Can tf.contrib.layers.batch_norm calculate 2-D convolutional batch normalization as in the paper?

Question

As described in the original paper of batch normalization, batch normalization on 1-D feature (for example, from a fully connected layer) and that on 2-D feature (for example, from a convolutional layer) are different in a nontrivial way.

The tensorflow library provided an easy way to batch normalize with 1-D feature but I'm not sure if it is the same case for 2-D. The tool is tf.contrib.layers.batch_norm.

I don't fully understand this method but can we apply this method for 2-D batch normalization?

I saw some people use it on 2-D feature map (with multiple channels): example 1 (link 1, link 2).

Source page

Answer 1

You can check the usage of batch_normalization here or search for the usage of fused_bn after Tensorflow 1.0.