Neural Network Transfer Function

Question

James He 2017-2-7

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编辑： Greg Heath 2018-2-18

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

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Answer 1

Greg Heath 2018-2-18

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编辑：Greg Heath 2018-2-18

在 MATLAB Online 中打开

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

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Answer 2

Akshay Joshi 2018-2-16

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编辑：Akshay Joshi 2018-2-16

在 MATLAB Online 中打开

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.