Izaizi Ismail asked . 2021-08-04

How to change the activation function in ANN model created using toolbox

% This script assumes these variables are defined:
%   data - input data.
%   target - target data.
% load data
load data.mat;
load target.mat;
x = data;
t = target;
% Choose a Training Function
% For a list of all training functions type: help nntrain
% 'trainlm' is usually fastest.
% 'trainbr' takes longer but may be better for challenging problems.
% 'trainscg' uses less memory. NFTOOL falls back to this in low memory situations.
trainFcn = 'trainbr';  % Bayesian Regularization
% Create a Feedforward Network
hiddenLayerSize = 18;
net = feedforwardnet (hiddenLayerSize,trainFcn);
% Setup Division of Data for Training, Validation, Testing
RandStream.setGlobalStream(RandStream('mt19937ar','seed',1)); % to get constant result
net.divideFcn = 'divideblock'; % Divide targets into three sets using blocks of indices
net.divideParam.trainRatio = 70/100;
net.divideParam.valRatio = 15/100;
net.divideParam.testRatio = 15/100;
%TRAINING PARAMETERS
net.trainParam.show=50;  %# of ephocs in display
net.trainParam.lr=0.05;  %learning rate
net.trainParam.epochs=10000;  %max epochs
net.trainParam.goal=0.05^2;  %training goal
net.performFcn='mse';  %Name of a network performance function %type help nnperformance
% Train the Network
[net,tr] = train(net,x,t); 
% Test the Network
y = net(x);
e = gsubtract(t,y);
performance = perform(net,t,y)
% View the Network
view(net)

The questions are: Is it correct to use this code below and will it affect the function of my model?

RandStream.setGlobalStream(RandStream('mt19937ar','seed',1)); % to get constant result

neural networks , activation functions