Struct relearn::torch::seq_modules::Stacked

pub struct Stacked<'a, T, U> {
    pub seq: T,
    pub activation: Option<Func<'a>>,
    pub top: U,
}

A module stacked on top of a sequence.

Fields

seq: T

The sequence module.

activation: Option<Func<'a>>

An optional activation function in between

top: U

A module applied to the sequence module outputs.

Implementations

impl<'a, T, U> Stacked<'a, T, U>

pub const fn new(seq: T, activation: Option<Func<'a>>, top: U) -> Self

Trait Implementations

impl<'a, T, U> CudnnSupport for Stacked<'a, T, U> where
    T: CudnnSupport,
    U: CudnnSupport, 

fn has_cudnn_second_derivatives(&self) -> bool

Whether cuDNN supports second derivatives of this module.

impl<'a, T: Debug, U: Debug> Debug for Stacked<'a, T, U>

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

Formats the value using the given formatter.

impl<'a, T, U> IterativeModule for Stacked<'a, T, U> where
    T: IterativeModule,
    U: Module, 

type State = T::State

Internal state of the module.

fn initial_state(&self, batch_size: usize) -> Self::State

Construct an initial state for the start of a new sequence.

fn step(&self, input: &Tensor, state: &Self::State) -> (Tensor, Self::State)

Apply one step of the module.

impl<'a, T, U> SequenceModule for Stacked<'a, T, U> where
    T: SequenceModule,
    U: Module, 

fn seq_serial(&self, inputs: &Tensor, seq_lengths: &[usize]) -> Tensor

Apply the network over multiple sequences arranged in series one after another.

fn seq_packed(&self, inputs: &Tensor, batch_sizes: &Tensor) -> Tensor

Apply the network over multiple sequences packed together in heterogeneous batches.