42 lines
1.0 KiB
Matlab
42 lines
1.0 KiB
Matlab
function test_example_DBN
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load mnist_uint8;
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train_x = double(train_x) / 255;
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test_x = double(test_x) / 255;
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train_y = double(train_y);
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test_y = double(test_y);
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%% ex1 train a 100 hidden unit RBM and visualize its weights
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rand('state',0)
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dbn.sizes = [100];
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opts.numepochs = 1;
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opts.batchsize = 100;
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opts.momentum = 0;
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opts.alpha = 1;
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dbn = dbnsetup(dbn, train_x, opts);
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dbn = dbntrain(dbn, train_x, opts);
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figure; visualize(dbn.rbm{1}.W'); % Visualize the RBM weights
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%% ex2 train a 100-100 hidden unit DBN and use its weights to initialize a NN
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rand('state',0)
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%train dbn
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dbn.sizes = [100 100];
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opts.numepochs = 1;
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opts.batchsize = 100;
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opts.momentum = 0;
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opts.alpha = 1;
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dbn = dbnsetup(dbn, train_x, opts);
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dbn = dbntrain(dbn, train_x, opts);
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%unfold dbn to nn
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nn = dbnunfoldtonn(dbn, 10);
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nn.activation_function = 'sigm';
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%train nn
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opts.numepochs = 1;
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opts.batchsize = 100;
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nn = nntrain(nn, train_x, train_y, opts);
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[er, bad] = nntest(nn, test_x, test_y);
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assert(er < 0.10, 'Too big error');
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