我正在读Chollet的书Deep Learning with Python的第3.7节。该项目是要找出20世纪70年代波士顿郊区房屋的中间价格。
https://github.com/fchollet/deep-learning-with-python-notebooks/blob/master/3.7-predicting-house-prices.ipynb
在“使用K折验证验证我们的方法”一节中，我尝试运行以下代码块：

num_epochs = 500
all_mae_histories = []
for i in range(k):
    print('processing fold #', i)
    # Prepare the validation data: data from partition # k
    val_data = train_data[i * num_val_samples: (i + 1) * num_val_samples]
    val_targets = train_targets[i * num_val_samples: (i + 1) * num_val_samples]

    # Prepare the training data: data from all other partitions
    partial_train_data = np.concatenate(
        [train_data[:i * num_val_samples],
         train_data[(i + 1) * num_val_samples:]],
        axis=0)
    partial_train_targets = np.concatenate(
        [train_targets[:i * num_val_samples],
         train_targets[(i + 1) * num_val_samples:]],
        axis=0)

    # Build the Keras model (already compiled)
    model = build_model()
    # Train the model (in silent mode, verbose=0)
    history = model.fit(partial_train_data, partial_train_targets,
                        validation_data=(val_data, val_targets),
                        epochs=num_epochs, batch_size=1, verbose=0)
    mae_history = history.history['val_mean_absolute_error']
    all_mae_histories.append(mae_history)

我得到一个错误KeyError：'瓦尔_mean_absolute_error'

mae_history = history.history['val_mean_absolute_error']

我猜解决方案是找出正确的参数来替换瓦尔_mean_absolute_error。我试着查看一些Keras文档，以确定正确的键值。有谁知道正确的key值？