A better approach would be to “save the network after training”, and then use the “saved network” to predict output on “test data”.
The reason behind using the “saved network” to predict the output on “test data” is that, back propagation works to minimize the “loss function” of your network, and network learns the most suitable values of “weights and biases”. Hence, after training the network, the values of “weights and biases” would be such that they minimize the error function.
However, if you want to experiment with different values of weights and biases, you can load the values of “weights and biases” from your “.mat file”.
To save your model after training, you can look for documentation of “save”:
You can look the below code for your reference:
net = feedforwardnet(10);
net = configure(net,x,t);
% where “x” is input and “t” is target
my_model = net;
save my_model;
% Now to use the saved network on test data, first load the “saved network”
load my_model
newoutput = my_model(newinput);
