Neural Network Transfer Function
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Hi friends,
I was using neural network to train a set of sensing data. The transfer function is designed to be 'logsig'. The input is a n*4 matrix values between 0 to 3, output is a n*3 matrix values between 0 to 10.
The training process is normal. But after we have the resultant network, when applying, output values are no less than 5 which is the average of my target output. That is to say, for all values supposed to be lower than 5, it returns 5.
Code will be the following: net1=feedforwardnet([80,80]); net1.trainParam.max_fail=10; net1.layers{3}.transferFcn='logsig'; net1.layers{1}.transferFcn='logsig'; net1.layers{2}.transferFcn='logsig'; net1=train(net1,transpose(det),transpose(pos));
Thank you all in advance.
Yichang
回答（2 个）
Greg Heath
2018-2-18
编辑：Greg Heath
2018-2-18
There is no good reason to differ from the default
help fitnet % Form of feedforwardnet for regression & curvefitting)
doc fitnet
given matrix sizes
[ I N ] = size(input) % [ 4 N ]
[ O N ] = size(target) % [ 3 N ]
Default number of training equations
Ntrneq = 0.7*N*O = 2.1*N
and number of unknown weights for a single hidden layer with H nodes yielding Nw unknown weights and biases
Nw = (I+1)*H+(H+1)*O = (4+1+3)*H + 3 = 8*H+3
For more training equations than unknowns
Ntrneq >= Nw
or
H <= (Ntrneq -O) /( I + O +1)=(2.1*N-3)/8
Therefore, the default value of H = 10 can be used if
N >= 40 .
Hope this helps.
Thank you for formally accepting my answer
Greg
Akshay Joshi
2018-2-16
编辑：Akshay Joshi
2018-2-16
Try the following:
net1.layers{1}.transferFcn='logsig';
net1.layers{2}.transferFcn='logsig';
net1.layers{3}.transferFcn='purelin'
In many cases, using non-linear function for classification at hidden layer, and then using linear function at output layer yields a proper, classified output data.
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2018-2-18
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