Batch Normalization

A primitive to perform batch normalization. More...

Classes
struct	dnnl::batch_normalization_forward
	Batch normalization forward propagation primitive. More...
struct	dnnl::batch_normalization_backward
	Batch normalization backward propagation primitive. More...
struct	dnnl_batch_normalization_desc_t
	A descriptor of a Batch Normalization operation. More...

Functions
dnnl_status_t DNNL_API	dnnl_batch_normalization_forward_desc_init (dnnl_batch_normalization_desc_t *bnrm_desc, dnnl_prop_kind_t prop_kind, const dnnl_memory_desc_t *data_desc, float epsilon, unsigned flags)
	Initializes a descriptor for a batch normalization forward propagation primitive. More...
dnnl_status_t DNNL_API	dnnl_batch_normalization_backward_desc_init (dnnl_batch_normalization_desc_t *bnrm_desc, dnnl_prop_kind_t prop_kind, const dnnl_memory_desc_t *diff_data_desc, const dnnl_memory_desc_t *data_desc, float epsilon, unsigned flags)
	Initializes a descriptor for a batch normalization backward propagation primitive. More...

Detailed Description

A primitive to perform batch normalization.

Both forward and backward propagation primitives support in-place operation; that is, src and dst can refer to the same memory for forward propagation, and diff_dst and diff_src can refer to the same memory for backward propagation.

The batch normalization primitives computations can be controlled by specifying different dnnl::normalization_flags values. For example, batch normalization can compute the mean and variance on its own or take them as inputs. It can either perform scaling and shifting using gamma and beta parameters or not. Optionally, it can also perform a fused ReLU, which in case of training would also require a workspace.

See also

Batch Normalization in developer guide

Function Documentation

dnnl_batch_normalization_forward_desc_init()

dnnl_status_t DNNL_API dnnl_batch_normalization_forward_desc_init	(	dnnl_batch_normalization_desc_t *	bnrm_desc,
		dnnl_prop_kind_t	prop_kind,
		const dnnl_memory_desc_t *	data_desc,
		float	epsilon,
		unsigned	flags
	)

Initializes a descriptor for a batch normalization forward propagation primitive.

Note

In-place operation is supported: the dst can refer to the same memory as the src.

Parameters

bnrm_desc	Output descriptor for batch normalization primitive.
prop_kind	Propagation kind. Possible values are dnnl_forward_training and dnnl_forward_inference.
data_desc	Source and destination memory descriptor.
epsilon	Batch normalization epsilon parameter.
flags	Batch normalization flags (dnnl_normalization_flags_t).

Returns

dnnl_success on success and a status describing the error otherwise.

dnnl_batch_normalization_backward_desc_init()

dnnl_status_t DNNL_API dnnl_batch_normalization_backward_desc_init	(	dnnl_batch_normalization_desc_t *	bnrm_desc,
		dnnl_prop_kind_t	prop_kind,
		const dnnl_memory_desc_t *	diff_data_desc,
		const dnnl_memory_desc_t *	data_desc,
		float	epsilon,
		unsigned	flags
	)

Initializes a descriptor for a batch normalization backward propagation primitive.

Note

In-place operation is supported: the diff_dst can refer to the same memory as the diff_src.

Parameters

bnrm_desc	Output descriptor for batch normalization primitive.
prop_kind	Propagation kind. Possible values are dnnl_backward_data and dnnl_backward (diffs for all parameters are computed in this case).
diff_data_desc	Diff source and diff destination memory descriptor.
data_desc	Source memory descriptor.
epsilon	Batch normalization epsilon parameter.
flags	Batch normalization flags (dnnl_normalization_flags_t).

Returns

dnnl_success on success and a status describing the error otherwise.