A primitive to perform batch normalization

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

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, double 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, double 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,
		double	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,
		double	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

Functions

Detailed Description

Function Documentation

◆ mkldnn_batch_normalization_backward_desc_init()

◆ mkldnn_batch_normalization_forward_desc_init()