Trait Backward 

pub trait Backward<X, Delta = X> {
    type Elem;

    // Required method
    fn backward(&mut self, input: &X, delta: &Delta, gamma: Self::Elem);
}

The Backward trait establishes a common interface for completing a single backward step in a neural network or machine learning model.

Required Associated Types

type Elem

Required Methods

fn backward(&mut self, input: &X, delta: &Delta, gamma: Self::Elem)

Implementations on Foreign Types

impl<A, S, D, S1, D1, S2, D2> Backward<ArrayBase<S1, D1, A>, ArrayBase<S2, D2, A>> for ArrayBase<S, D, A>

where A: 'static + Copy + Num, D: Dimension, S: DataMut<Elem = A>, D1: Dimension, D2: Dimension, S1: Data<Elem = A>, S2: Data<Elem = A>, &'b ArrayBase<S1, D1, A>: for<'b> Dot<ArrayBase<ViewRepr<&'b A>, D2>, Output = ArrayBase<OwnedRepr<A>, D2>>,

type Elem = A

fn backward( &mut self, input: &ArrayBase<S1, D1, A>, delta: &ArrayBase<S2, D2, A>, gamma: <ArrayBase<S, D, A> as Backward<ArrayBase<S1, D1, A>, ArrayBase<S2, D2, A>>>::Elem, )

Implementors

impl<A, D1, D2, S1, S2> Backward<ArrayBase<S1, D1, A>, ArrayBase<S2, D2, A>> for ParamsBase<OwnedRepr<A>, D1, A>

where A: 'static + Copy + Num, D1: RemoveAxis<Smaller = D2>, D2: Dimension<Larger = D1>, S1: Data<Elem = A>, S2: Data<Elem = A>, &'b ArrayBase<S1, D1, A>: for<'b> Dot<ArrayBase<ViewRepr<&'b A>, D2>, Output = ArrayBase<OwnedRepr<A>, D2>>,

type Elem = A

impl<A, S, T> Backward<ArrayBase<S, Dim<[usize; 0]>, A>, ArrayBase<T, Dim<[usize; 0]>, A>> for ParamsBase<OwnedRepr<A>, Dim<[usize; 1]>, A>

where A: Float + FromPrimitive + ScalarOperand, S: Data<Elem = A>, T: Data<Elem = A>,

type Elem = A

impl<A, S, T> Backward<ArrayBase<S, Dim<[usize; 1]>, A>, ArrayBase<T, Dim<[usize; 1]>, A>> for ParamsBase<OwnedRepr<A>, Dim<[usize; 2]>, A>

where A: Float + FromPrimitive + ScalarOperand, S: Data<Elem = A>, T: Data<Elem = A>,

type Elem = A