LiGRU

LiGRU(input_size => hidden_size;
    return_state = false, kwargs...)

Light gated recurrent network ^{[Ravanelli2018]}. The implementation does not include the batch normalization as described in the original paper. See LiGRUCell for a layer that processes a single sequence.

Arguments

• input_size => hidden_size: input and inner dimension of the layer.

Keyword arguments

• return_state: Option to return the last state together with the output. Default is false.
• init_kernel: initializer for the input to hidden weights. Default is glorot_uniform.
• init_recurrent_kernel: initializer for the hidden to hidden weights. Default is glorot_uniform.
• bias: include a bias or not. Default is true.

Equations

\[\begin{aligned} \mathbf{z}(t) &= \sigma\left( \mathbf{W}^{z}_{ih} \mathbf{x}(t) + \mathbf{W}^{z}_{hh} \mathbf{h}(t-1) + \mathbf{b}^{z} \right), \\ \tilde{\mathbf{h}}(t) &= \text{ReLU}\left( \mathbf{W}^{h}_{ih} \mathbf{x}(t) + \mathbf{W}^{h}_{hh} \mathbf{h}(t-1) + \mathbf{b}^{h} \right), \\ \mathbf{h}(t) &= \mathbf{z}(t) \odot \mathbf{h}(t-1) + \left(1 - \mathbf{z}(t)\right) \odot \tilde{\mathbf{h}}(t) \end{aligned}\]

Forward

ligru(inp, state)
ligru(inp)

Arguments

• inp: The input to the ligru. It should be a vector of size input_size x len or a matrix of size input_size x len x batch_size.
• state: The hidden state of the LiGRU. If given, it is a vector of size hidden_size or a matrix of size hidden_size x batch_size. If not provided, it is assumed to be a vector of zeros, initialized by Flux.initialstates.

Returns

• New hidden states new_states as an array of size hidden_size x len x batch_size. When return_state = true it returns a tuple of the hidden stats new_states and the last state of the iteration.