BatchNormInference // Adjust input for mean and variance
Description¶
Inputs¶
Name 
Element Type 
Shape 


real 
\((\bullet, C, \ldots)\) 

same as 
\((C)\) 

same as 
\((C)\) 

same as 
\((C)\) 

same as 
\((C)\) 
Attributes¶
Name 
Type 
Notes 



Small bias added to variance to avoid division by 0. 
Outputs¶
Name 
Element Type 
Shape 


same as 
Same as 
Mathematical Definition¶
The axes of the input fall into two categories: positional and channel, with channel being axis 1. For each position, there are \(C\) channel values, each normalized independently.
Normalization of a channel sample is controlled by two values:
the mean \(\mu\), and
the variance \(\sigma^2\);
and by two scaling attributes: \(\gamma\) and \(\beta\).
C++ Interface¶

class
BatchNormInference
: public ngraph::op::Op¶ 
Public Functions

const NodeTypeInfo &
get_type_info
() const¶ 
Returns the NodeTypeInfo for the node’s class. During transition to type_info, returns a dummy type_info for Node if the class has not been updated yet.

BatchNormInference
(const Output<Node> &input, const Output<Node> &gamma, const Output<Node> &beta, const Output<Node> &mean, const Output<Node> &variance, double epsilon)¶ 
 Parameters

input
: [., C, …]gamma
: gamma scaling for normalized value. [C]beta
: bias added to the scaled normalized value [C]mean
: value for mean normalization [C]variance
: value for variance normalization [C]epsilon
: Avoids divsion by 0 if input has 0 variance

NGRAPH_DEPRECATED_DOC
BatchNormInference
(double eps, const Output<Node> &gamma, const Output<Node> &beta, const Output<Node> &input, const Output<Node> &mean, const Output<Node> &variance)¶ 
In this version of BatchNorm:
MEAN AND VARIANCE: provided by the ‘mean’ and ‘variance’ parameters.
OUTPUT VALUE: a single tensor with the normalized value of ‘input’.
AUTODIFF SUPPORT:
’generate_adjoints(…) may throw an exception.
SHAPE DETAILS: gamma: must have rank 1, with the same span as input’s channel axis. beta: must have rank 1, with the same span as input’s channel axis. input: must have rank >= 2. The second dimension represents the channel axis and must have a span of at least 1. mean: must have rank 1, with the same span as input’s channel axis. variance: must have rank 1, with the same span as input’s channel axis. output: shall have the same shape as ‘input’.

void
validate_and_infer_types
()¶ 
Throws if the node is invalid.

const NodeTypeInfo &