Trait burn_core::tensor::ops::ActivationOps

pub trait ActivationOps<B>
where
    B: Backend,
{
    // Provided methods
    fn leaky_relu<const D: usize>(
        tensor: <B as Backend>::FloatTensorPrimitive<D>,
        negative_slope: <B as Backend>::FloatElem
    ) -> <B as Backend>::FloatTensorPrimitive<D> { ... }
    fn relu<const D: usize>(
        tensor: <B as Backend>::FloatTensorPrimitive<D>
    ) -> <B as Backend>::FloatTensorPrimitive<D> { ... }
    fn relu_backward<const D: usize>(
        output: <B as Backend>::FloatTensorPrimitive<D>,
        grad: <B as Backend>::FloatTensorPrimitive<D>
    ) -> <B as Backend>::FloatTensorPrimitive<D> { ... }
    fn gelu<const D: usize>(
        tensor: <B as Backend>::FloatTensorPrimitive<D>
    ) -> <B as Backend>::FloatTensorPrimitive<D> { ... }
    fn prelu<const D: usize>(
        tensor: <B as Backend>::FloatTensorPrimitive<D>,
        alpha: <B as Backend>::FloatTensorPrimitive<D>
    ) -> <B as Backend>::FloatTensorPrimitive<D> { ... }
    fn gelu_backward<const D: usize>(
        x: <B as Backend>::FloatTensorPrimitive<D>,
        grad: <B as Backend>::FloatTensorPrimitive<D>
    ) -> <B as Backend>::FloatTensorPrimitive<D> { ... }
    fn sigmoid<const D: usize>(
        tensor: <B as Backend>::FloatTensorPrimitive<D>
    ) -> <B as Backend>::FloatTensorPrimitive<D> { ... }
    fn sigmoid_backward<const D: usize>(
        output: <B as Backend>::FloatTensorPrimitive<D>,
        grad: <B as Backend>::FloatTensorPrimitive<D>
    ) -> <B as Backend>::FloatTensorPrimitive<D> { ... }
    fn log_sigmoid<const D: usize>(
        tensor: <B as Backend>::FloatTensorPrimitive<D>
    ) -> <B as Backend>::FloatTensorPrimitive<D> { ... }
    fn log_sigmoid_backward<const D: usize>(
        x: <B as Backend>::FloatTensorPrimitive<D>,
        grad: <B as Backend>::FloatTensorPrimitive<D>
    ) -> <B as Backend>::FloatTensorPrimitive<D> { ... }
}

Activation function operations.

This trait let backend implementations override activation functions for better performance.

Provided Methods

fn leaky_relu<const D: usize>( tensor: <B as Backend>::FloatTensorPrimitive<D>, negative_slope: <B as Backend>::FloatElem ) -> <B as Backend>::FloatTensorPrimitive<D>

Applies the LeakyReLU activation function.

Arguments

• tensor - The tensor.
• negative_slope - The negative_slope value that values smaller than 0 are multiplied with.

Returns

The output tensor.

fn relu<const D: usize>( tensor: <B as Backend>::FloatTensorPrimitive<D> ) -> <B as Backend>::FloatTensorPrimitive<D>

Applies the ReLU activation function.

Arguments

• tensor - The tensor.

Returns

The output tensor.

fn relu_backward<const D: usize>( output: <B as Backend>::FloatTensorPrimitive<D>, grad: <B as Backend>::FloatTensorPrimitive<D> ) -> <B as Backend>::FloatTensorPrimitive<D>

Applies the ReLU activation function backward.

Arguments

• output - The output tensor.

Returns

The gradient.

fn gelu<const D: usize>( tensor: <B as Backend>::FloatTensorPrimitive<D> ) -> <B as Backend>::FloatTensorPrimitive<D>

Applies the Gelu activation function.

Arguments

• tensor - The tensor.

Returns

The output tensor.

fn prelu<const D: usize>( tensor: <B as Backend>::FloatTensorPrimitive<D>, alpha: <B as Backend>::FloatTensorPrimitive<D> ) -> <B as Backend>::FloatTensorPrimitive<D>

Applies the PReLu activation function.

Arguments

• tensor - The input tensor
• alpha - The weight tensor

fn gelu_backward<const D: usize>( x: <B as Backend>::FloatTensorPrimitive<D>, grad: <B as Backend>::FloatTensorPrimitive<D> ) -> <B as Backend>::FloatTensorPrimitive<D>

Applies the Gelu activation function backward.

Arguments

• x - The tensor.
• grad - The gradient.

Returns

The output tensor.

fn sigmoid<const D: usize>( tensor: <B as Backend>::FloatTensorPrimitive<D> ) -> <B as Backend>::FloatTensorPrimitive<D>

Applies the Sigmoid activation function.

Arguments

• tensor - The tensor.

Returns

The output tensor.

fn sigmoid_backward<const D: usize>( output: <B as Backend>::FloatTensorPrimitive<D>, grad: <B as Backend>::FloatTensorPrimitive<D> ) -> <B as Backend>::FloatTensorPrimitive<D>

Applies the Sigmoid activation function backward.

Arguments

• output - The output tensor of the sigmoid function.
• grad - The gradient.

Returns

The output tensor.

fn log_sigmoid<const D: usize>( tensor: <B as Backend>::FloatTensorPrimitive<D> ) -> <B as Backend>::FloatTensorPrimitive<D>

Applies the LogSigmoid activation function.

Arguments

• tensor - The tensor.

Returns

The output tensor.

fn log_sigmoid_backward<const D: usize>( x: <B as Backend>::FloatTensorPrimitive<D>, grad: <B as Backend>::FloatTensorPrimitive<D> ) -> <B as Backend>::FloatTensorPrimitive<D>

Applies the LogSigmoid activation function backward.

Arguments

• x - The input tensor.
• grad - The gradient.

Returns

The output gradient.

Object Safety

This trait is not object safe.

Implementations on Foreign Types

impl<B> ActivationOps<Fusion<B>> for Fusion<B>

where B: FusionBackend,

impl<R> ActivationOps<JitBackend<R>> for JitBackend<R>

where R: Runtime,

Implementors

impl<B, C> ActivationOps<Autodiff<B, C>> for Autodiff<B, C>

where B: Backend, C: CheckpointStrategy,

impl<E> ActivationOps<LibTorch<E>> for LibTorch<E>

where E: TchElement,

impl<E> ActivationOps<NdArray<E>> for NdArray<E>

where E: FloatNdArrayElement,

impl<F, I> ActivationOps<Candle<F, I>> for Candle<F, I>

where F: FloatCandleElement, I: IntCandleElement,