Struct LinearLayer 

pub struct LinearLayer {
    pub weight: Array2<f64>,
    pub bias: Array2<f64>,
    pub input_size: usize,
    pub output_size: usize,
    /* private fields */
}

A fully connected (linear/dense) layer for neural networks

Performs the transformation: output = input * weight^T + bias where weight has shape (output_size, input_size) and bias has shape (output_size, 1)

Fields

weight: Array2<f64>

bias: Array2<f64>

input_size: usize

output_size: usize

Implementations

impl LinearLayer

pub fn new(input_size: usize, output_size: usize) -> Self

Create a new linear layer with random initialization

Arguments

• input_size - Size of input features
• output_size - Size of output features

Returns

• New LinearLayer with Xavier/Glorot initialization

pub fn new_zeros(input_size: usize, output_size: usize) -> Self

Create a new linear layer with zero initialization

pub fn from_weights(weight: Array2<f64>, bias: Array2<f64>) -> Self

Create a new linear layer with custom initialization

pub fn forward(&mut self, input: &Array2<f64>) -> Array2<f64>

Forward pass through the linear layer

Arguments

• input - Input tensor of shape (input_size, batch_size)

Returns

• Output tensor of shape (output_size, batch_size)

pub fn backward( &self, grad_output: &Array2<f64>, ) -> (LinearGradients, Array2<f64>)

Backward pass through the linear layer

Arguments

• grad_output - Gradient w.r.t. output of shape (output_size, batch_size)

Returns

• Tuple of (gradients, input_gradient)
  • gradients: LinearGradients containing weight and bias gradients
  • input_gradient: Gradient w.r.t. input of shape (input_size, batch_size)

pub fn update_parameters<O: Optimizer>( &mut self, gradients: &LinearGradients, optimizer: &mut O, prefix: &str, )

Update parameters using the provided optimizer

pub fn zero_gradients(&self) -> LinearGradients

Initialize zero gradients for accumulation

pub fn num_parameters(&self) -> usize

Get the number of parameters in this layer

pub fn dimensions(&self) -> (usize, usize)

Get layer dimensions

pub fn train(&mut self)

Set the layer to training mode

pub fn eval(&mut self)

Set the layer to evaluation mode

Trait Implementations

impl Clone for LinearLayer

fn clone(&self) -> LinearLayer

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for LinearLayer

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

Formats the value using the given formatter.