www.gusucode.com > nnet 案例源码 matlab代码程序 > nnet/appcr1.m

    %% Character Recognition
% This example illustrates how to train a neural network to perform
% simple character recognition.

% Copyright 2011-2012 The MathWorks, Inc.

%% Defining the Problem
% The script *prprob* defines a matrix X with 26 columns, one for
% each letter of the alphabet.  Each column has 35 values which can either
% be 1 or 0.  Each column of 35 values defines a 5x7 bitmap of a letter.
%
% The matrix T is a 26x26 identity matrix which maps the 26 input
% vectors to the 26 classes.

[X,T] = prprob;

%%
% Here A, the first letter, is plotted as a bit map.

plotchar(X(:,1))

%% Creating the First Neural Network
% To solve this problem we will use a feedforward neural network set up
% for pattern recognition with 25 hidden neurons.
%
% Since the neural network is initialized with random initial weights, the
% results after training vary slightly every time the example is run.
% To avoid this randomness, the random seed is set to reproduce the
% same results every time. This is not necessary for your own applications.

setdemorandstream(pi);

net1 = feedforwardnet(25);
view(net1)

%% Training the first Neural Network
% The function *train* divides up the data into training, validation and
% test sets.  The training set is used to update the network, the
% validation set is used to stop the network before it overfits the
% training data, thus preserving good generalization.  The test set acts
% as a completely independent measure of how well the network can be
% expected to do on new samples.
%
% Training stops when the network is no longer likely to improve on the
% training or validation sets.

net1.divideFcn = '';
net1 = train(net1,X,T,nnMATLAB);

%% Training the Second Neural Network
% We would like the network to not only recognize perfectly formed
% letters, but also noisy versions of the letters.  So we will try training
% a second network on noisy data and compare its ability to genearlize
% with the first network.
%
% Here 30 noisy copies of each letter Xn are created.  Values are limited
% by *min* and *max* to fall between 0 and 1.  The corresponding targets
% Tn are also defined.

numNoise = 30;
Xn = min(max(repmat(X,1,numNoise)+randn(35,26*numNoise)*0.2,0),1);
Tn = repmat(T,1,numNoise);

%%
% Here is a noise version of A.

figure
plotchar(Xn(:,1))

%%
% Here the second network is created and trained.

net2 = feedforwardnet(25);
net2 = train(net2,Xn,Tn,nnMATLAB);

%% Testing Both Neural Networks

noiseLevels = 0:.05:1;
numLevels = length(noiseLevels);
percError1 = zeros(1,numLevels);
percError2 = zeros(1,numLevels);
for i = 1:numLevels
  Xtest = min(max(repmat(X,1,numNoise)+randn(35,26*numNoise)*noiseLevels(i),0),1);
  Y1 = net1(Xtest);
  percError1(i) = sum(sum(abs(Tn-compet(Y1))))/(26*numNoise*2);
  Y2 = net2(Xtest);
  percError2(i) = sum(sum(abs(Tn-compet(Y2))))/(26*numNoise*2);
end

figure
plot(noiseLevels,percError1*100,'--',noiseLevels,percError2*100);
title('Percentage of Recognition Errors');
xlabel('Noise Level');
ylabel('Errors');
legend('Network 1','Network 2','Location','NorthWest')

%%
% Network 1, trained without noise, has more errors due to noise than
% does Network 2, which was trained with noise.