LSTM -RNN : How to get continuous range output instead of categorical? 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?
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 :)