Struct Dropout 

pub struct Dropout<F>
where
    F: Float + Debug + Send + Sync + NumAssign,
{ /* private fields */ }

Dropout layer

During training, randomly sets input elements to zero with probability p. During inference, scales the output by 1/(1-p) to maintain the expected value.

Implementations

impl<F> Dropout<F>

where F: Float + Debug + ScalarOperand + Send + Sync + 'static + NumAssign,

pub fn new<R>(p: f64, rng: &mut R) -> Result<Dropout<F>, NeuralError>

where R: Rng + 'static + Clone + Send + Sync,

Create a new dropout layer

Arguments

• p - Dropout probability (0.0 to 1.0)
• rng - Random number generator

pub fn set_training(&mut self, training: bool)

Set the training mode In training mode, elements are randomly dropped. In inference mode, all elements are kept but scaled.

pub fn p(&self) -> f64

Get the dropout probability

pub fn is_training(&self) -> bool

Get the training mode

Trait Implementations

impl<F> Clone for Dropout<F>

where F: Float + Debug + Send + Sync + NumAssign,

fn clone(&self) -> Dropout<F>

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl<F> Debug for Dropout<F>

where F: Float + Debug + Send + Sync + NumAssign,

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

Formats the value using the given formatter.

impl<F> Layer<F> for Dropout<F>

where F: Float + Debug + ScalarOperand + Send + Sync + 'static + NumAssign,

fn forward( &self, input: &ArrayBase<OwnedRepr<F>, Dim<IxDynImpl>>, ) -> Result<ArrayBase<OwnedRepr<F>, Dim<IxDynImpl>>, NeuralError>

Forward pass of the layer

fn backward( &self, _input: &ArrayBase<OwnedRepr<F>, Dim<IxDynImpl>>, grad_output: &ArrayBase<OwnedRepr<F>, Dim<IxDynImpl>>, ) -> Result<ArrayBase<OwnedRepr<F>, Dim<IxDynImpl>>, NeuralError>

Backward pass of the layer to compute gradients

fn update(&mut self, _learningrate: F) -> Result<(), NeuralError>

Update the layer parameters with the given learning rate

fn as_any(&self) -> &(dyn Any + 'static)

Get the layer as a dyn Any for downcasting

fn as_any_mut(&mut self) -> &mut (dyn Any + 'static)

Get the layer as a mutable dyn Any for downcasting

fn set_training(&mut self, training: bool)

Set the layer to training mode (true) or evaluation mode (false)

fn is_training(&self) -> bool

Get the current training mode

fn layer_type(&self) -> &str

Get the type of the layer (e.g., “Dense”, “Conv2D”)

fn parameter_count(&self) -> usize

Get the number of trainable parameters in this layer

fn layer_description(&self) -> String

Get a detailed description of this layer

fn params(&self) -> Vec<ArrayBase<OwnedRepr<F>, Dim<IxDynImpl>>>

Get the parameters of the layer

fn gradients(&self) -> Vec<ArrayBase<OwnedRepr<F>, Dim<IxDynImpl>>>

Get the gradients of the layer parameters

fn set_gradients( &mut self, _gradients: &[ArrayBase<OwnedRepr<F>, Dim<IxDynImpl>>], ) -> Result<(), NeuralError>

Set the gradients of the layer parameters

fn set_params( &mut self, _params: &[ArrayBase<OwnedRepr<F>, Dim<IxDynImpl>>], ) -> Result<(), NeuralError>

Set the parameters of the layer

fn inputshape(&self) -> Option<Vec<usize>>

Get the input shape if known

fn outputshape(&self) -> Option<Vec<usize>>

Get the output shape if known

fn name(&self) -> Option<&str>

Get the name of the layer if set

impl<F> Send for Dropout<F>

where F: Float + Debug + Send + Sync + NumAssign,

impl<F> Sync for Dropout<F>

where F: Float + Debug + Send + Sync + NumAssign,