train_cifar10.py

import os
import tensorflow as tf
from tensorflow.keras import layers, models, callbacks
import matplotlib.pyplot as plt
import numpy as np

# Set random seed for reproducibility
tf.random.set_seed(42)
np.random.seed(42)

def load_and_preprocess_data():
    """Loads CIFAR-10 dataset and normalizes pixel values."""
    print("Loading CIFAR-10 dataset...")
    (train_images, train_labels), (test_images, test_labels) = tf.keras.datasets.cifar10.load_data()

    # Normalize pixel values to be between 0 and 1
    train_images, test_images = train_images / 255.0, test_images / 255.0
    
    print(f"Train images shape: {train_images.shape}")
    print(f"Test images shape: {test_images.shape}")
    
    return (train_images, train_labels), (test_images, test_labels)

def build_cnn_model():
    """Defines a robust CNN architecture for CIFAR-10."""
    print("Building CNN architecture...")
    model = models.Sequential([
        # Block 1
        layers.Conv2D(32, (3, 3), padding='same', activation='relu', input_shape=(32, 32, 3)),
        layers.BatchNormalization(),
        layers.Conv2D(32, (3, 3), padding='same', activation='relu'),
        layers.BatchNormalization(),
        layers.MaxPooling2D((2, 2)),
        layers.Dropout(0.2),

        # Block 2
        layers.Conv2D(64, (3, 3), padding='same', activation='relu'),
        layers.BatchNormalization(),
        layers.Conv2D(64, (3, 3), padding='same', activation='relu'),
        layers.BatchNormalization(),
        layers.MaxPooling2D((2, 2)),
        layers.Dropout(0.3),

        # Block 3
        layers.Conv2D(128, (3, 3), padding='same', activation='relu'),
        layers.BatchNormalization(),
        layers.Conv2D(128, (3, 3), padding='same', activation='relu'),
        layers.BatchNormalization(),
        layers.MaxPooling2D((2, 2)),
        layers.Dropout(0.4),

        # Classification Head
        layers.Flatten(),
        layers.Dense(128, activation='relu'),
        layers.BatchNormalization(),
        layers.Dropout(0.5),
        layers.Dense(10, activation='softmax')
    ])

    model.compile(optimizer='adam',
                  loss='sparse_categorical_crossentropy',
                  metrics=['accuracy'])
    
    return model

def train_and_evaluate():
    # 1. Prepare Data
    (train_images, train_labels), (test_images, test_labels) = load_and_preprocess_data()
    
    # 2. Build Model
    model = build_cnn_model()
    model.summary()

    # 3. Callbacks
    early_stop = callbacks.EarlyStopping(monitor='val_loss', patience=10, restore_best_weights=True)
    reduce_lr = callbacks.ReduceLROnPlateau(monitor='val_loss', factor=0.5, patience=3, min_lr=1e-6)

    # 4. Train
    print("\nStarting training (limited to 2 epochs for demonstration)...")
    history = model.fit(
        train_images, train_labels,
        epochs=2, # Increase to 50 for full training
        batch_size=64,
        validation_data=(test_images, test_labels),
        callbacks=[early_stop, reduce_lr]
    )

    # 5. Evaluate
    print("\nEvaluating model...")
    test_loss, test_acc = model.evaluate(test_images, test_labels, verbose=2)
    print(f"\nFinal Test Accuracy: {test_acc*100:.2f}%")

    # 6. Save Model
    model.save('cifar10_cnn_v1.h5')
    print("Model saved to cifar10_cnn_v1.h5")

    return history

def plot_results(history):
    """Visualizes training 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, 5))
    
    plt.subplot(1, 2, 1)
    plt.plot(epochs_range, acc, label='Training Accuracy')
    plt.plot(epochs_range, val_acc, label='Validation Accuracy')
    plt.title('Accuracy')
    plt.legend()

    plt.subplot(1, 2, 2)
    plt.plot(epochs_range, loss, label='Training Loss')
    plt.plot(epochs_range, val_loss, label='Validation Loss')
    plt.title('Loss')
    plt.legend()

    plt.savefig('training_plot.png')
    print("Training plots saved as training_plot.png")

if __name__ == "__main__":
    try:
        hist = train_and_evaluate()
        plot_results(hist)
    except Exception as e:
        print(f"\n[ERROR] An error occurred: {e}")
        print("\nNote: If you encounter DLL errors or ModuleNotFound errors, please ensure "
              "TensorFlow is correctly installed in your environment (e.g., pip install tensorflow).")