www.gusucode.com > nnet 案例源码 matlab代码程序 > nnet/ReconstructObservationsUsingSparseAutoencoderExample.m
%% Reconstruct Observations Using Sparse Autoencoder % Generate the training data. % Copyright 2015 The MathWorks, Inc. rng(0,'twister'); % For reproducibility n = 1000; r = linspace(-10,10,n)'; x = 1 + r*5e-2 + sin(r)./r + 0.2*randn(n,1); %% % Train autoencoder using the training data. hiddenSize = 25; autoenc = trainAutoencoder(x',hiddenSize,... 'EncoderTransferFunction','satlin',... 'DecoderTransferFunction','purelin',... 'L2WeightRegularization',0.01,... 'SparsityRegularization',4,... 'SparsityProportion',0.10); %% % Generate the test data. n = 1000; r = sort(-10 + 20*rand(n,1)); xtest = 1 + r*5e-2 + sin(r)./r + 0.4*randn(n,1); %% % Predict the test data using the trained autoencoder, |autoenc| . xReconstructed = predict(autoenc,xtest'); %% % Plot the actual test data and the predictions. figure; plot(xtest,'r.'); hold on plot(xReconstructed,'go');