Training neural network - Forward Pass

I have written a code for forward pass of neural network with the following inputs and targets as shown in the code below. A bias is also present and there is one hidden layer (with 2 neurons) and one output layer.

I am not sure whether the code I have written is correct. Could someone tell me if my logic is correct?

X = [0 0; 0 1];

D = [0;1];

X = [ones(1,2); X];

w1 = [0.15, 0.1, 0.3];

w2 = [-0.05, -0.2,-0.25];

w3 = [0.1,0.2, -0.3];

lr = 1;

H = 2;

No = 1;

max_iter = 10;

for iter = 1:max_iter

for j=1:H %Forward Pass

inpH1 = (w1*X);

outH1 = logsig(inpH1);

inpH2 = (w2*X);

outH2 = logsig(inpH2);

output = [ones(1,2); outH1; outH2];

end

for k=1:No

inpO = w3*output;

out_layer = logsig(inpO);

end

Error = 0.5*(D-out_layer').^2

end

Answer 1

[Greg Heath]

You are very mixed up. I suggest going to the library and finding a good elementary NN book.

Obviously, the appropriate info is available online. However, you will have to search and/or get help from elsewhere.

Sorry,

Greg