MATLAB: Hidden Layer Activations in NN Toolbox

Deep Learning Toolboxhidden layer activationsneural network

I'm looking for a non-manual way to compute the layer activations of an arbitrary neural network created with the Neural Network Toolbox.
Consider following example for detailed problem description:
[x,t] = simplefit_dataset;       % toy data
net   = fitnet(5);               % initialize network
net.inputs{1}.processFcns = {};  % don't pre-process data
net.outputs{2}.processFcns = {}; % don't post-process data
net   = train(net,x,t);          % train network
The output of the network can be obtained using the sim function or manually:
testX = 0.5;                  % test value
testYnntbx  = sim(net,testX)  % automatic computation of network output
testYmanual = net.LW{2} ...   % manual computation of network output 
              *(tansig(net.IW{1}*testX+net.b{1})) ...
              +net.b{2}
The activations of the neurons in the hidden layer are:
testAmanual = tansig(net.IW{1}*testX+net.b{1})
I'm looking for a way to get the layer activations without manually specifying the equations, analogous to the sim function.

Best Answer

You can create a 2nd net with no hidden layer. Next, make the output layer of the second net to be the same as the hidden layer of the first net.
net2 = fitnet([]);
will create a net with no hidden layer.
If the target matrix is not 5 dimensional, create a 5-fimensional target so that you can configure the correct topology. If t is 1-dimensional use
net2 = configure(net2,x, repmat(t,5,1));
Now you can replace the random initial weights of net2 with the hidden weights of net1.
I have not tried this. Therefore, there may be some details for you to work out.
Another approach might be to reproduce net1, net3 = net1, then remove the outer layer of net3. I can't see how to do this, but it may be possible.
Hope this helps.
Thank you for formally accepting my answer
Greg
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