test_callbacks.py 文件源码

def test_LambdaCallback():
    (X_train, y_train), (X_test, y_test) = get_test_data(nb_train=train_samples,
                                                         nb_test=test_samples,
                                                         input_shape=(input_dim,),
                                                         classification=True,
                                                         nb_class=nb_class)
    y_test = np_utils.to_categorical(y_test)
    y_train = np_utils.to_categorical(y_train)
    model = Sequential()
    model.add(Dense(nb_hidden, input_dim=input_dim, activation='relu'))
    model.add(Dense(nb_class, activation='softmax'))
    model.compile(loss='categorical_crossentropy',
                  optimizer='sgd',
                  metrics=['accuracy'])

    # Start an arbitrary process that should run during model training and be terminated after training has completed.
    def f():
        while True:
            pass

    p = multiprocessing.Process(target=f)
    p.start()
    cleanup_callback = callbacks.LambdaCallback(on_train_end=lambda logs: p.terminate())

    cbks = [cleanup_callback]
    model.fit(X_train, y_train, batch_size=batch_size,
              validation_data=(X_test, y_test), callbacks=cbks, nb_epoch=5)
    p.join()
    assert not p.is_alive()