Intel(R) Math Kernel Library for Deep Neural Networks (Intel(R) MKL-DNN)  0.13
Performance library for Deep Learning
Functions
Batch Normalization

A primitive to perform batch normalization

\[dst[n][c][h][w] = \gamma[c] \frac{src[n][c][h][w] - \mu[c]} {\sqrt{\sigma[c] + eps}} + \beta[c],\]

. More...

Functions

mkldnn_status_t MKLDNN_API mkldnn_batch_normalization_forward_desc_init (mkldnn_batch_normalization_desc_t *bnrm_desc, mkldnn_prop_kind_t prop_kind, const mkldnn_memory_desc_t *data_desc, float epsilon, unsigned flags)
 Initializes a batch normalization descriptor bnrm_desc for forward propagation using prop_kind, (possible values are mkldnn_forward_training or mkldnn_forward_inference), memory descriptor data_desc, normalization parameter epsilon and flags (possible values are mkldnn_use_global_stats and mkldnn_use_scaleshift). More...
 
mkldnn_status_t MKLDNN_API mkldnn_batch_normalization_backward_desc_init (mkldnn_batch_normalization_desc_t *bnrm_desc, mkldnn_prop_kind_t prop_kind, const mkldnn_memory_desc_t *diff_data_desc, const mkldnn_memory_desc_t *data_desc, float epsilon, unsigned flags)
 Initializes a batch normalization descriptor bnrm_desc for backward propagation with respect to data and scale-shift parameters using memory descriptors data_desc and diff_data_desc, and normalization parameter epsilon and flags (possible values are mkldnn_use_global_stats and mkldnn_use_scaleshift). More...
 

Detailed Description

A primitive to perform batch normalization

\[dst[n][c][h][w] = \gamma[c] \frac{src[n][c][h][w] - \mu[c]} {\sqrt{\sigma[c] + eps}} + \beta[c],\]

.

where $\gamma[c], \beta[c]$ are weights and bias for a channel and,

$\mu[c] = \frac{1}{NHW} \sum\limits_{whn} src[n][c][h][w]$, $\sigma[c] = \frac{1}{NHW} \sum\limits_{whn} (src[n][c][h][w] - \mu[c])^2$,

and eps is a constant to improve numerical stability.

Function Documentation

◆ mkldnn_batch_normalization_backward_desc_init()

mkldnn_status_t MKLDNN_API mkldnn_batch_normalization_backward_desc_init ( mkldnn_batch_normalization_desc_t bnrm_desc,
mkldnn_prop_kind_t  prop_kind,
const mkldnn_memory_desc_t diff_data_desc,
const mkldnn_memory_desc_t data_desc,
float  epsilon,
unsigned  flags 
)

Initializes a batch normalization descriptor bnrm_desc for backward propagation with respect to data and scale-shift parameters using memory descriptors data_desc and diff_data_desc, and normalization parameter epsilon and flags (possible values are mkldnn_use_global_stats and mkldnn_use_scaleshift).

See also
mkldnn_batch_normalization_desc_t

◆ mkldnn_batch_normalization_forward_desc_init()

mkldnn_status_t MKLDNN_API mkldnn_batch_normalization_forward_desc_init ( mkldnn_batch_normalization_desc_t bnrm_desc,
mkldnn_prop_kind_t  prop_kind,
const mkldnn_memory_desc_t data_desc,
float  epsilon,
unsigned  flags 
)

Initializes a batch normalization descriptor bnrm_desc for forward propagation using prop_kind, (possible values are mkldnn_forward_training or mkldnn_forward_inference), memory descriptor data_desc, normalization parameter epsilon and flags (possible values are mkldnn_use_global_stats and mkldnn_use_scaleshift).

See also
mkldnn_batch_normalization_desc_t