Struct Linear

pub struct Linear { /* private fields */ }

Implementations

impl Linear

pub fn new( in_features: Option<usize>, out_features: Option<usize>, bias: bool, ) -> Linear

pub fn weight(&self) -> &Tensor

pub fn set_weight(&self, var: Var)

pub fn bias(&self) -> &Tensor

pub fn set_bias(&self, var: Var)

Trait Implementations

impl<'de> Deserialize<'de> for Linear

fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error>

where __D: Deserializer<'de>,

Deserialize this value from the given Serde deserializer.

impl OpCall for Linear

fn call(&mut self, inputs: &[&Var]) -> Result<Vec<Var>, AutoDiffError>

impl OpTrait for Linear

fn get_grads(&self) -> Vec<Tensor>

access gradient values

fn get_name(&self) -> &'static str

A conventional name for the op

fn get_input_size(&self) -> usize

The number of input needs by this op.

fn get_output_size(&self) -> usize

The number of output produced by this op.

fn apply(&self, inputs: &[Tensor], outputs: &[Tensor])

Forward pass

fn grad(&self, inputs: &[Tensor], output_grad: &[Tensor], input_grad: &[Tensor])

Given the forward input value and backward output_grad, Update weight gradient. return backward input gradeint.

fn get_values(&self) -> Vec<Tensor>

access weight values

fn set_values(&self, v: &[Tensor])

fn as_any(&self) -> &dyn Any

impl Serialize for Linear

fn serialize<__S>(&self, __serializer: __S) -> Result<__S::Ok, __S::Error>

where __S: Serializer,

Serialize this value into the given Serde serializer.