 @@
+import tensorflow as tf
+from tensorflow.keras import layers, models
+from tensorflow.keras.callbacks import EarlyStopping
+from tensorflow.keras.preprocessing.image import ImageDataGenerator
+import numpy as np
+import matplotlib.pyplot as plt
+
+# Load and preprocess dataset (using MNIST dataset as an example)
+mnist = tf.keras.datasets.mnist
+(train_images, train_labels), (test_images, test_labels) = mnist.load_data()
+
+# Reshape and normalize the data
+train_images = train_images.reshape((60000, 28, 28, 1)).astype('float32') / 255
+test_images = test_images.reshape((10000, 28, 28, 1)).astype('float32') / 255
+
+# Data Augmentation
+datagen = ImageDataGenerator(
+    rotation_range=10,
+    width_shift_range=0.1,
+    height_shift_range=0.1,
+    zoom_range=0.1
+)
+datagen.fit(train_images)
+
+# Model building
+model = models.Sequential()
+
+# Adding Convolutional layers
+model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)))
+model.add(layers.MaxPooling2D((2, 2)))
+
+model.add(layers.Conv2D(64, (3, 3), activation='relu'))
+model.add(layers.MaxPooling2D((2, 2)))
+
+model.add(layers.Conv2D(64, (3, 3), activation='relu'))
+
+# Flattening and adding Dense layers
+model.add(layers.Flatten())
+model.add(layers.Dense(64, activation='relu'))
+model.add(layers.Dropout(0.5))  # Dropout to prevent overfitting
+
+# Output layer
+model.add(layers.Dense(10, activation='softmax'))
+
+# Compile the model
+model.compile(optimizer='adam',
+              loss='sparse_categorical_crossentropy',
+              metrics=['accuracy'])
+
+# Early stopping to avoid overfitting
+early_stopping = EarlyStopping(monitor='val_loss', patience=3, restore_best_weights=True)
+
+# Train the model with data augmentation and early stopping
+history = model.fit(
+    datagen.flow(train_images, train_labels, batch_size=32),
+    epochs=10,
+    validation_data=(test_images, test_labels),
+    callbacks=[early_stopping]
+)
+
+# Plotting training and validation accuracy/loss
+def plot_history(history):
+    acc = history.history['accuracy']
+    val_acc = history.history['val_accuracy']
+    loss = history.history['loss']
+    val_loss = history.history['val_loss']
+
+    epochs_range = range(len(acc))
+
+    plt.figure(figsize=(12, 6))
+
+    # Plot accuracy
+    plt.subplot(1, 2, 1)
+    plt.plot(epochs_range, acc, label='Training Accuracy')
+    plt.plot(epochs_range, val_acc, label='Validation Accuracy')
+    plt.legend(loc='lower right')
+    plt.title('Training and Validation Accuracy')
+
+    # Plot loss
+    plt.subplot(1, 2, 2)
+    plt.plot(epochs_range, loss, label='Training Loss')
+    plt.plot(epochs_range, val_loss, label='Validation Loss')
+    plt.legend(loc='upper right')
+    plt.title('Training and Validation Loss')
+
+    plt.show()
+
+# Call the plot function to visualize training progress
+plot_history(history)