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I'm creating mini-batches to put into a CNN. Is it best to try and get an even mix of classes into each mini-batch (Scenario 1), or can this/should this be a random assortment of my classes (Scenario 2)?

Scenario 1:

  • I have 2 classes and a mini-batch size of 32. I should try and have 16 samples from each class in each mini-batch.

Scenario 2:

  • Same as 1, but I have a random distribution of samples in each mini-batch. So this could be 20 of one class and 12 of the other. Or even 32 of one class and none of the other on occasion.
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2 Answers 2

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Theoretically, it should not matter. As long as there are a large number of mini-batches and a good balance of classes within the training data.

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If you use minibatch gradient descent , we should do random sampling. I think using just one class may lead to bias

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  • $\begingroup$ Sure thing. However, say I had 200 minibatches its quite likely that a small number of these would contain only one class. But, you suspect random is better than ensuring each has an even split of classes? $\endgroup$
    – TheyTakingTheHobbitsToIsengard
    Commented Mar 4, 2022 at 18:00

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