model_performance.py

es = EarlyStopping(monitor = 'val_accuracy', patience = 5)

# Run the model for a batch size of 5 for 100 epochs
history = model.fit(X_train, 
                    y_train, 
                    validation_data = (X_test, y_test),
                    batch_size = 5,
                    epochs = 100,
                    callbacks = es
                   )

# Function to plot "accuracy vs epoch" graphs and "loss vs epoch" graphs for training and validation data
def plot_metrics(model_name, metric = 'accuracy'):
    if metric == 'loss':
        plt.title("Loss Values")
        plt.plot(model_name.history['loss'], label = 'train')
        plt.plot(model_name.history['val_loss'], label = 'test')
        plt.legend()
        plt.show()
    else:
        plt.title("Accuracy Values")
        plt.plot(model_name.history['accuracy'], label='train') 
        plt.plot(model_name.history['val_accuracy'], label='test') 
        plt.legend()
        plt.show()

        
plot_metrics(history, 'accuracy')
plot_metrics(history, 'loss')