[−][src]Trait auto_diff::op::OpTrait

pub trait OpTrait {
    fn get_name(&self) -> String;

    fn apply(&mut self, input: &[&Tensor], output: &[&Tensor]);

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

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

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

    fn get_grads(&self) -> Vec<&Tensor>;
}
[−]

All op is OpTrait

Required methods

fn get_name(&self) -> String

fn apply(&mut self, input: &[&Tensor], output: &[&Tensor])[−]

Forward pass

fn grad(
    &self,
    input: &[&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 get_grads(&self) -> Vec<&Tensor>[−]

access gradient values

Implementors

impl OpTrait for Linear[src][−]

impl OpTrait for Add[src][−]

impl OpTrait for Div[src][−]

impl OpTrait for Mul[src][−]

impl OpTrait for Sub[src][−]

impl OpTrait for BCEWithLogitsLoss[src][−]

fn apply(&mut self, input: &[&Tensor], output: &[&Tensor])[src][−]

The first is the prediction, the second input is the label

fn grad(
    &self,
    input: &[&Tensor],
    output_grad: &[&Tensor],
    input_grad: &[&Tensor]
)[src][−]

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

fn get_values(&self) -> Vec<&Tensor>[src][−]

access weight values

fn get_grads(&self) -> Vec<&Tensor>[src][−]

access gradient values

impl OpTrait for MSELoss[src][−]