Why is the size of the input weight matrix sometimes smaller than the input length when training a neural network?
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I have a question regarding the size of the inut weight matrix for a neural network. My IW Matrix is smaller than expected and I don't know why. What I do:
net=patternnet(1);
[net,tr]=train(net,inputs,targets);
net.IW %size of the input weight matrices
ans =
[1x14 double]
[]
net.inputs.size %size of my inputs
ans =
[15]
net.layers.size %size of my hidden and output layer
ans =
[1]
[2]
As far as I understood, the size of my input weight matrix should be 1 (size of hidden layer) by 15 (length of input vectors). I tried it several times with different input sizes, but the size of IW sometimes is equal or 1-2 smaller than my input size.
I want to know why this happens and how I can match the weights to the input variables. Thanks in advance, Antje
2 Kommentare
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Antje
am 6 Sep. 2012
5 Kommentare
enjy fikry
am 5 Mai 2017
how can i stop that from happening ? i don't want the training process to ignore these constant columns
Greg Heath
am 5 Mai 2017
You should.
They have zero variance.
Therefore they cannot contribute to learning.
However, they can confuse those who do not understand this.
Hope this helps.
Greg
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