wehub-resource-sync
39 lines
883 B
Matlab
Executable File
39 lines
883 B
Matlab
Executable File
function [model, mse] = mlp(X, T, h)
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% Train a multilayer perceptron neural network
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% Input:
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% X: d x n data matrix
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% T: p x n response matrix
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% h: L x 1 vector specify number of hidden nodes in each layer l
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% Ouput:
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% model: model structure
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% mse: mean square error
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% Written by Mo Chen (sth4nth@gmail.com).
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eta = 1/size(X,2);
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h = [size(X,1);h(:);size(T,1)];
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L = numel(h);
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W = cell(L-1,1);
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for l = 1:L-1
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W{l} = randn(h(l),h(l+1));
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end
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Z = cell(L,1);
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Z{1} = X;
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maxiter = 200;
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mse = zeros(1,maxiter);
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for iter = 1:maxiter
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% forward
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for l = 2:L
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Z{l} = sigmoid(W{l-1}'*Z{l-1}); % 5.10, 5.49
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end
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% backward
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E = T-Z{L};
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mse(iter) = mean(dot(E,E),1);
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for l = L-1:-1:1
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df = Z{l+1}.*(1-Z{l+1});
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dG = df.*E;
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dW = Z{l}*dG';
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W{l} = W{l}+eta*dW;
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E = W{l}*dG;
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end
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end
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mse = mse(1:iter);
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model.W = W; |