Struct Dropout 

pub struct Dropout { /* private fields */ }

Dropout is a builder for Dropout regularization which is a technique that randomly sets a fraction of the weights to zero during training, effectively “dropping out” some neurons. This helps to prevent overfitting by introducing noise and forcing the network to learn more robust features.

Dropout is typically applied after the activation function in each layer during the forward propagation step.

The dropoutRate parameter determines the fraction of weights to be set to zero. A higher dropout rate means more weights will be dropped, which can help reduce overfitting but may also make the training process slower and more difficult.

Dropout is commonly used in deep neural networks with many layers and parameters, as these networks are more prone to overfitting.

Implementations

impl Dropout

pub fn dropout_rate(self, dropout_rate: f32) -> Self

Set the dropout rate.

Specifies the fraction of input units to randomly set to zero during training. A higher rate increases regularization strength.

Parameters

• dropout_rate: Fraction of units to drop, in [0.0, 1.0] (e.g., 0.3 for 30%).

pub fn seed(self, seed: u64) -> Self

Set the random seed for reproducibility.

Fixes the random number generator used for dropout to ensure consistent results across runs.

Parameters

• seed: Random seed value.

pub fn build(self) -> Result<Box<dyn Regularization>, NetworkError>

Trait Implementations

impl Default for Dropout

fn default() -> Self

Creates a new builder with default values Default values:

• dropout_rate: 0.5
• seed: None (random seed)