added mlp

chapter05/demo.m

@@ -0,0 +1,6 @@
+clear; close all;
+h = 4;
+X = [0 0 1 1;0 1 0 1];
+Y = [0 1 1 0];
+[model,mse] = mlp(X,Y,h);
+plot(mse);

chapter05/mlp.m

@@ -1,40 +1,39 @@
-function model = mlp(X, Y, h)
-
-
-
-[D,N] = size(X);
-h = [h,size(Y,1)];
+function [model, mse] = mlp(X, Y, h)
+% Multilayer perceptron
+% Input:
+%   X: d x n data matrix
+%   Y: p x n response matrix
+%   h: L x 1 vector specify number of hidden nodes in each layer l
+% Ouput:
+%   model: model structure
+%   mse: mean square error
+% Written by Mo Chen ([email protected]).
+h = [size(X,1);h(:);size(Y,1)];
 L = numel(h);
-
-maxiter = 200;
-eta = 1/N;
-
-W = cell(L);
-W{1} = randn(D,h(1));
-
-for l = 2:L
-    W{l} = randn(h(l-1),h(l));
+W = cell(L-1);
+for l = 1:L-1
+    W{l} = randn(h(l),h(l+1));
 end
-
-
 Z = cell(L);
-
+Z{1} = X;
+eta = 1/size(X,2);
+maxiter = 2000;
+mse = zeros(1,maxiter);
 for iter = 1:maxiter
 %     forward
-    Z{1} = sigmoid(W{1}'*X);
     for l = 2:L
-        Z{l} = sigmoid(W{l}'*Z{l-1});
+        Z{l} = sigmoid(W{l-1}'*Z{l-1});
     end
 %     backward
     E = Y-Z{L};
-    df = Z{L}.*(1-Z{L});
-
+    mse(iter) = mean(dot(E(:),E(:)));
     for l = L-1:-1:1
-        df = Z{l}.*(1-Z{l});
+        df = Z{l+1}.*(1-Z{l+1});
         dG = df.*E;
-        dW = Z{l}*dG;
-        W = W+eta*dW;
-        E = (W{l}*dG);
+        dW = Z{l}*dG';
+        W{l} = W{l}+eta*dW;
+        E = W{l}*dG;
     end
 end
+mse = mse(1:iter);
 model.W = W;