Why does my neural network always output 0.05?

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I'm trying to implement a neural network, replicating that from a research paper.
There are 150 input variables, with values in the range [0,1]. The net has 4 hidden layers, the first 3 have 128 nodes, the final layer 20 nodes.
As per the paper, I'm creating the network by sequentially training auto encoders, then stacking them to create the final network.
However, whatever input I provide, every value is mapped to ~0.05 in the final output.
Is this an issue anyone has seen, and are there any suggestions as to why it may be occurring?
Code below, with some random numbers added as input so it will execute.
X = rand(150, 3000);
noNodes = 128;
ae1 = trainAutoencoder(X, noNodes);
f1 = encode(ae1, X);
ae2 = trainAutoencoder(f1, noNodes, 'ScaleData', false);
f2 = encode(ae2, f1);
ae3 = trainAutoencoder(f2, noNodes, 'ScaleData', false);
f3 = encode(ae3, f2);
ae4 = trainAutoencoder(f3, 20, 'ScaleData', false);
dnn = stack(ae1, ae2, ae3, ae4);
Y = dnn(X);
  1 Comment
mctaff on 25 Feb 2017
Edited: mctaff on 25 Feb 2017
Ok, so I've established that the 0.05 is the default value for the Sparsity Proportion attribute of the trainAutoencoder function. If I amend the value of this attribute, then my network will output the amended value.
Further, I notice all the layer weights are similar, which would cause all my nodes to give a similar output.
Can anyone explain why the Matlab trainAutoencoder function is making all the weights similar, and how I can avoid this?

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Answers (1)

Vandana Rajan
Vandana Rajan on 28 Feb 2017
Could you please try the code by reducing the value of 'SparsityRegularization' (not 'SparsityProportion') or just setting it off to 0?
mctaff on 28 Feb 2017
Thanks Vandana,
I've been playing around with other input data sets over the weekend - with the above parameter set to 0 as you suggest - and have found some data that lead to a network with multiple output values... so I'm wondering if its something to do with a combination of the initial input data I was using, and the sparsity regularisation setting
Vandana Rajan
Vandana Rajan on 1 Mar 2017
Hi Mctaff,
When training a sparse autoencoder, we optimize a loss function that has two terms:
1) A mean squared error term.
2) A sparsity regulariser which tries to force the average activation value to the value defined by ‘SparsityProportion’.
The parameter ‘SparsityRegularization’ controls the weighting of the second term in the loss function. If it is too high for a particular problem, then the training procedure can collapse into a pathological solution to a problem, where it simply forces the value of the activations to match the value of ‘SparsityProportion’. This is what appears to be happening when running your code.
You can prevent this by reducing the value of ‘SparsityRegularization’, or switching it off entirely by setting it to 0.

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