LSTM Keras API预测多个输出 [英] LSTM Keras API predicting multiple outputs

问题描述

我正在训练一个LSTM模型，使用50个步骤的序列来输入3种不同功能，如下所示:

I'm training an LSTM model using as input a sequence of 50 steps of 3 different features laid out as below:

#x_train
[[[a0,b0,c0],.....[a49,b49,c49]],
  [a1,b1,c1]......[a50,b50,c50]],
  ...
  [a49,b49,c49]...[a99,b99,c99]]]

使用以下因变量

#y_train
[a50, a51, a52, ... a99]

下面的代码只能预测a，如何在给定的时间步长预测并返回[a，b，c]向量?

The code below works to predict just a, how do I get it to predict and return a vector of [a,b,c] at a given timestep?

def build_model():
model = Sequential()

model.add(LSTM(
    input_shape=(50,3),
    return_sequences=True, units=50))
model.add(Dropout(0.2))

model.add(LSTM(
    250,
    return_sequences=False))
model.add(Dropout(0.2))

model.add(Dense(1))
model.add(Activation("linear"))

model.compile(loss="mse", optimizer="rmsprop")
return model

推荐答案

每层的输出取决于它具有多少个单元/单元/过滤器.

The output of every layer is based on how many cells/units/filters it has.

您的输出具有1个功能，因为Dense(1...)只有一个单元格.

Your output has 1 feature because Dense(1...) has only one cell.

只需将其设置为Dense(3...)即可解决您的问题.

Just making it a Dense(3...) would solve your problem.

现在，如果希望输出与输入具有相同的时间步长，则需要在所有LSTM层中打开return_sequences = True.

Now, if you want the output to have the same number of time steps as the input, then you need to turn on return_sequences = True in all your LSTM layers.

LSTM的输出是:

• (批量大小，单位)-带return_sequences=False
• (批量大小，时间步长，单位)-使用return_sequences=True

• (Batch size, units) - with return_sequences=False
• (Batch size, time steps, units) - with return_sequences=True

然后，在随后的图层中使用TimeDistributed图层包装，就像它们也有时间步长一样(它基本上会将尺寸保留在中间).

Then you use a TimeDistributed layer wrapper in your following layers to work as if they also had time steps (it will basically preserve the dimension in the middle).

def build_model():
    model = Sequential()

    model.add(LSTM(
        input_shape=(50,3),
        return_sequences=True, units=50))
    model.add(Dropout(0.2))

    model.add(LSTM(
        250,
        return_sequences=True))
    model.add(Dropout(0.2))

    model.add(TimeDistributed(Dense(3)))
    model.add(Activation("linear"))

    model.compile(loss="mse", optimizer="rmsprop")
    return model

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