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NN training process?

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Raza Ali
Raza Ali on 18 May 2020
Commented: Raza Ali on 22 May 2020
why mini batch accuracy (value) graph of training is goes down during training process?

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Accepted Answer

Shishir Singhal
Shishir Singhal on 22 May 2020
Hi,
Mini batch accuracy should likely to increase with no. of epochs.
But for your case, there can be of multiple reasons behind this:
  • Mini-batch size
  • Learning rate
  • cost function.
  • Network Architechture
  • Quality of data and lot more.
It would be better if you provide more information about the NN model you are using.
If your case is similar like that.

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Raza Ali
Raza Ali on 22 May 2020
Thank you for your reply.
plz see the detail below
Network = [
imageInputLayer([256 256 3],"Name","imageinput")
convolution2dLayer([3 3],32,"Name","conv_1","BiasLearnRateFactor",2,"Padding","same")
reluLayer("Name","relu_1")
batchNormalizationLayer("Name","batchnorm")
convolution2dLayer([3 3],64,"Name","conv_2","BiasLearnRateFactor",2,"Padding","same")
reluLayer("Name","relu_2")
transposedConv2dLayer([3 3],2,"Name","transposed-conv","Cropping","same")
softmaxLayer("Name","softmax")
dicePixelClassificationLayer("Name","dice-pixel-class")];
options = trainingOptions('sgdm', ...
'LearnRateSchedule','piecewise',...
'LearnRateDropPeriod',10,...
'LearnRateDropFactor',0.3,...
'Momentum',0.9, ...
'InitialLearnRate',1e-3, ...
'L2Regularization',0.005, ...
'MaxEpochs',30, ...
'MiniBatchSize',2, ...
'Shuffle','every-epoch', ...
'VerboseFrequency',2,...
'Plots','training-progress');

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