Neural networks - calculating output manually if $x_1=x_2=0$ . Should this be easy to do? This is a problem question I'm trying to make sure I understand from a past paper (with no solutions). The R output is below.
> nnet1 ¡- nnet(y ˜ x1 + x2, data = data, size = 2, decay = 0)

>summary(nnet1)
a 2-2-1 network with 9 weights
options were - entropy fitting
b->h1  i1->h1  i2->h1
-7.05  74.10   99.43
b->h2  i1->h2  i2->h2
9.51   -6.44   30.21
b->o   h1->o   h2->o
-53.16 6.82    81.40

The question asks to manually calculate the prediction with both $x_1$ and $x_2$ equal to $0$.
Given this, am I correct I thinking that the only figures you need are the bias ones? So the answer is obtained by calculating the values at h1 and h2 using just the bias values in the sigmoid function, then summing those with the h1>o and h2>0 values, as well as the b>o value, then running this total through another sigmoid function in the final output node to get the result?
I wasn't sure what "entropy fitting" meant and the question doesn't say explicitly what the activation function is, so I assumed it to be the classic sigmoid. Is this right or have I fundamentally misunderstood?

You are correct, if input data is just zeros, the output of the first layer would be just the biases transformed with the activation function. The next layer would consume as input the output of the previous layer, so the transformed biases. If you don't know the activation function, the question cannot be answered because the results would depend on the activation functions used.
