Hi Brian!



 ApeelingPotato:

The inputs are acceleration and gyroscope data and timing of that data. The purpose of the NN is a study into the effectiveness of NN to measure stride lengths.


Okay, that sounds reasonable.
But what are the batch_Y (targets)?  Are they known stride lengths?


I am getting values greater than one now thankfully.


We’re not on the same page here.  You can’t  get a value greater
than one out of a sigmoid().


The problem now is that for a test batch of 200 inputs the output remains the same.


I don’t really understand what you mean.  Do you mean that you have
a batch size of 200, and for each sample in the batch you get exactly
the same value for the single, scalar output?  (Also, I thought you
said earlier that your batch size was ten.)
Do you mean that the values of your outputs remains exactly the
same (for the same inputs) even as you train your network?


Typical values range from 1.2514 to 1.5568. Is there any way to get it so it gives a unique float output for each input in this range?


Typical values of what?  Are you referring to input values?  Output
values?
What do you mean by a “unique float output?”  A generic floating-point
number is pretty close to unique?  (There are approximately 2^32
different floating-point numbers, so any two generic values will almost
certainly differ.)
Best.
K. Frank