Recurrent Layers

Recurrent layers process sequential data by maintaining hidden state across time steps. They are essential for tasks involving sequences: language modeling, speech recognition, time series prediction, and more.

RNN: Basic recurrent layer (simple but prone to vanishing gradients)
LSTM: Long Short-Term Memory (gated architecture, handles long-range dependencies)
GRU: Gated Recurrent Unit (simpler than LSTM, often similar performance)
Cell variants: Single-step versions for custom loop implementations

Key parameters:

input_size: Number of features in input
hidden_size: Number of features in hidden state
num_layers: Number of stacked recurrent layers
batch_first: If true, input shape is [batch, seq, features]
bidirectional: Process sequence in both directions

RNN

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Example:

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auto rnn = torch::nn::RNN(
    torch::nn::RNNOptions(128, 256)  // input_size, hidden_size
        .num_layers(2)
        .batch_first(true)
        .bidirectional(false));

auto input = torch::randn({32, 10, 128});  // [batch, seq_len, input_size]
auto [output, hidden] = rnn->forward(input);

LSTM

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Example:

cpp

auto lstm = torch::nn::LSTM(
    torch::nn::LSTMOptions(128, 256)
        .num_layers(2)
        .batch_first(true)
        .dropout(0.1)
        .bidirectional(true));

auto input = torch::randn({32, 10, 128});
auto [output, state] = lstm->forward(input);
auto [h_n, c_n] = state;  // hidden state, cell state

GRU

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RNNCell

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LSTMCell

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GRUCell

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