Neural Network theory to implementation mix up

Question

I'm looking to create a neural network for the first time in matlab. As such I'm just a little confused and need some quick guidance. Below is an image:

Now the problem I'm currently having/ needs verification is the values that are generated from my hidden layer that move to my outer layer are these values 0's and 1's? i.e from u0 to unh do these nodes output 0's and 1's or values in between 0 and 1 like 0.8,0.4 etc? Another question is then my output node that should be outputting for me a value in between 0 and 1, so that an error can be found and used in the back propagation?

Like I said it's my first time doing this so I just need some guidance.

Answer 1

Not quite, the output of the hidden layer is like any other layer and each node gives a ranged value. The output of any node in a neural network is thus usually restricted to the [0, 1] or the [-1, 1] range. Your output node will similarly output a range of values, but that range is oftentimes thresholded to snap to 0 or 1 for simplicity of interpretation.

This however, doesn't mean that the outputs are linearly distributed. Usually you have a sigmoid, or some other non-linear, distribution which spreads more information through the middle, [-0.5, 0.5], range rather than evenly across the domain. Sometimes specialty functions are used to detect certain patterns, such as sinusoids -- though generally this is rarer and usually unnecessary.