Struct burn::nn::Dropout

pub struct Dropout {
    pub prob: f64,
}

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).

Should be created with DropoutConfig.

Fields

prob: f64

The probability of randomly zeroes some elements of the input tensor during training.

Implementations

impl Dropout

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

where B: Backend,

Applies the forward pass on the input tensor.

See Dropout for more information.

Shapes

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

Trait Implementations

impl<B> AutodiffModule<B> for Dropout

where B: AutodiffBackend,

type InnerModule = Dropout

Inner module without auto-differentiation.

fn valid(&self) -> <Dropout as AutodiffModule<B>>::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<(), Error>

Formats the value using the given formatter.

impl Display for Dropout

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

Formats the value using the given formatter.

impl<B> Module<B> for Dropout

where B: Backend,

type Record = ConstantRecord

Type to save and load the module.

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

where V: ModuleVisitor<B>,

Visit each tensor parameter in the module with a visitor.

fn map<M>(self, _mapper: &mut M) -> Dropout

where M: ModuleMapper<B>,

Map each tensor parameter in the module with a mapper.

fn load_record(self, _record: <Dropout as Module<B>>::Record) -> Dropout

Load the module state from a record.

fn into_record(self) -> <Dropout as Module<B>>::Record

Convert the module into a record containing the state.

fn to_device(self, _: &<B as Backend>::Device) -> Dropout

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

fn fork(self, _: &<B as Backend>::Device) -> Dropout

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

fn collect_devices( &self, devices: Vec<<B as Backend>::Device>, ) -> Vec<<B as Backend>::Device>

Return all the devices found in the underneath module tree added to the given vector without duplicates.

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

Return all the devices found in the underneath module tree without duplicates.

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.

fn save_file<FR, PB>( self, file_path: PB, recorder: &FR, ) -> Result<(), RecorderError>

where FR: FileRecorder<B>, PB: Into<PathBuf>,

Save the module to a file using the provided file recorder.

fn load_file<FR, PB>( self, file_path: PB, recorder: &FR, device: &<B as Backend>::Device, ) -> Result<Self, RecorderError>

where FR: FileRecorder<B>, PB: Into<PathBuf>,

Load the module from a file using the provided file recorder.

fn quantize_weights<C>(self, quantizer: &mut Quantizer<C>) -> Self

where C: Calibration,

Quantize the weights of the module.

impl ModuleDisplay for Dropout

fn custom_settings(&self) -> Option<DisplaySettings>

Custom display settings for the module.

fn custom_content(&self, content: Content) -> Option<Content>

Custom attributes for the module.

fn format(&self, passed_settings: DisplaySettings) -> String

Formats the module with provided display settings.

impl ModuleDisplayDefault for Dropout

fn content(&self, content: Content) -> Option<Content>

Attributes of the module used for display purposes.

fn num_params(&self) -> usize

Gets the number of the parameters of the module.