Body: <p>I want to predict two separate y-values (not really logically connected) based on an input sequence of data (values x). Using LSTM cells.</p>
  <p>Should I train two models separately or should I just increase the dimension of the last layer to 2 instead of 1 and feed the fitting algorithm y-values as 2D pairs? In other words, will there be a lot of interference or can I expect on average similar results with one or two models?</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-02-25T20:51:29.930Z
Comments.Text: Just to be clear, there is only one x sequence for each two different y values? The two y values are therefore two different kinds of thing associated with the same type of x sequence?
Comments.UserDisplayName: Neil Slater
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-02-26T16:52:05.953Z
Comments.Text: yes. For example, if I am trying to predict the next value of the sequence m step ahead and n steps ahead, n > m.
  Another example is I am observing a multi-valued function $f:R \rightarrow R^2$ and the values are somewhat related and I'm trying to predict a new pair.
Comments.UserDisplayName: Jake B.
Tags: <neural-networks><tensorflow><recurrent-neural-networks><long-short-term-memory>
Title: If I want to predict two unrelated values given the same sequence of data points, should I have a model with two outputs or two models?
UserDisplayName: Jake B.
ThreadText: # If I want to predict two unrelated values given the same sequence of data points, should I have a model with two outputs or two models?
  
  (Asked by: username_0 on 2021-02-25)
  
  I want to predict two separate y-values (not really logically connected) based on an input sequence of data (values x). Using LSTM cells.
  
  Should I train two models separately or should I just increase the dimension of the last layer to 2 instead of 1 and feed the fitting algorithm y-values as 2D pairs? In other words, will there be a lot of interference or can I expect on average similar results with one or two models?
  
  **Tags:** neural-networks, tensorflow, recurrent-neural-networks, long-short-term-memory
  
  > **Comments:**
  > **username_1**: Just to be clear, there is only one x sequence for each two different y values? The two y values are therefore two different kinds of thing associated with the same type of x sequence?  
  > **username_0**: yes. For example, if I am trying to predict the next value of the sequence m step ahead and n steps ahead, n > m. Another example is I am observing a multi-valued function $f:R \rightarrow R^2$ and the values are somewhat related and I'm trying to predict a new pair.  
  
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  ## 0 answers
  
  