Single-layer GRU – gated recurrence with a reset-gated candidate. More...

#include <optional>
#include <initializer_list>
#include <vector>
#include "pulsatrix/module.hpp"

Include dependency graph for gru_module.hpp:

Classes
class	pulsatrix::GRUModule
	Standard GRU recurrence (Cho et al. 2014), h_0 = 0 (zero-initialized, not learnable – the same deliberate scope cut RNNModule/LSTMModule made, and the same thing that makes this module's conservation exact; see the LRP note below): z_t = sigmoid(x_t @ W_xz + h_{t-1} @ W_hz + b_z) (update gate), r_t = sigmoid(x_t @ W_xr + h_{t-1} @ W_hr + b_r) (reset gate), hn_prev_t = h_{t-1} @ W_hn (internal projection, no bias), n_t = tanh(x_t @ W_xn + r_t * hn_prev_t + b_n) (candidate), h_t = (1 - z_t) * h_{t-1} + z_t * n_t. Note the reset gate multiplies the projected previous hidden state `hn_prev_t`, not `h_{t-1}` itself – that projection is a distinct cached intermediate, and it is what gives GRU's LRP rule a different shape from LSTM's. Input (N, L, input_size) -> output (N, L, hidden_size), the full hidden-state sequence (matches RNNModule's/LSTMModule's convention). Single layer, no bidirectional/multi-layer/variable-length support. More...

Detailed Description

Single-layer GRU – gated recurrence with a reset-gated candidate.

Classes