Table Of Contents
Table Of Contents



Batch normalization.

Normalizes a data batch by mean and variance, and applies a scale gamma as well as offset beta.

Assume the input has more than one dimension and we normalize along axis 1. We first compute the mean and variance along this axis:

\[\begin{split} data\_mean[i] = mean(data[:,i,:,...]) \\ data\_var[i] = var(data[:,i,:,...])\end{split}\]

Then compute the normalized output, which has the same shape as input, as following:

\[out[:,i,:,...] = \frac{data[:,i,:,...] - data\_mean[i]}{\sqrt{data\_var[i]+\epsilon}} * gamma[i] + beta[i]\]

Both mean and var returns a scalar by treating the input as a vector.

Assume the input has size k on axis 1, then both gamma and beta have shape (k,). If output_mean_var is set to be true, then outputs both data_mean and the inverse of data_var, which are needed for the backward pass. Note that gradient of these two outputs are blocked.

Besides the inputs and the outputs, this operator accepts two auxiliary states, moving_mean and moving_var, which are k-length vectors. They are global statistics for the whole dataset, which are updated by:

moving_mean = moving_mean * momentum + data_mean * (1 - momentum)
moving_var = moving_var * momentum + data_var * (1 - momentum)

If ``use_global_stats`` is set to be true, then ``moving_mean`` and
``moving_var`` are used instead of ``data_mean`` and ``data_var`` to compute
the output. It is often used during inference.

The parameter ``axis`` specifies which axis of the input shape denotes
the 'channel' (separately normalized groups).  The default is 1.  Specifying -1 sets the channel
axis to be the last item in the input shape.

Both ``gamma`` and ``beta`` are learnable parameters. But if ``fix_gamma`` is true,
then set ``gamma`` to 1 and its gradient to 0.


When fix_gamma is set to True, no sparse support is provided. If fix_gamma is set to False,
the sparse tensors will fallback.






Input data to batch normalization


NDArray-or-Symbol gamma array


NDArray-or-Symbol beta array


NDArray-or-Symbol running mean of input


NDArray-or-Symbol running variance of input


double, optional, default=0.001.

Epsilon to prevent div 0. Must be no less than CUDNN_BN_MIN_EPSILON defined in cudnn.h when using cudnn (usually 1e-5)


float, optional, default=0.9.

Momentum for moving average


boolean, optional, default=1.

Fix gamma while training

boolean, optional, default=0.

Whether use global moving statistics instead of local batch-norm. This will force change batch-norm into a scale shift operator.


boolean, optional, default=0.

Output the mean and inverse std


int, optional, default=‘1’.

Specify which shape axis the channel is specified

boolean, optional, default=0.

Do not select CUDNN operator, if available