Enum LossReduction 

#[repr(u8)]
pub enum LossReduction {
    None = 0,
    Mean = 1,
    Sum = 2,
}

Loss reduction mode. Selects the output shape and the final scalar scaling for a LossKind plan. PyTorch’s reduction parameter.

Variants

None = 0

Output is per-cell (same shape as the loss surface). No reduction.

Mean = 1

Output is a scalar — sum of per-cell terms divided by element count.

Sum = 2

Output is a scalar — sum of per-cell terms (no divide).

Trait Implementations

impl Clone for LossReduction

fn clone(&self) -> LossReduction

Returns a duplicate of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Copy for LossReduction

impl Debug for LossReduction

fn fmt(&self, f: &mut Formatter<'_>) -> Result<(), Error>

Formats the value using the given formatter.

impl Eq for LossReduction

impl Hash for LossReduction

fn hash<__H>(&self, state: &mut __H)

where __H: Hasher,

Feeds this value into the given Hasher.

fn hash_slice<H>(data: &[Self], state: &mut H)

where H: Hasher, Self: Sized,

Feeds a slice of this type into the given Hasher.

impl PartialEq for LossReduction

fn eq(&self, other: &LossReduction) -> bool

Tests for self and other values to be equal, and is used by ==.

fn ne(&self, other: &Rhs) -> bool

Tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.

impl StructuralPartialEq for LossReduction