Neural Network Last Layer Shows a Different Number of Outputs

조회 수: 7 (최근 30일)
Nour Aburaed
Nour Aburaed 2019년 1월 29일
편집: Nour Aburaed 2019년 1월 30일
So I have the following inputs and outputs with their respective dimenions
input: 521 x 21048
output: 15 x 21048
So basically I have 15 classes, and the network should tell me what class the input belongs to.
This is the network's configuration:
trainFcn = 'trainscg';
hiddenLayerSize = ([10 5]);
net = patternnet(hiddenLayerSize, trainFcn);
net.input.processFcns = {'removeconstantrows','mapminmax'};
net.divideFcn = 'dividerand'; % Divide data randomly
net.divideMode = 'sample'; % Divide up every sample
net.divideParam.trainRatio = 90/100;
net.divideParam.valRatio = 5/100;
net.divideParam.testRatio = 5/100;
net.performFcn = 'crossentropy'; % Cross-Entropy
net.plotFcns = {'plotperform','plottrainstate','ploterrhist', ...
'plotconfusion', 'plotroc'};
[net,tr] = train(net,x,t);
However, when I view the network, it always shows the output player with "13" outputs instead of 15!
This does not happen when I use the GUI to train the network. But as soon as I use the advanced scipt (even with the same configurations I set in the GUI) I still get 13 in the output layer, whether it is 1 layer or multiple ones.
  댓글 수: 2
Greg Heath
Greg Heath 2019년 1월 30일
I still get 15 with
close all, clear all, clc
x = randn( 521, 21048);
t = rand( 15, 21048);
YOUR CODE
view(net)
HOPE THIS HELPS
GREG
Nour Aburaed
Nour Aburaed 2019년 1월 30일
I have just figured out the problem. I have no inputs that yield to classes 10 and 5, which makes the network skip them and just stick to 13 classes. Thank you for taking time to help!

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답변 (1개)

Greg Heath
Greg Heath 2019년 1월 29일
편집: Greg Heath 2019년 1월 30일
Line 2: target, not output
trainFcn is undefined.
I get 15, not 13 in the figure
Hope this helps.
Thank you for formally accepting my answer
Greg
  댓글 수: 1
Nour Aburaed
Nour Aburaed 2019년 1월 29일
편집: Nour Aburaed 2019년 1월 29일
1- I am writing "output" in the decription only, in order to be consistent with the figure shown.
2- trainFcn is defined in my code. I will add it to my post. (otherwise the code wouldn't run to begin with)
3- I get 13. Not 15. The layer right before the last shows 13, inconsistent with the final output I am supposed to get.

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