Question

我正在尝试使用来自Hvass-Labs时间序列教程＃23的代码来实现具有多个输入的混合LSTM-DNN预测器。基本上，我想使用有序和无序数据预测电力的日间价格（目前距离未来仅24步）。我正在使用的模型是输入LSTM（用于顺序数据）和Dense（用于非顺序数据）的两组输入，它们的输出是串联的。看起来像这样：

！https://imgur.com/a/x15FfIy

基本上，每当我在一个时期后尝试拟合模型时，就会显示此错误：

更新：

ValueError: Error when checking input: expected input_1 to have shape (168, 5) but got array with shape (5808, 5)

我已实施的更改：

# Chop off x_test_scaled into two parts:
x_test1_scaled = x_test_scaled[:,0:5] # shape is (5808, 5)
x_test2_scaled = x_test_scaled[:,5:12] # shape is (5808, 7)

validation_data = [np.expand_dims(x_test1_scaled, axis=0), np.expand_dims(x_test2_scaled, axis=0)], np.expand_dims(y_test_scaled, axis=0)

我很困惑，因为我确实已经将生成器分配给了model.fit_generator中的生成器，并且我没有传递x_test1_scaled，其形状为（5808，5）。编辑：（不是validation_data）

%%time
model.fit_generator(generator=generator,
                    epochs=10,
                    steps_per_epoch=30,
                    validation_data=validation_data,
                    callbacks=callbacks)

如果有帮助，这是我的模型：

# first input model
input_1 = Input(shape=((168,5)))
dense_1 = Dense(50)(input_1)

# second input model
input_2 = Input(shape=((168,7)))
lstm_1 = LSTM(units=64, return_sequences=True, input_shape=(None, 7,))(input_2)

# merge input models
merge = concatenate([dense_1, lstm_1])
output = Dense(num_y_signals, activation='sigmoid')(merge)
model = Model(inputs=[input_1, input_2], outputs=output)
# summarize layers
print(model.summary())

编辑：已清除此问题，替换为顶部错误。

到目前为止，我已经管理了所有事情，以实际拟合模型。每当一个纪元完成时，就会出现错误：

ValueError: Error when checking model input: the list of Numpy arrays that you are passing to your model is not the size the model expected. Expected to see 2 array(s), but instead got the following list of 1 arrays: [array([[[0.4       , 0.44444442, 0.        , ..., 0.1734707 ,
         0.07272629, 0.07110982],
        [0.3904762 , 0.43434343, 0.04347826, ..., 0.1740398 ,
         0.07282589, 0.06936309],
       ...

我尝试了来自同一错误消息的其他stackexchange帖子的解决方案。他们没有成功，但是我最终能够将问题数组与validation_data隔离。我只是不知道如何将其“重塑”为所需的2个数组。

批处理生成器：我已经包含了两组输入。 x_batch_1和x_batch_2

def batch_generator(batch_size, sequence_length):
    """
    Generator function for creating random batches of training-data.
    """

    # Infinite loop.
    while True:
        # Allocate a new array for the batch of input-signals.
        x_shape = (batch_size, sequence_length, num_x_signals)
        x_batch = np.zeros(shape=x_shape, dtype=np.float16)

        # Allocate a new array for the batch of output-signals.
        y_shape = (batch_size, sequence_length, num_y_signals)
        y_batch = np.zeros(shape=y_shape, dtype=np.float16)

        # Fill the batch with random sequences of data.
        for i in range(batch_size):
            # Get a random start-index.
            # This points somewhere into the training-data.
            idx = np.random.randint(num_train - sequence_length)

            # Copy the sequences of data starting at this index.
            x_batch[i] = x_train_scaled[idx:idx+sequence_length]
            y_batch[i] = y_train_scaled[idx:idx+sequence_length]

        x_batch_1 = x_batch[ :, :, 0:5]
        x_batch_2 = x_batch[ :, :, 5:12]
        yield ([x_batch_1, x_batch_2], y_batch)

batch_size = 32
sequence_length = 24 * 7
generator = batch_generator(batch_size=batch_size,
                            sequence_length=sequence_length)

验证集：

validation_data = np.expand_dims(x_test_scaled, axis=0), np.expand_dims(y_test_scaled, axis=0)

最后模型拟合：

%%time
model.fit_generator(generator=generator,
                    epochs=10,
                    steps_per_epoch=30,
                    validation_data=validation_data,
                    callbacks=callbacks)

ValueError: Error when checking model input: the list of Numpy arrays that you are passing to your model is not the size the model expected. Expected to see 2 array(s), but instead got the following list of 1 arrays: [array([[[0.4       , 0.44444442, 0.        , ..., 0.1734707 ,
         0.07272629, 0.07110982],
        [0.3904762 , 0.43434343, 0.04347826, ..., 0.1740398 ,
         0.07282589, 0.06936309],
       ...

该数组与validation_data相同。另一件事是，每当第一个时期结束时，错误就会逐渐蔓延，这加强了问题的有效性，即validation_data。

Answer 1

这是因为您的模型需要两组输入，x_batch_1, x_batch_2中的batch_generator。虽然您的validation_data只有一个数组np.expand_dims(x_test_scaled, axis=0)

您需要使validation_data看起来像batch_generator，可能是[np.expand_dims(x_test1_scaled, axis=0), np.expand_dims(x_test2_scaled, axis=0)], np.expand_dims(y_test_scaled, axis=0)。

如果您仍然不了解，请提供有关x_test1_scaled的信息，例如形状或加载方式。

ValueError：检查输入时出错：期望input_1的形状为（168，5），但数组的形状为（5808，5）

1 个答案: