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I am trying to solve a problem predicting a value between a range for a sentence:

The dataset looks like this:

Index_no               text_sentence                             value 

01                     yes I like riding a bike. 
                       I was 4 when I learned                   4.2311
                       to ride a bike. the colors of my 
                       bike is yellow and black.   


02                     i like riding my bike, i learnt 
                       riding a bike when i was 8 or            -2.11
                       9 years old ,my bike is sparkling 
                       pink with white marks  

Range of values is -7 to 7. Now, I am thinking about using a LSTM for text, but I am confused about the continuous output.

I was thinking about two methods:

Converting (normalizing the data between 0 and 1) and then after getting the output from network, denormalize the data, will this work?

Second approach, using a custom activation function?

Or how can I get output between a range?

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  • 2
    $\begingroup$ The question is: what makes you believe that the LSTM will output categorical values by default :) ? $\endgroup$ – pcko1 Jun 19 '18 at 13:17
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    $\begingroup$ This is a difficult question to answer as written because it's hard to tell what you're looking for from this value or where it came from in the first place. There are a number of ways to get network output and put it in a range from -7 to 7. What is the distribution of the target values in the training set? Should your predictions be normally distributed around 0? Uniformly distributed over the whole range? I think it's worth getting an understanding of what properties the output should have before you come up with the method. $\endgroup$ – Matthew Jul 19 '18 at 15:05
  • $\begingroup$ Hi @AyodhyankitPaul, did any answer below help? $\endgroup$ – Escachator Sep 5 '18 at 15:38
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Using Sigmoid Activation function (standard, no need for customization), you will get outputs in the range [0,1]. If you pre-scale your labels accordingly to fit in [0,1], the network will output values which you can manually re-scale to match the original amplitude. Hope it helps :)

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Just take the last activation of the RNN and sum it. Then train using a loss suitable for regression, e.g. MAE or Quadratic Loss where you compare the real value vs your sum from the model.

You can also pass the last activation through a linear layer with an output of dimension one, for example.

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