Trait LossFunctionLinear 

pub trait LossFunctionLinear<'a, U, I, D, const N: usize>:
    LossFunction<U>
    + Send
    + Sync
    + 'static
where
    U: Clone + Copy + UnitValue<U>,
    D: Device<U>,
{
    type Output;

    // Required method
    fn linear_derive<'b>(
        &self,
        device: &D,
        actual: &'b I,
        expected: &'b I,
    ) -> Result<Self::Output, TrainingError>;
}

A property that defines the implementation of the loss function used in the linear layer when training a neural network.

Required Associated Types

type Output

Required Methods

fn linear_derive<'b>( &self, device: &D, actual: &'b I, expected: &'b I, ) -> Result<Self::Output, TrainingError>

Differentiation of loss functions

Arguments

• actual - actual value
• expected - expected value

Implementors

impl<'a, T, U, I, const N: usize> LossFunctionLinear<'a, U, I, DeviceCpu<U>, N> for T

where T: LossFunction<U>, U: UnitValue<U>, for<'b> ArrView<'b, U, N>: From<&'b I>,

type Output = Arr<U, N>

impl<'a, U, I, const N: usize> LossFunctionLinear<'a, U, I, DeviceGpu<U>, N> for CrossEntropy<U>

where U: Clone + Copy + UnitValue<U> + DataTypeInfo, DeviceGpu<U>: Device<U>, for<'b> CudaTensor1dPtrView<'b, U, N>: From<&'b I>, for<'b> LinearCrossEntropy<'b, U, N>: Kernel<Args = LinearCrossEntropyArgs<'b, U, N>>,

type Output = CudaTensor1dPtr<U, N>

impl<'a, U, I, const N: usize> LossFunctionLinear<'a, U, I, DeviceGpu<U>, N> for CrossEntropyMulticlass<U>

where U: Clone + Copy + UnitValue<U> + DataTypeInfo, DeviceGpu<U>: Device<U>, for<'b> CudaTensor1dPtrView<'b, U, N>: From<&'b I>, for<'b> LinearCrossEntropyMulticlass<'b, U, N>: Kernel<Args = LinearCrossEntropyMulticlassArgs<'b, U, N>>,

type Output = CudaTensor1dPtr<U, N>

impl<'a, U, I, const N: usize> LossFunctionLinear<'a, U, I, DeviceGpu<U>, N> for Mse<U>

where U: Clone + Copy + UnitValue<U> + DataTypeInfo, DeviceGpu<U>: Device<U>, for<'b> CudaTensor1dPtrView<'b, U, N>: From<&'b I>, for<'b> LinearMse<'b, U, N>: Kernel<Args = LinearMseArgs<'b, U, N>>,

type Output = CudaTensor1dPtr<U, N>