Struct burn_core::nn::Dropout

pub struct Dropout { /* private fields */ }

Set at random some elements of the input tensor to zero during training.

This is an effective regularization technique as describe in the paper Improving neural networks by preventing co-adaptation of feature detectors.

The input is also scaled during training to 1 / (1 - prob_keep).

Implementations

impl Dropout

pub fn forward<B: Backend, const D: usize>( &self, input: Tensor<B, D> ) -> Tensor<B, D>

Applies the forward pass on the input tensor.

Shapes

• input: [..., any]
• output: [..., any]

Trait Implementations

impl<B: ADBackend> ADModule<B> for Dropout

type InnerModule = Dropout

fn valid(&self) -> Self::InnerModule

Get the same module, but on the inner backend without auto-differentiation.

impl Clone for Dropout

fn clone(&self) -> Dropout

Returns a copy of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for Dropout

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl<B: Backend> Module<B> for Dropout

type Record = ()

Type to save and load the module.

fn visit<V: ModuleVisitor<B>>(&self, _visitor: &mut V)

Visit each tensor in the module with a visitor.

fn map<M: ModuleMapper<B>>(self, _mapper: &mut M) -> Self

Map each tensor in the module with a mapper.

fn load_record(self, _record: Self::Record) -> Self

Load the module state from a record.

fn into_record(self) -> Self::Record

Convert the module into a record containing the state.

fn devices(&self) -> Vec<B::Device>

Get the device list of the module and all of its sub-modules.

fn fork(self, device: &B::Device) -> Self

Fork the module and all of its sub-modules to the given device.

fn to_device(self, device: &B::Device) -> Self

Move the module and all of its sub-modules to the given device.

fn no_grad(self) -> Self

Each tensor in the module tree will not require grad.

fn num_params(&self) -> usize

Get the number of parameters the module has, including all of its sub-modules.