Mini batch size for SeriesNetwork

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Hi!
I have got some issue, it seems that miniBatchSize does not divide my training data into batches, whole matrix of 2e6x15 goes though training per one iteration. I can see in options function that miniBatchSize gets a value, tried everything from 2 to 1M. Thank you for your answers :)
  2 个评论
Maria Duarte Rosa
Hi Maxim, can you provide more details on what made you conclude that the whole matrix 2e6x15 goes through training per one iteration?
Maxim Abdullayev
Maxim Abdullayev 2019-8-6
For example in the gui wondow during training process, iterations per epoch is equall to 1, and it really is equal in the graph and so on. I dont have possibility to post screenshot of training process, but here is the code. I have tried differen parameter values, but nothing works.
size = 50000;
layers = [ sequenceInputLayer(15)
% sequenceFoldingLayer
% lstmLayer(2,'OutputMode','sequence')
fullyConnectedLayer(100)
dropoutLayer(0.5)
fullyConnectedLayer(1)
% sequenceUnfoldingLayer('unfold')
regressionLayer];
validationFrequency = floor(numel(trainOUT)/size);
options = trainingOptions('adam', ... % sgdm rmsprop adam
'MaxEpochs',50, ...
'InitialLearnRate',1e-2, ...
'LearnRateSchedule','piecewise', ...
'LearnRateDropFactor',0.1, ...
'LearnRateDropPeriod',20, ...
'Shuffle','never', ...
'MiniBatchSize',size, ...
'ValidationData',{testIN',testOUT'}, ...
'ValidationFrequency',validationFrequency, ...
'ValidationPatience',5,...
'Plots','training-progress', ...
'Verbose',false);
net22 = trainNetwork(trainIN',trainOUT',layers,options);

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采纳的回答

Maria Duarte Rosa
Hi Maxim,
Thanks for providing more details.
If your data is in a D x S matrix format (D being 2e6 and S being 15) MATLAB assumes that this is a single observation problem with 15 time-series each being 2e6 points long. For each epoch, we have only 1 iteration and so the mini-batch size option is ignored because it doesn't apply to just 1 observation.
If you'd like to break the time-series into smaller chuncks of data that are treated as different observations, you can do that using the sequenceLength parameter in trainingOptions, by providing a positive integer with the desired sequence length:
Depending on how many smaller sequences this generates then the mini-batch size parameter can be used to control de size of the mini-batches as you would expect.
If you'd like to input more than one observation (each observation being D x S, i.e. D data points and S time-series) then it's better to use N x 1 cell arrays (each cell containing a D x S matrix), where N is the number of observations. Please see here for more information:
I hope this helps.

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