Hello everyone! I would like to create a neural network with 5 input nodes. In the following I have created a simple code with the help of the neural network toolbox. I have a question regarding this code.
How can I change the processing function in the hidden layer nodes? In the default settings the sigmoid function is used… I would like to change it to the tanh function
Here is my code:
Solve an Input-Output Fitting problem with a Neural Network% Script generated by Neural Fitting app
% Created 28-Mar-2016 11:42:43
%
% This script assumes these variables are defined:
%% rinputs - input data.
% rtargetfourthroot - target data.
x = rinputs; t = rtargetfourthroot;
% 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. Suitable in low memory situations. trainFcn = 'trainlm'; % Levenberg-Marquardt backpropagation.
% Create a Fitting Network hiddenLayerSize = 50; net = fitnet(hiddenLayerSize,trainFcn);
% Choose Input and Output Pre/Post-Processing Functions % For a list of all processing functions type: help nnprocess net.input.processFcns = {'removeconstantrows','mapminmax'}; net.output.processFcns = {'removeconstantrows','mapminmax'};
% Setup Division of Data for Training, Validation, Testing % For a list of all data division functions type: help nndivide net.divideFcn = 'dividerand'; % Divide data randomly net.divideMode = 'sample'; % Divide up every sample net.divideParam.trainRatio = 60/100; net.divideParam.valRatio = 20/100; net.divideParam.testRatio = 20/100;
% Choose a Performance Function % For a list of all performance functions type: help nnperformance net.performFcn = 'mse'; % Mean Squared Error
% Choose Plot Functions % For a list of all plot functions type: help nnplot net.plotFcns = {'plotperform','plottrainstate','ploterrhist', … 'plotregression', 'plotfit'};
% Train the Network [net,tr] = train(net,x,t);
% Test the Network y = net(x); e = gsubtract(t,y); performance = perform(net,t,y)
% Recalculate Training, Validation and Test Performance trainTargets = t .* tr.trainMask{1}; valTargets = t .* tr.valMask{1}; testTargets = t .* tr.testMask{1}; trainPerformance = perform(net,trainTargets,y) valPerformance = perform(net,valTargets,y) testPerformance = perform(net,testTargets,y)
% View the Network view(net)
% Plots % Uncomment these lines to enable various plots. %figure, plotperform(tr) %figure, plottrainstate(tr) %figure, ploterrhist(e) %figure, plotregression(t,y) %figure, plotfit(net,x,t)
% Deployment % Change the (false) values to (true) to enable the following code blocks. % See the help for each generation function for more information. if (false) % Generate MATLAB function for neural network for application % deployment in MATLAB scripts or with MATLAB Compiler and Builder % tools, or simply to examine the calculations your trained neural % network performs. genFunction(net,'myNeuralNetworkFunction'); y = myNeuralNetworkFunction(x); end if (false) % Generate a matrix-only MATLAB function for neural network code % generation with MATLAB Coder tools. genFunction(net,'myNeuralNetworkFunction','MatrixOnly','yes'); y = myNeuralNetworkFunction(x); end if (false) % Generate a Simulink diagram for simulation or deployment with. % Simulink Coder tools. gensim(net); end
Thank you
Best Answer