Wrong prediction results from feedforwardnet

4 views (last 30 days)
I made a simple feedforward net as follows:
mynet = feedforwardnet(5)
mynet.layers{1}.transferFcn = 'poslin'; % one hidden layer(5 neurons) with poslin = ReLU activation function
mynet.layers{2}.transferFcn = 'purelin'; % last layer has simply linear activation function
I want to train this Neural Network to learn a non-linear function that looks like this. So basically it is a regression problem.
So we have two inputs(u1, u2), and one output(y).
After training I get the weights and biases by:
W1 = mynet.IW{1,1}; b1 = mynet.b{1}; W2 = mynet.LW{2,1}; b2 = mynet.b{2}
Now to estimate the output, we can simply use:
inputs = [3;2] % u1 = 3, u2 = 2
y_predicted = mynet(inputs])
Y_predicted = 2.9155 % the prediction given by the NN
Fine, the prediction is good.
But when I manually checked it by forward propagation, I got different result:
Z1 = W1*[3; 2] + b1;
A1 = poslin(Z1); % applying ReLU activation function
Z2 = W2*A1 + b2;
A2 = Z2; % linear activation function
y_predicted = A2;
y_predicted = 2.2549
Should not both be the same? Am I missing something?

Accepted Answer

Anshika Chaurasia
Anshika Chaurasia on 4 Oct 2021

More Answers (0)

Categories

Find more on Sequence and Numeric Feature Data Workflows in Help Center and File Exchange

Products


Release

R2021a

Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!