Added learning rate scaling. Thanks @marcofraccaro

Author: rasmusbergpalm

NN/nnsetup.m

@@ -9,6 +9,7 @@
     nn.activation_function              = 'tanh_opt';   %  Activation functions of hidden layers: 'sigm' (sigmoid) or 'tanh_opt' (optimal tanh).
     nn.learningRate                     = 2;            %  learning rate Note: typically needs to be lower when using 'sigm' activation function and non-normalized inputs.
     nn.momentum                         = 0.5;          %  Momentum
+    nn.scaling_learningRate             = 1;            %  Scaling factor for the learning rate (each epoch)
     nn.weightPenaltyL2                  = 0;            %  L2 regularization
     nn.nonSparsityPenalty               = 0;            %  Non sparsity penalty
     nn.sparsityTarget                   = 0.05;         %  Sparsity target

NN/nntrain.m

@@ -69,7 +69,7 @@
     end
 
     disp(['epoch ' num2str(i) '/' num2str(opts.numepochs) '. Took ' num2str(t) ' seconds' '. Mean squared error on training set is ' num2str(mean(L((n-numbatches):(n-1))))]);
-    
+    nn.learningRate = nn.learningRate * nn.scaling_learningRate;
 end
 end