Struct auto_diff::var::Module

pub struct Module { /* private fields */ }

Implementations

impl Module

Network holder.

pub fn new() -> Module

Create an empty module. A module is mainly used to create new variables.

pub fn var(&mut self) -> Var

Create a new variable.

pub fn var_value(&mut self, v: Tensor) -> Var

pub fn func<F: 'static>(&self, closure: F) -> Func where
    F: Fn(&[&Var]) -> Var, 

Create a composed function with the given closure.

pub fn rm_var(&mut self, var: &Var)

pub fn eval(&self)

Try best evaluation of the computation graph.

pub fn forward(&self)

Same as eval

pub fn backward_vector(&self, og: &BTreeMap<NetIndex, Tensor>)

Back propagation

pub fn backward(&self, og: f32)

Back propgation with a single value.

pub fn _visit_data<F>(&self, closure: F) where
    F: Fn(NetIndex, &Tensor), 

iterator over all data node.

pub fn _visit_op<F>(&self, closure: F) where
    F: Fn(&Op), 

iterator over all op node.

pub fn add(&self) -> Func

pub fn sub(&self) -> Func

pub fn mul(&self) -> Func

pub fn div(&self) -> Func

pub fn conv2d(
    &self,
    in_channels: usize,
    out_channels: usize,
    kernel_size: (usize, usize),
    stride: (usize, usize),
    padding: (usize, usize),
    dilation: (usize, usize),
    bias: bool,
    padding_mode: PaddingMode
) -> Func

pub fn linear(
    &self,
    in_features: Option<usize>,
    out_features: Option<usize>,
    bias: bool
) -> Func

pub fn elu(&self, alpha: f32) -> Func

pub fn relu(&self) -> Func

pub fn sigmoid(&self) -> Func

pub fn mse_loss(&self) -> Func

pub fn bce_with_logits_loss(&self) -> Func

pub fn cross_entropy_loss(&self) -> Func