Struct LogSoftmaxLayer 

pub struct LogSoftmaxLayer { /* private fields */ }

layer to apply an operation

Implementations

impl LogSoftmaxLayer

pub fn get_dim(&self) -> i32

gets the dimension

pub fn get_temperature(&self) -> f32

gets the temperature

pub fn new(temperature: f32) -> Self

creates a new layer

pub fn set_dim(&mut self, dim: i32)

sets the dimension

pub fn set_temperature(&mut self, temperature: f32)

sets the mismatch behavior. A temperature of NaN will make the non soft version if possible. A finite temperature will make the soft version

pub fn with_dim(self, dim: i32) -> Self

sets the dimension

pub fn with_temperature(self, temperature: f32) -> Self

sets the temperature. A temperature of NaN will make the non soft version if possible. A finite temperature will make the soft version

Trait Implementations

impl AI<Vec<f32>, Vec<f32>> for LogSoftmaxLayer

fn forward(&self, input: Vec<f32>) -> Vec<f32>

applies to the input

fn forward_mut(&mut self, input: X) -> Y

applies to the input, possibly updating internal caches

impl Clone for LogSoftmaxLayer

fn clone(&self) -> LogSoftmaxLayer

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for LogSoftmaxLayer

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

Formats the value using the given formatter.

impl Decompose for LogSoftmaxLayer

type Decomposition = (i32, f32)

the decomposed type

fn compose((dim, temperature): Self::Decomposition) -> Self

recreates from the decomposition

fn decompose(self) -> Self::Decomposition

owned decomposition

fn decompose_cloned(&self) -> Self::Decomposition

decomposition that copies data

impl Default for LogSoftmaxLayer

fn default() -> Self

Returns the “default value” for a type.

impl<'de> Deserialize<'de> for LogSoftmaxLayer

fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error>

where __D: Deserializer<'de>,

Deserialize this value from the given Serde deserializer.

impl Op for LogSoftmaxLayer

type Output = Vec<f32>

suggested output type to help with composition coherence. Ideally, Self should implement AI<X,Self::Output> for some X

fn abnormal_softmax(self, temperature: f32) -> AbnormalSoftmax<Self>

where Self: Sized, AbnormalSoftmax<Self>: Op,

wraps with a softmax operation

fn acc_q(self, gamma: f32) -> AccQ<Self>

where AccQ<Self>: Op, Self: Sized,

wraps with a accq operation

fn chain<B>(self, b: B) -> Sequential<(Self, B)>

where Self: Sized, Sequential<(Self, B)>: Op,

sequences with another ai operation

fn duplicate(self) -> Duplicate<Self>

where Duplicate<Self>: Op, Self: Sized,

wraps with a duplicate operation

fn fix_type<Z>(self) -> SetType<Self, Z, Z>

where Self: AI<Z, Z> + Sized,

set type but with the same input and output

fn flatten<R: Clone>(self, args: R) -> Flatten<Self, R>

where Flatten<Self, R>: Op, Self: Sized,

wraps with a flatten layer

fn forward_fixed<Z>(&self, input: Z) -> Z

where Self: AI<Z, Z> + Sized,

applies to the input

fn forward_fixed_mut<Z>(&mut self, input: Z) -> Z

where Self: AI<Z, Z> + Sized,

applies to the input

fn forward_typed<W, Z>(&self, input: W) -> Z

where Self: AI<W, Z> + Sized,

applies to the input

fn forward_typed_mut<W, Z>(&mut self, input: W) -> Z

where Self: AI<W, Z> + Sized,

applies to the input, possibly updating internal caches

fn infer_autoregressive<X, Y>(self, input: X) -> Autoregression<Self, Y>

where Self: AI<X, Y> + AI<Y, Y> + Sized, Y: Clone,

creates an autoregressive inference

fn log_softmax(self, temperature: f32) -> LogSoftmax<Self>

where Self: Sized, LogSoftmax<Self>: Op,

wraps with a softmax operation

fn map<B>(self, b: B) -> Map<Sequential<(Self, B)>>

where Map<Sequential<(Self, B)>>: Op, Self: Sized, Sequential<(Self, B)>: Op,

applies the operation to every output

fn mean(self) -> Mean<Self>

where Mean<Self>: Op, Self: Sized,

wraps with a mean operation

fn optional(self) -> Option<Self>

where Self: Sized,

creates an optional operation

fn reshape<R: Clone>(self, args: R) -> Reshape<Self, R>

where Reshape<Self, R>: Op, Self: Sized,

wraps with a reshape layer

fn residual(self) -> Residual<Self>

where Residual<Self>: Op, Self: Sized,

wraps in a residual layer

fn set_type<W, Z>(self) -> SetType<Self, W, Z>

where Self: AI<W, Z> + Sized,

sets the input output types

fn softmax(self, temperature: f32) -> Softmax<Self>

where Self: Sized, Softmax<Self>: Op,

wraps with a softmax operation

fn squared_error(self) -> SquaredError<Self>

where SquaredError<Self>: Op, Self: Sized,

wraps with a mse operation

fn squeeze(self, dim: i32) -> Squeeze<Self>

where Squeeze<Self>: Op, Self: Sized,

wraps with a squeeze operation

fn to_each(self) -> Map<Self>

where Map<Self>: Op, Self: Sized,

wraps with a map operation

fn unsqueeze(self, dim: i32) -> Unsqueeze<Self>

where Unsqueeze<Self>: Op, Self: Sized,

wraps with a unsqueeze operation

fn sum(self) -> Sum<Self>

where Sum<Self>: Op, Self: Sized,

wraps with a sum operation

fn wrap_inner(self) -> Inner<Self>

where Self: Sized,

wraps the inner value so it can be unwrapped with unwrap inner

fn zip<B>(self, b: B) -> Zip<(Self, B)>

where Self: Sized, Zip<(Self, B)>: Op,

zips with another ai operation

impl PartialEq for LogSoftmaxLayer

fn eq(&self, other: &LogSoftmaxLayer) -> 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 Serialize for LogSoftmaxLayer

fn serialize<__S>(&self, __serializer: __S) -> Result<__S::Ok, __S::Error>

where __S: Serializer,

Serialize this value into the given Serde serializer.

impl Copy for LogSoftmaxLayer

impl StructuralPartialEq for LogSoftmaxLayer