Why is predicting the output of a trained neural network by using
Why is predicting the output of a trained neural network by using "net/sim" different than manually calculating the output using the network weights, biases and transfer functions? Refer to the following example: %%create random data rng(1); xdata = rand(5,100); ydata = rand(1,100); %%fit a forward network nlayersize = 10; net = feedforwardnet(nlayersize); net.trainParam.showWindow = false; net = train(net,xdata,ydata); net.layers{1}.transferFcn %%calculate the model output at a new point xNew = rand(5,1); y1 = net(xNew); % can use sim as well %%manually calculate model output IW = net.IW{1}; LW = net.LW{2,1}; b = net.b; y2 = IW*xNew+b{1}; y2 = tansig(y2); y2 = LW*y2+b{2}; dy = y1-y2 "dy" is not zero but it should be.
John Williams answered .
2025-11-20
xNew = rand(5,1);
y1 = net(xNew);
xNew = mapminmax('apply', xNew , net.inputs{1}.processSettings{1}); % apply input pre-processing
IW = net.IW{1};
LW = net.LW{2, 1};
b = net.b;
y2 = IW*xNew+b{1};
y2 = tansig(y2);
y2 = LW*y2+b{2};
y2 = mapminmax('reverse', y2, net.outputs{2}.processSettings{1}); % reverse the output pre-processing
dy = y1 - y2