cnn_mnist.py: For the Convolutional Neural Network (CNN) model.

Author: asRot0

machine_learning/neural_networks/cnn_mnist.py

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+"""
+Convolutional Neural Network (CNN) for MNIST Classification
+
+Goal: This script builds a deep CNN to classify the MNIST dataset using TensorFlow and Keras. It leverages
+    convolutional layers for feature extraction and pooling layers for down-sampling, followed by fully connected layers
+    for classification.
+
+Objectives:
+- Load and preprocess MNIST data (reshape for CNN input).
+- Build a CNN with multiple convolutional, pooling, and batch normalization layers.
+- Train the CNN, evaluate its accuracy, and display model performance.
+
+"""
+
+
+import tensorflow as tf
+from tensorflow.keras import layers, models
+from tensorflow.keras.datasets import mnist
+from tensorflow.keras.utils import to_categorical
+
+# Load and preprocess the MNIST data
+(X_train, y_train), (X_test, y_test) = mnist.load_data()
+
+# Normalize the pixel values (0 to 1)
+X_train = X_train.reshape(-1, 28, 28, 1).astype('float32') / 255
+X_test = X_test.reshape(-1, 28, 28, 1).astype('float32') / 255
+
+# Convert labels to one-hot encoding
+y_train = to_categorical(y_train, 10)
+y_test = to_categorical(y_test, 10)
+
+# Building the CNN model
+model = models.Sequential()
+
+# 1st Convolutional Layer
+model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)))
+model.add(layers.MaxPooling2D((2, 2)))
+model.add(layers.BatchNormalization())
+
+# 2nd Convolutional Layer
+model.add(layers.Conv2D(64, (3, 3), activation='relu'))
+model.add(layers.MaxPooling2D((2, 2)))
+model.add(layers.BatchNormalization())
+
+# 3rd Convolutional Layer
+model.add(layers.Conv2D(128, (3, 3), activation='relu'))
+model.add(layers.BatchNormalization())
+
+# Flattening the data before fully connected layers
+model.add(layers.Flatten())
+
+# Fully Connected (Dense) Layer with Dropout for regularization
+model.add(layers.Dense(128, activation='relu'))
+model.add(layers.Dropout(0.5))
+
+# Output Layer for classification
+model.add(layers.Dense(10, activation='softmax'))
+
+# Compile the model
+model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
+
+# Display the model summary
+model.summary()
+
+# Train the model
+history = model.fit(X_train, y_train, epochs=5, batch_size=32, validation_data=(X_test, y_test))
+
+# Evaluate the model on test data
+test_loss, test_acc = model.evaluate(X_test, y_test, verbose=2)
+print(f'\nTest accuracy: {test_acc}')