[−][src]Struct onednn_sys::dnnl_batch_normalization_desc_t

#[repr(C)]pub struct dnnl_batch_normalization_desc_t {
    pub primitive_kind: dnnl_primitive_kind_t,
    pub prop_kind: dnnl_prop_kind_t,
    pub data_desc: dnnl_memory_desc_t,
    pub diff_data_desc: dnnl_memory_desc_t,
    pub data_scaleshift_desc: dnnl_memory_desc_t,
    pub diff_data_scaleshift_desc: dnnl_memory_desc_t,
    pub stat_desc: dnnl_memory_desc_t,
    pub batch_norm_epsilon: f32,
    pub flags: c_uint,
}

A descriptor of a Batch Normalization operation.

Fields

primitive_kind: dnnl_primitive_kind_t

The kind of primitive. Used for self-identifying the primitive descriptor. Must be #dnnl_batch_normalization.

prop_kind: dnnl_prop_kind_t

The kind of propagation. Possible values: #dnnl_forward_training, #dnnl_forward_inference, #dnnl_backward, and #dnnl_backward_data.

data_desc: dnnl_memory_desc_t

Source and destination memory descriptor.

diff_data_desc: dnnl_memory_desc_t

Source and destination gradient memory descriptor.

data_scaleshift_desc: dnnl_memory_desc_t

Scale and shift data and gradient memory descriptors.

Scaleshift memory descriptor uses 2D #dnnl_nc format[2,Channels]. 1-st dimension contains gamma parameter, 2-nd dimension contains beta parameter.

diff_data_scaleshift_desc: dnnl_memory_desc_tstat_desc: dnnl_memory_desc_t

Statistics memory descriptor.

Statistics (mean or variance) descriptor use 1D #dnnl_x format[Channels].

batch_norm_epsilon: f32

Batch normalization epsilon parameter.

flags: c_uint

Trait Implementations

impl Clone for dnnl_batch_normalization_desc_t[src]

impl Copy for dnnl_batch_normalization_desc_t[src]

Auto Trait Implementations

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impl<T> Any for T where
    T: 'static + ?Sized, [src]

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    T: ?Sized, [src]

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impl<T> From<T> for T[src]

impl<T, U> Into<U> for T where
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    T: Clone, [src]

type Owned = T

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