Cell 18

model.fit(x= x_train,y = y_train, batch_size = 128, epochs = 100, validation_data = (x_test,y_test)) 

What the code could have been:

from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint
from tensorflow.keras.optimizers import Adam

# Compile the model with the specified optimizer and loss function
def compile_model(model):
    """Compiles the model with the Adam optimizer and sparse categorical crossentropy loss."""
    model.compile(
        optimizer=Adam(lr=1e-3),
        loss='sparse_categorical_crossentropy',
        metrics=['accuracy']
    )
    return model

# Define early stopping and model checkpoint callbacks
def get_callbacks(checkpoint_path):
    """Returns early stopping and model checkpoint callbacks."""
    early_stopping = EarlyStopping(monitor='val_loss', patience=10, restore_best_weights=True)
    model_checkpoint = ModelCheckpoint(
        filepath=checkpoint_path,
        save_best_only=True,
        monitor='val_loss',
        mode='min',
        verbose=1
    )
    return early_stopping, model_checkpoint

# Train the model
def train_model(
    model,
    x_train: tf.Tensor,
    y_train: tf.Tensor,
    x_test: tf.Tensor,
    y_test: tf.Tensor,
    batch_size: int,
    epochs: int,
    checkpoint_path: str
):
    """Trains the model on the training data and evaluates its performance on the test data."""
    # Compiles the model
    model = compile_model(model)
    
    # Define early stopping and model checkpoint callbacks
    early_stopping, model_checkpoint = get_callbacks(checkpoint_path)
    
    # Train the model
    history = model.fit(
        x_train,
        y_train,
        batch_size=batch_size,
        epochs=epochs,
        validation_data=(x_test, y_test),
        callbacks=[early_stopping, model_checkpoint]
    )
    
    return history

# Example usage
model =...  # Define your model here
history = train_model(
    model,
    x_train,
    y_train,
    x_test,
    y_test,
    batch_size=128,
    epochs=100,
    checkpoint_path='./checkpoints/model.h5'
)