train.py

config = {
           'data': data,
           'train_test_ratio': 0.2,
           'model_config': {
               'model_name': 'house_pricing_model',
               'layers': {
                        'first_layer': 12, 
                        'second_layer': 5, 
                        'output_layer': 1
                       },
                'activations': ['relu', 'relu', None],
                'loss_function': 'mse',
                'optimiser': 'adam',
                'metrics': ['mae']
                },
            'training_config': {
                'batch_size': 10,
                'epochs': 8
            }
           }

def train(model, config):
    """
        Training function over data.
        It takes a compiled model and trains it over data.
        
        Returns:
            model.history
    """
    batch_size, epochs = config['training_config']['batch_size'], config['training_config']['epochs']
    X_train, X_test, Y_train, Y_test = import_data(config)
    
    hist = model.fit(X_train, Y_train, batch_size=batch_size, epochs=epochs, validation_data=(X_test, Y_test))
    return hist