Skopt Tutorial - Fitness Function

skopt_fitness.py

@use_named_args(dimensions=dimensions)
def fitness(learning_rate, num_dense_layers, num_input_nodes, 
            num_dense_nodes,activation, batch_size,adam_decay):

    model = create_model(learning_rate=learning_rate,
                         num_dense_layers=num_dense_layers,
                         num_input_nodes=num_input_nodes,
                         num_dense_nodes=num_dense_nodes,
                         activation=activation,
                         adam_decay=adam_decay
                        )
    

    #named blackbox becuase it represents the structure
    blackbox = model.fit(x=X_train,
                        y=y_train,
                        epochs=3,
                        batch_size=batch_size,
                        validation_split=0.15,
                        )
    #return the validation accuracy for the last epoch.
    accuracy = blackbox.history['val_acc'][-1]

    # Print the classification accuracy.
    print()
    print("Accuracy: {0:.2%}".format(accuracy))
    print()


    # Delete the Keras model with these hyper-parameters from memory.
    del model
    
    # Clear the Keras session, otherwise it will keep adding new
    # models to the same TensorFlow graph each time we create
    # a model with a different set of hyper-parameters.
    K.clear_session()
    tensorflow.reset_default_graph()
    
    # the optimizer aims for the lowest score, so we return our negative accuracy
    return -accuracy