chainer.functions.batch_renormalization¶

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

Note: This function does not perform in-place update to running_mean and running_var, 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 access the updated running statistics, it is necessary to get a new instance of the function object, call the object, and then access the running_mean and/or running_var attributes. See the corresponding Link class for an example of how to do this.

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