Struct neuronika::nn::LSTMCell

pub struct LSTMCell {
    pub weight_ih: Learnable<Ix2>,
    pub weight_hh: Learnable<Ix2>,
    pub bias_ih: Learnable<Ix1>,
    pub bias_hh: Learnable<Ix1>,
}

A long short-term memory (LSTM) cell.

Fields

weight_ih: Learnable<Ix2>

weight_hh: Learnable<Ix2>

bias_ih: Learnable<Ix1>

bias_hh: Learnable<Ix1>

Implementations

impl LSTMCell

pub fn new(input_size: usize, hidden_size: usize) -> Self

Creates a new LSTMCell.

Arguments

• input_size - number of expected features in the input.

• hidden_size - number of features in the hidden state.

All the weight and biases are initialized from U(-k, k) where k = (1. / hidden_size as f32).sqrt().

pub fn forward<Cf, Cb, Hf, Hb, I, T, U>(
    &self,
    state: (VarDiff<Cf, Cb>, VarDiff<Hf, Hb>),
    input: I
) -> (VarDiff<impl Data<Dim = Ix2> + Forward, impl Gradient<Dim = Ix2> + Overwrite + Backward>, VarDiff<impl Data<Dim = Ix2> + Forward, impl Gradient<Dim = Ix2> + Overwrite + Backward>) where
    Cf: Data<Dim = Ix2>,
    Cb: Gradient<Dim = Ix2> + Overwrite,
    Hf: Data<Dim = Ix2>,
    Hb: Gradient<Dim = Ix2> + Overwrite,
    I: MatMatMulT<Learnable<Ix2>>,
    I::Output: Into<VarDiff<T, U>>,
    T: Data<Dim = Ix2>,
    U: Gradient<Dim = Ix2> + Overwrite, 

Computes a single LSTM step.

Arguments

• state - a tuple of tensors, both of shape (batch, hidden_size), containing the initial hidden state for each element in the batch and the initial cell’s state for each element in the batch.

• input - a variable containing the input features of shape (batch, input_size).

The output is a tuple of tensors made of the next hidden state for each element in the batch, of shape (batch, hidden_size) and the next cell’s state for each element in the batch, of shape (batch, hidden_size).

Trait Implementations

impl Register for LSTMCell

fn register_params(&self, params: &mut Vec<RawParam>)

Registers the weights and the biases of this LSTMCell instance.

fn register_status(&mut self, _: Rc<Cell<bool>>)

Register self’s status to the model’s status state status.