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why mini batch accuracy (value) graph of training is goes down during training process?

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Shishir Singhal
Shishir Singhal 2020-5-22

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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.
Moreover, You can also refer to this link : https://www.mathworks.com/matlabcentral/answers/484326-mini-batch-loss-and-accuracy-trends?s_tid=answers_rc1-2_p2_MLT
If your case is similar like that.

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Raza Ali
Raza Ali 2020-5-22
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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