fixes rasmusbergpalm#21. Thanks @skaae

SAE/saetrain.m

@@ -4,5 +4,7 @@
         sae.ae{i} = nntrain(sae.ae{i}, x, x, opts);
         t = nnff(sae.ae{i}, x, x);
         x = t.a{2};
+        %remove bias term
+        x = x(:,2:end);
     end
 end

tests/test_example_SAE.m

@@ -32,3 +32,39 @@
 nn = nntrain(nn, train_x, train_y, opts);
 [er, bad] = nntest(nn, test_x, test_y);
 assert(er < 0.16, 'Too big error');
+
+%% ex2 train a 100-100 hidden unit SDAE and use it to initialize a FFNN
+%  Setup and train a stacked denoising autoencoder (SDAE)
+rng(0);
+sae = saesetup([784 100 100]);
+sae.ae{1}.normalize_input           = 0;
+sae.ae{1}.activation_function       = 'sigm';
+sae.ae{1}.learningRate              = 1;
+sae.ae{1}.inputZeroMaskedFraction   = 0.5;
+
+sae.ae{2}.normalize_input           = 0;
+sae.ae{2}.activation_function       = 'sigm';
+sae.ae{2}.learningRate              = 1;
+sae.ae{2}.inputZeroMaskedFraction   = 0.5;
+
+opts.numepochs =   1;
+opts.batchsize = 100;
+sae = saetrain(sae, train_x, opts);
+visualize(sae.ae{1}.W{1}(:,2:end)')
+
+% Use the SDAE to initialize a FFNN
+nn = nnsetup([784 100 100 10]);
+nn.normalize_input                  = 0;
+nn.activation_function              = 'sigm';
+nn.learningRate                     = 1;
+
+%add pretrained weights
+nn.W{1} = sae.ae{1}.W{1};
+nn.W{2} = sae.ae{2}.W{1};
+
+% Train the FFNN
+opts.numepochs =   1;
+opts.batchsize = 100;
+nn = nntrain(nn, train_x, train_y, opts);
+[er, bad] = nntest(nn, test_x, test_y);
+assert(er < 0.1, 'Too big error');