# mx.symbol.BatchNorm_v1¶

## Description¶

Batch normalization.

This operator is DEPRECATED. Perform BatchNorm on the input.

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 data_var as well, which are needed for the backward pass.

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.

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

There's no sparse support for this operator, and it will exhibit problematic behavior if used with
sparse tensors.


## Usage¶

mx.symbol.BatchNorm_v1(...)


## Arguments¶

Argument

Description

data

NDArray-or-Symbol.

Input data to batch normalization

gamma

NDArray-or-Symbol gamma array

beta

NDArray-or-Symbol beta array

eps

float, optional, default=0.001.

Epsilon to prevent div 0

momentum

float, optional, default=0.9.

Momentum for moving average

fix.gamma

boolean, optional, default=1.

Fix gamma while training

use.global.stats

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.

output.mean.var

boolean, optional, default=0.

Output All,normal mean and var

name

string, optional.

Name of the resulting symbol.

## Value¶

out The result mx.symbol