Control the epochs while training a neural network

I am trying to train a BP neural network with the following codes. I mean to run the iterations for 1000 epochs. However, when the net.trainParam.goal = 0 is achieved, the training process will stop, which is much less than 1000.

How can I set some parameters so that I can train the neural network for 1000 times. I can to plot MSE VS. epoch.

Thanks!

%%%%%%%%%%%%%%%%%%%% clear all; close all; clc;

% Number of Inputs(n), Outputs(r) and neurons in hidden layer(m) n = 1; r = 1; m = 12; % Number of training values (epochs) epochs = 1000; % Input value range x_min = -1; x_max = 1; for k = 1:n x_train(k,:) = x_min + (x_max-x_min)* rand(epochs,1); end % Desired values for random vector x_train y_des = 2*x_train.^2 + 1;

net = feedforwardnet(m);

net.trainParam.epochs = 1000; % epoch net.trainParam.show = 10; % show frequency net.trainParam.goal = 0; % objective MSE

net = train(net, x_train, y_des);

Answer 1

[Greg Heath]

The purpose of training is to reduce mse to a reasonably low value in as few epochs as possible. When training is sufficiently long, the plot of mse will asymptotically decrease to a horizontal straight line at mse = 0.

Therefore, your request makes no sense to me.

In fact, if the training target is standardized (zero-mean/unit-variance rows) via the functions zscore or mapstd, there is no practical reason to reduce mse below

~mean(var(transpose(target)))/100.

Hope this helps.

Greg

[Greg Heath]

Thank you for accepting my answer!