Neural network with multiple inputs and single output - how to change processing functions?

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

[Greg Heath]

PLEASE !!!

1. Remove assignment statements that just assign default values.

2. Justify all nondefault values that are used

3. Use {} to format

THANKS

Greg

Answer 1

[Greg Heath]

% NO SEMICOLONS !!!

 clc
 net = fitnet   
 transferFcn1 = net.layers{1}.transferFcn
 transferFcn2 = net.layers{2}.transferFcn

Hope this helps.

Thank you for formally accepting my answer

Greg

[pepper yuan]

May i know how to justify the tanh as my transfer function?

>> [x,t] = house_dataset;

>> net = feedforwardnet;

>> view(net)

>> net.layers{1}.transferFcn = 'tanh'

Error using feval Undefined function 'tanh' for input arguments of type 'char'.

Error in network/subsasgn>getDefaultParam (line 2048) param = struct(feval(fcn,'defaultParam'));

Error in network/subsasgn>setLayerTransferFcn (line 1224) net.layers{i}.transferParam = getDefaultParam(transferFcn);

Error in network/subsasgn>network_subsasgn (line 208) if isempty(err), [net,err] = setLayerTransferFcn(net,i,transferFcn); end

These are the example code that I'm trying to use tanh as my transfer function. How can i make the tanh function as the transfer function in hidden layer? Thank you

[Greg Heath]

 TANSIG == TANH   !!!