custom multi layer feed forward neural network
3 vues (au cours des 30 derniers jours)
Afficher commentaires plus anciens
_ _ *I have custom multi layer feed forward program , but the validation and test performance equal to NaN value (ther is no curve , but only training performance ), i want simple code of using custom network for any type of inputs and outputs but i want introduce the validation and test performance value , I would be grateful if anyone can help me , the code is as follows :
close all, clear all, clc, format compact
inputs = [1:6]' % input vector (6-dimensional pattern)
outputs = [1 2]' % corresponding target output vector
% create network
net = network( ...
1, ... % numInputs, number of inputs,
2, ... % numLayers, number of layers
[1; 0], ... % biasConnect, numLayers-by-1 Boolean vector,
[1; 0], ... % inputConnect, numLayers-by-numInputs Boolean matrix,
[0 0; 1 0], ... % layerConnect, numLayers-by-numLayers Boolean matrix
[0 1] ... % outputConnect, 1-by-numLayers Boolean vector
);
net.layers{1}.size = 5;
% hidden layer transfer function
net.layers{1}.transferFcn = 'radbas';
view(net);
net = configure(net,inputs,outputs);
view(net);
% initial network response without training
initial_output = net(inputs)
% network training
net.trainFcn = 'trainlm';
net.performFcn = 'mse';
[net,tr] = train(net,inputs,outputs);
% network response after training final_output = net(inputs)
plotperf(tr);
2 commentaires
Image Analyst
le 12 Sep 2015
Read http://www.mathworks.com/matlabcentral/answers/13205-tutorial-how-to-format-your-question-with-markup and then fix your post's formatting.
Réponses (0)
Voir également
Catégories
En savoir plus sur Define Shallow Neural Network Architectures dans Help Center et File Exchange
Produits
Community Treasure Hunt
Find the treasures in MATLAB Central and discover how the community can help you!
Start Hunting!