Struct gad::net::Map

pub struct Map<N, F>(_, _);

The result of Net::map

Trait Implementations

impl<N: Clone, F: Clone> Clone for Map<N, F>

fn clone(&self) -> Map<N, F>

Returns a copy of the value.

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

Performs copy-assignment from source.

impl<N: Debug, F: Debug> Debug for Map<N, F>

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

Formats the value using the given formatter.

impl<Algebra, N, F, O> Net<Algebra> for Map<N, F> where
    Algebra: HasGradientReader,
    N: Net<Algebra>,
    F: Fn(&mut Algebra, N::Output) -> Result<O>, 

type Input = N::Input

Input of the network.

type Output = O

Output of the network.

type Weights = N::Weights

External representation for the weights of the network.

type GradientInfo = N::GradientInfo

How to read the gradients of the weights after a backward pass.

fn eval_with_gradient_info(
    &self,
    graph: &mut Algebra,
    input: Self::Input
) -> Result<(Self::Output, Self::GradientInfo)>

fn get_weights(&self) -> Self::Weights

fn set_weights(&mut self, weights: Self::Weights) -> Result<()>

fn update_weights(&mut self, delta: Self::Weights) -> Result<()>

fn read_weight_gradients(
    &self,
    info: Self::GradientInfo,
    reader: &Algebra::GradientReader
) -> Result<Self::Weights>

fn eval(&self, graph: &mut Algebra, input: Self::Input) -> Result<Self::Output>

fn map<F, O>(self, f: F) -> Map<Self, F> where
    Self: Sized,
    F: Fn(&mut Algebra, Self::Output) -> Result<O>, 

fn using<N>(self, net: N) -> Using<Self, N> where
    Self: Sized,
    N: Net<Algebra, Input = ()>, 

fn then<N>(self, net: N) -> Then<Self, N> where
    Self: Sized,
    N: Net<Algebra, Input = Self::Output>, 

fn and<N>(self, net: N) -> (Self, N) where
    Self: Sized,
    N: Net<Algebra>,