sigmoid - How to calculate weights and biases in hidden layer in neural net

Question

I started developing a neural net but I don't really understand how to update weights and biases in the hidden layer or lets say there is one factor I do not understand.

Lets assume we have a net of this structure:

For updating weights in the last layer I have the following formula (example for w5):

sigmoid' stands for sigmoid * 1 - sigmoid and w5-updated would be w5 - learning-rate * the product of the formula.

So far everything makes sence for me

The problem I have is in the hidden layer, as mentioned above. For the weights there I have this formula (example of w1):

And the thing I don't get here is the sum at the end. In this example it makes sence, but if i had two hidden layers instead of one, what would I sum then? I first thought I'd need to sum the data of the next layer but this does not make any sense because I need the expected output of the neurons and I have no expected output for a neuron in a hidden layer. Lets assume we had this net:

How would I calculate a new weight for w1 for example?

Thank you!

question from:https://stackoverflow.com/questions/65905039/how-to-calculate-weights-and-biases-in-hidden-layer-in-neural-net

Answer 1

Waitting for answers