components/training/code/train.py

import argparse
import os
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
import pathlib

def load_dataset(folder_path, img_size=(180, 180), batch_size=32):
    """Loads image dataset from folder using tf.keras.utils.image_dataset_from_directory"""

    folder_path = pathlib.Path(folder_path)

    if not folder_path.exists():
        raise ValueError(f"Folder does not exist: {folder_path}")

    dataset = tf.keras.utils.image_dataset_from_directory(
        folder_path,
        labels='inferred',
        label_mode='categorical',
        shuffle=True,
        batch_size=batch_size,
        image_size=img_size
    )
    return dataset


def build_model(input_shape=(180, 180, 3), num_classes=4, learning_rate=0.001):
    """Builds a simple CNN for Alzheimer's classification"""

    model = keras.Sequential([
        layers.Rescaling(1./255, input_shape=input_shape),

        layers.Conv2D(32, 3, activation="relu"),
        layers.MaxPooling2D(),

        layers.Conv2D(64, 3, activation="relu"),
        layers.MaxPooling2D(),

        layers.Conv2D(128, 3, activation="relu"),
        layers.MaxPooling2D(),

        layers.Flatten(),
        layers.Dense(128, activation="relu"),
        layers.Dropout(0.3),

        layers.Dense(num_classes, activation="softmax")
    ])

    model.compile(
        optimizer=keras.optimizers.Adam(learning_rate=learning_rate),
        loss="categorical_crossentropy",
        metrics=["accuracy"]
    )

    return model


def main():
    parser = argparse.ArgumentParser(description="Train CNN on Alzheimer's MRI Dataset")

    parser.add_argument("--training_folder", type=str, required=True)
    parser.add_argument("--testing_folder", type=str, required=True)
    parser.add_argument("--output_folder", type=str, required=True)

    parser.add_argument("--epochs", type=int, default=5)
    parser.add_argument("--learning_rate", type=float, default=0.01)
    parser.add_argument("--batch_size", type=int, default=32)
    parser.add_argument("--patience", type=int, default=10)
    parser.add_argument("--model_name", type=str, default="alzheimers-cnn")

    args = parser.parse_args()

    print("\n=== LOADING DATASETS ===")
    train_ds = load_dataset(args.training_folder, batch_size=args.batch_size)
    val_ds = load_dataset(args.testing_folder, batch_size=args.batch_size)

    class_names = train_ds.class_names
    num_classes = len(class_names)

    print(f"Detected classes: {class_names}")
    print(f"Number of classes: {num_classes}")

    print("\n=== BUILDING MODEL ===")
    model = build_model(num_classes=num_classes, learning_rate=args.learning_rate)
    model.summary()

    print("\n=== TRAINING STARTED ===")

    early_stop = keras.callbacks.EarlyStopping(
        patience=args.patience,
        restore_best_weights=True
    )

    history = model.fit(
        train_ds,
        validation_data=val_ds,
        epochs=args.epochs,
        callbacks=[early_stop]
    )

    print("\n=== TRAINING COMPLETED ===")

    os.makedirs(args.output_folder, exist_ok=True)
    output_path = os.path.join(args.output_folder, f"{args.model_name}.h5")

    print(f"Saving model to: {output_path}")
    model.save(output_path)

    print("\n✔ Model saved successfully!")
    print("✔ Training finished!")


if __name__ == "__main__":
    main()