# What is a “batch” in batch normalization?

I'm working on an example of CNN with the MNIST hand-written numbers dataset. Currently I've got convolution -> pool -> dense -> dense, and for the optimiser I'm using Mini-Batch Gradient Descent with a batch size of 32.

Now this concept of batch normalization is being introduced. We are supposed to take a "batch" after or before a layer, and normalize it by subtracting its mean, and dividing by its standard deviation.

So what is a "batch"? If I feed a sample into a 32 kernel conv layer, I get 32 feature maps.

• Is each feature map a "batch"?
• Are the 32 feature maps the "batch"?

Or, if I'm doing Mini-Batch Gradient Descent with a batch size of 64,

• Are 64 sets of 32 feature maps the "batch"? So in other words, the batch from Mini-Batch Gradient Descent, is the same as the "batch" from batch-optimization?

Or is a "batch" something else that I've missed?