Trait yarnn::optimizer::Optimizable

pub trait Optimizable<N, B: Backend<N>, O: Optimizer<N, B>> {
    fn calc_gradients(
        &mut self, 
        backend: &B, 
        inputs: &B::Tensor, 
        deltas: &B::Tensor
    );
    fn optimize(&mut self, backend: &B, optimizer: &O);
}

Required methods

fn calc_gradients(
    &mut self,
    backend: &B,
    inputs: &B::Tensor,
    deltas: &B::Tensor
)

fn optimize(&mut self, backend: &B, optimizer: &O)

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Implementors

impl<N, B, O> Optimizable<N, B, O> for Conv2d<N, B, O> where
    B: Backend<N> + BackendConv2d<N> + BackendBias<N> + BackendScale<N>,
    O: Optimizer<N, B>, 

fn calc_gradients(&mut self, backend: &B, x: &B::Tensor, dy: &B::Tensor)

fn optimize(&mut self, backend: &B, optimizer: &O)

impl<N, B, O> Optimizable<N, B, O> for Linear<N, B, O> where
    B: Backend<N> + BackendGemm<N> + BackendBias<N> + BackendScale<N>,
    O: Optimizer<N, B>, 

fn calc_gradients(
    &mut self,
    backend: &B,
    inputs: &B::Tensor,
    deltas: &B::Tensor
)

fn optimize(&mut self, backend: &B, optimizer: &O)

impl<T, N, B, O> Optimizable<N, B, O> for T where
    T: Layer<N, B>,
    B: Backend<N>,
    O: Optimizer<N, B>, 

Temporary solution until I find a solution with problem of inference with specializations

default fn calc_gradients(
    &mut self,
    _backend: &B,
    _inputs: &B::Tensor,
    _deltas: &B::Tensor
)

default fn optimize(&mut self, _backend: &B, _optimizer: &O)

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