具有多个输入序列和对应的多个输出序列的LSTM [英] LSTM with multiple input sequences and corresponding multiple output sequences

问题描述

我有一个与重塑LSTM的输入/输出数据有关的问题.尽管有很多文章都在考虑这些问题，但我无法找到合适的解决方案.如果错误很明显，我深表歉意-我是深度学习领域的新手.

I have an issue related to reshaping input/output data for LSTM. While there are a lot of posts considering these issues, I couldn't come across to find a proper solution for this. My apologies if the mistake is quite obvious - I am rather new to the field of Deep Learning.

我的问题如下:我进行了一次仿真，得出了一些与时间相关的数据序列，这些序列想输入到LSTM网络中.数据(非常简化)如下所示:

My issue is as follows: I performed a simulation which resulted in several sequences of time dependent data which I'd like to feed into an LSTM-network. The data (very much simplified) looks as follows:

X=[[[8, 0, 18, 10]
    [9, 0, 20, 7]
    [7, 0, 17, 12]]
   [[7, 0, 31, 8]
    [5, 0, 22, 9]
    [7, 0, 17, 12]]]

也就是说，我有两个序列，每个序列具有三个时间步长，每个时间步长有4个特征.因此，X的形状为(2,3,4).相应地，我想预测的内容如下

That is I have two sequences with three time steps each and 4 features per time step. Hence, the shape of X is (2,3,4). Correspondingly, what I would like to predict looks as follows

y=[[[10]
    [7]
   [12]]
   [[8]
    [9]
   [12]]]

并具有形状(2,3,1).也就是说，假设数据点[8,0,18,10]可以预测[10]，然后是点[9,0,20,7]可以预测7，依此类推.然后，我的模型如下所示:

and has shape (2,3,1). That is, the data point [8,0,18,10] is supposed to predict [10], followed by point [9,0,20,7] which should predict 7 and so on. My model then looks as follows:

model.add(LSTM(input_shape=(X.shape[1], X.shape[2]), return_sequences=True))
model.add(Dense(50, activation='tanh'))
model.add(Dense(1, activation='tanh'))

虽然这似乎可以正常工作，但我的结果却很糟糕.我最有可能认为这与正确重塑输出向量有关.另外，我不确定return_sequences是否必须为true.如果将其设置为False，则会收到错误消息"Expected density_2具有2个维，但是得到了形状为(2,3,1)的数组.对此不太确定. 我也正在研究Seq2Seq建模，因为我试图根据序列预测序列，但是找不到解决方法. 有人可以帮忙吗?

While this seems to work without errors, my result is quite bad. Most likely, I think this is related to reshaping the output vector correctly. Also, I am not quite sure about whether or not return_sequences has to be true or not. If it is set to False, I get the error message 'Expected dense_2 to have 2 dimensions, but got an array with shape (2,3,1). Not quite sure about this. I was also looking into Seq2Seq modelling since I am trying to predict a sequence based on a sequence, but I couldn't find a workaround. Can anybody help?

推荐答案

您可能正试图从'tanh'获取大数字，而'tanh'仅输出-1和1之间的数字.

You're probably trying to get big numbers from a 'tanh', which only outputs between -1 and 1.

例如，您不能使用tanh达到10.

You can't reach 10 with tanh, for instance.

要么用'linear'替换最终激活(输出任何内容)，要么将输出数据标准化为-1和1之内.

Either you replace the final activation with 'linear' (outputs anything), or you normalize your output data to be within -1 and 1.

如果数据始终为正，则可以尝试使用'softplus'而不是'linear'；如果选择对数据进行规范化，则将其设置为0到1之间并使用'sigmoid'.

If your data is always positive, you can try using 'softplus' instead of 'linear', and if you opt for normalizing data, make it between 0 and 1 and use 'sigmoid'.

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