chainer.functions.batch_renormalization(x, gamma, beta, rmax, dmax, eps=2e-05, running_mean=None, running_var=None, decay=0.9, update_statistics=False)[source]

Batch renormalization function.

This is an extension of batch normalization, which ensures that the training and inference models generate the same outputs that depend on individual examples rather than the entire minibatch.


This function does not perform in-place update to running_mean and running_var by default, contrary to batch_normalization(). If the function is called, it will not be possible to access the updated running mean and variance statistics, because they are members of the function object, which cannot be accessed by the caller. If it is desired to update the running statistics, call the function with update_statistics=True option.

See: Batch Renormalization: Towards Reducing Minibatch Dependence in Batch-Normalized Models