what?

Why would you want to do this?

Regardless of your end goals and how much sense this makes or not, this is not too difficult to implement:

Let’s create a ”virtual hidden layer” between input and hidden layers. The weights (and biases) between the input and the “virtual hidden layer” are normal weights, just like the ones in your network. The weights between the virtual hidden layer and the original hidden layer are frozen with the value of 1, bias with 0. And voilà, now you are passing the formerly unknown vales from the hidden unit nodes to each other.

I’m not sure what this would be good for, but let us know if you find something interesting.

Did you just make something up and ask us to explain it to you?

where did you get this arch from ? any reference ? it's not clear what the meaning of this is.

What you have drawn there is a recurrent model where there is a feedback loop. The only way that can work is if what you are feeding back into a layer/node is from the *previous* input/time-step, otherwise you've got an infinite loop.

You can certainly build something like that if you want you (2nd "layer" is an LSTM or RNN), however this is total overkill if all you are trying to do is build is a binary classifier. Depending on your input a single fully-connected layer may work, else add more layers.

Thanks for your response. What exactly do you mean by virtual hidden layer, is this a layer for just initializing the input in the hidden layer and that layer is not used again, something like that?

Just a normal hidden layer. The connection between nodes of the same layer in your schema can be thought of as two layers where each node forwards it’s exact value to the corresponding node of the next layer (weight frozen to 1), and the rest of the weights get trained.

What's the question? wut