使用Anaconda Python 2.7 Windows 10。

我正在使用Keras范例训练语言模型：

print('Build model...')
model = Sequential()
model.add(GRU(512, return_sequences=True, input_shape=(maxlen, len(chars))))
model.add(Dropout(0.2))
model.add(GRU(512, return_sequences=False))
model.add(Dropout(0.2))
model.add(Dense(len(chars)))
model.add(Activation('softmax'))

model.compile(loss='categorical_crossentropy', optimizer='rmsprop')

def sample(a, temperature=1.0):
    # helper function to sample an index from a probability array
    a = np.log(a) / temperature
    a = np.exp(a) / np.sum(np.exp(a))
    return np.argmax(np.random.multinomial(1, a, 1))


# train the model, output generated text after each iteration
for iteration in range(1, 3):
    print()
    print('-' * 50)
    print('Iteration', iteration)
    model.fit(X, y, batch_size=128, nb_epoch=1)
    start_index = random.randint(0, len(text) - maxlen - 1)

    for diversity in [0.2, 0.5, 1.0, 1.2]:
        print()
        print('----- diversity:', diversity)

        generated = ''
        sentence = text[start_index: start_index + maxlen]
        generated += sentence
        print('----- Generating with seed: "' + sentence + '"')
        sys.stdout.write(generated)

        for i in range(400):
            x = np.zeros((1, maxlen, len(chars)))
            for t, char in enumerate(sentence):
                x[0, t, char_indices[char]] = 1.

            preds = model.predict(x, verbose=0)[0]
            next_index = sample(preds, diversity)
            next_char = indices_char[next_index]

            generated += next_char
            sentence = sentence[1:] + next_char

            sys.stdout.write(next_char)
            sys.stdout.flush()
        print()

根据model.fit文档， model.fit方法返回一个History回调，该回调具有一个history属性，其中包含连续损失列表和其他度量。

hist = model.fit(X, y, validation_split=0.2)
print(hist.history)

训练好模型后，如果我运行print(model.history)我会得到以下错误：

 AttributeError: 'Sequential' object has no attribute 'history'

使用上述代码训练模型后，如何返回模型历史记录？

更新

问题是：

首先必须定义以下内容：

from keras.callbacks import History 
history = History()

回调选项必须被调用

model.fit(X_train, Y_train, nb_epoch=5, batch_size=16, callbacks=[history])

但是现在如果我打印

print(history.History)

它返回

{}

即使我进行了迭代。