BackwardAll

nncombinator::layer

Trait BackwardAll

pub trait BackwardAll<U>: PreTrain<U> + UpdateWeight<U>where
    U: UnitValue<U>,{
    type LossInput: Debug;
    type LossOutput: Debug;

    // Required method
    fn backward_all<L: LossFunction<U>>(
        &mut self,
        input: Self::LossInput,
        stack: Self::OutStack,
        lossf: &L,
    ) -> Result<(<Self as BackwardAll<U>>::LossOutput, <Self as UpdateWeight<U>>::GradientStack), TrainingError>;

    // Provided method
    fn is_canonical_link<L: LossFunction<U>>(&self, _: &L) -> bool { ... }
}

Expand description

Trait defining the implementation of error back propagation in neural networks

Required Associated Types§

type LossInput: Debug

Losses during neural network training

type LossOutput: Debug

Losses in the top layer during neural network training

Required Methods§

fn backward_all<L: LossFunction<U>>( &mut self, input: Self::LossInput, stack: Self::OutStack, lossf: &L, ) -> Result<(<Self as BackwardAll<U>>::LossOutput, <Self as UpdateWeight<U>>::GradientStack), TrainingError>

Back propagation of errors

§Arguments

input - loss
stack - Stack to store calculation results at upper layers
lossf - loss function

§Errors

This function may return the following errors

TrainingError

Provided Methods§

fn is_canonical_link<L: LossFunction<U>>(&self, _: &L) -> bool

Dyn Compatibility§

This trait is not dyn compatible.

In older versions of Rust, dyn compatibility was called "object safety", so this trait is not object safe.

Implementors§

impl<U, C, BC, P, D, I, PI, OP, const NI: usize, const NO: usize> BackwardAll<U> for LinearLayer<U, C, BC, P, D, I, PI, OP, NI, NO>
where P: BackwardAll<U, LossInput = PI> + ForwardAll<Input = I, Output = PI> + PreTrain<U, PreOutput = PI> + Loss<U>, U: Default + Clone + Copy + Send + UnitValue<U>, I: Debug + Send + Sync, C: Debug, BC: Debug, OP: Optimizer<U, D>, PI: Debug + BatchDataType + From<<D as DeviceLinear<U, C, BC, PI, NI, NO>>::LossOutput>, D: Device<U> + DeviceLinear<U, C, BC, PI, NI, NO>, for<'a, 'a> &'a <OP as Optimizer<U, D>>::InternalType: From<&'a C> + From<&'a BC>, for<'a, 'a> <OP as Optimizer<U, D>>::InternalUpdateType<'a>: From<&'a mut C> + From<&'a mut BC>,

type LossInput = <D as DeviceLinear<U, C, BC, PI, NI, NO>>::Output

type LossOutput = <P as BackwardAll<U>>::LossOutput

impl<U, C, BC, P, D, OP, I, const NI: usize, const NO: usize> BackwardAll<U> for DiffLinearLayer<U, C, BC, P, OP, D, I, NI, NO>
where P: BackwardAll<U, LossInput = ()> + ForwardAll<Input = I, Output = DiffInput<DiffArr<U, NI>, U, NI, NO>> + PreTrain<U, PreOutput = DiffInput<DiffArr<U, NI>, U, NI, NO>> + Loss<U>, U: Default + Clone + Copy + UnitValue<U>, I: Debug + Send + Sync, C: Debug, BC: Debug + From<<D as DeviceDiffLinear<U, C, BC, NI, NO>>::Output>, OP: Optimizer<U, D>, D: Device<U> + DeviceDiffLinear<U, C, BC, NI, NO>, <D as DeviceDiffLinear<U, C, BC, NI, NO>>::Output: Debug + 'static, for<'a, 'a> &'a <OP as Optimizer<U, D>>::InternalType: From<&'a C> + From<&'a BC>, for<'a, 'a> <OP as Optimizer<U, D>>::InternalUpdateType<'a>: From<&'a mut C> + From<&'a mut BC>, Self: ForwardAll + PreTrain<U, OutStack = Cons<<P as PreTrain<U>>::OutStack, BC>>,

type LossInput = <D as DeviceDiffLinear<U, C, BC, NI, NO>>::Output

type LossOutput = <P as BackwardAll<U>>::LossOutput

impl<U, C, P, OP, D, I, PI, S, const N: usize> BackwardAll<U> for BatchNormalizationLayer<U, C, P, OP, D, I, PI, S, N>
where P: ForwardAll<Input = I, Output = PI> + BackwardAll<U, LossInput = PI> + PreTrain<U, PreOutput = PI> + Loss<U>, U: Default + Clone + Copy + Send + UnitValue<U>, I: Debug + Send + Sync, PI: BatchDataType + Debug + 'static, S: Debug + Sized + 'static, C: Debug, OP: Optimizer<U, D>, D: Device<U> + DeviceBatchNorm<U, C, PI, N>, <PI as BatchDataType>::Type: Debug + 'static, for<'a> &'a <OP as Optimizer<U, D>>::InternalType: From<&'a C>, for<'a> <OP as Optimizer<U, D>>::InternalUpdateType<'a>: From<&'a mut C>,

type LossInput = PI

type LossOutput = <P as BackwardAll<U>>::LossOutput

impl<U, O, LI, D> BackwardAll<U> for InputLayer<U, O, LI, D>
where U: UnitValue<U>, O: Debug + BatchDataType + Send + Sync + 'static, LI: Debug, D: Device<U> + DeviceInput<U, O>, <O as BatchDataType>::Type: Debug + 'static,

type LossInput = LI

type LossOutput = LI

impl<U, P, A, I, PI, D, const N: usize> BackwardAll<U> for ActivationLayer<U, P, A, I, PI, D, N>
where P: PreTrain<U, PreOutput = PI> + ForwardAll<Input = I, Output = PI> + BackwardAll<U, LossInput = PI> + Loss<U>, U: Default + Clone + Copy + UnitValue<U>, D: Device<U> + DeviceActivation<U, PI, A, N>, PI: Debug + BatchDataType, I: Debug + Send + Sync,

type LossInput = PI

type LossOutput = <P as BackwardAll<U>>::LossOutput

impl<U, P, D, I, PI, const N: usize> BackwardAll<U> for LinearOutputLayer<U, P, D, I, PI, N>
where P: BackwardAll<U, LossInput = PI> + ForwardAll<Input = I, Output = PI> + PreTrain<U, PreOutput = PI> + Loss<U>, U: Default + Clone + Copy + UnitValue<U>, PI: Debug + BatchDataType + ToHost<U, Output = Arr<U, N>> + 'static, I: Debug + Send + Sync, <PI as ToHost<U>>::Output: Debug + 'static, for<'a> D: Device<U> + DeviceLinearOutput<'a, U, N, IO = PI>,

type LossInput = PI

type LossOutput = <P as BackwardAll<U>>::LossOutput

impl<U, P, I, PI, CI, D> BackwardAll<U> for BridgeLayer<U, P, I, PI, CI, D>
where P: PreTrain<U, PreOutput = PI> + ForwardAll<Input = I, Output = PI> + BackwardAll<U, LossInput = PI> + Loss<U>, U: Default + Clone + Copy + UnitValue<U>, D: Device<U>, PI: Debug + From<CI>, CI: Debug + 'static, I: Debug + Send + Sync,

type LossInput = CI

type LossOutput = <P as BackwardAll<U>>::LossOutput

impl<U, P, OP, D, I, PI, const N: usize> BackwardAll<U> for BiasLayer<U, Arr<U, N>, P, OP, D, I, PI, N>
where P: BackwardAll<U, LossInput = PI> + ForwardAll<Input = I, Output = PI> + PreTrain<U, PreOutput = PI> + Loss<U>, U: Default + Clone + Copy + Send + UnitValue<U>, D: Device<U> + DeviceBias<U, Arr<U, N>, PI, N>, I: Debug + Send + Sync, PI: Debug + BatchDataType + 'static, OP: Optimizer<U, D>, <PI as BatchDataType>::Type: Debug + BatchSize + 'static, for<'a> &'a <OP as Optimizer<U, D>>::InternalType: From<&'a Arr<U, N>>, for<'a> <OP as Optimizer<U, D>>::InternalUpdateType<'a>: From<&'a mut Arr<U, N>>,

type LossInput = PI

type LossOutput = <P as BackwardAll<U>>::LossOutput