Struct Linear 

pub struct Linear { /* private fields */ }

Contract input, introduce output, and preserve all unrelated axes. Cloning a built Linear explicitly ties its parameters. Carries a learned [output] bias by default; .bias(false) before build omits it entirely, so named_parameters then has only weight and no zero-filled bias tensor is ever allocated.

Implementations

impl Linear

pub fn new(input: Axis, output: Dim) -> Self

pub fn bias(self, bias: bool) -> Self

Disable the learned bias term. Has no effect once build has already allocated parameters; call it before build.

Trait Implementations

impl Clone for Linear

fn clone(&self) -> Self

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Module for Linear

fn output_shape(&self, input: &Shape) -> Result<Shape>

fn build(&mut self, input: &Shape, device: &Device, seed: u64) -> Result<Shape>

Infer extents, validate contracts, allocate and initialize parameters; never run forward. Rebuilding with compatible extents/device preserves parameters and ignores the new seed.

fn forward(&self, input: &Tensor) -> Result<Tensor>

Evaluation semantics: no mode flag on self, no randomness beyond what forward itself already defines. This never changes with the arrival of forward_training.

fn named_parameters(&self) -> Vec<(String, Parameter)>

Structural paths identify slots; ParamId identifies shared storage within this process.

fn forward_training( &self, input: &Tensor, pass: &mut TrainingPass, ) -> Result<Tensor>

Training-mode forward pass, driven by an explicit TrainingPass rather than a flag on self. The default implementation ignores pass and calls Module::forward, which is exactly today’s behavior for every module without a train/eval distinction. Override this (never forward, which must stay evaluation semantics) to add explicit randomness or a train/eval difference, such as Dropout. A container that holds child modules (Sequential, or any future one) overrides this to thread the same pass through every child in order, so their draws stay distinct within one step.

fn parameter(&self, name: &str) -> Result<Parameter>

fn parameters(&self) -> Vec<Parameter>

fn zero_grad(&self)

fn named_states(&self) -> Vec<(String, State)>

Persistent non-parameter tensors this module owns across training steps – BatchNorm-style running statistics. Same slot-path convention as Module::named_parameters: a container (Sequential or any future one) aggregates every child’s own states under "{index}.{name}". Defaults empty, exactly like named_parameters, since most modules have no persistent state. A State here is never written directly; only TrainingPass::commit writes one, after staging in forward_training.

fn state(&self, name: &str) -> Result<State>

fn states(&self) -> Vec<State>