UGRNNCell

UGRNNCell(input_size => hidden_size;
    init_kernel = glorot_uniform,
    init_recurrent_kernel = glorot_uniform,
    bias = true)

Update gate recurrent unit (Collins et al., 2017). See UGRNN for a layer that processes entire sequences.

Arguments

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

Keyword arguments

• 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

\[\begin{aligned} \mathbf{c}(t) &= s\left( \mathbf{W}_{hh}^c \, \mathbf{h}(t-1) + \mathbf{W}_{xh}^c \, \mathbf{x}(t) + \mathbf{b}^c \right), \\ \mathbf{g}(t) &= \sigma\left( \mathbf{W}_{hh}^g \, \mathbf{h}(t-1) + \mathbf{W}_{xh}^g \, \mathbf{x}(t) + \mathbf{b}^g \right), \\ \mathbf{h}(t) &= \mathbf{g}(t) \circ \mathbf{h}(t-1) + \left( 1 - \mathbf{g}(t) \right) \circ \mathbf{c}(t). \end{aligned}\]

Forward

ugrnncell(inp, state)
ugrnncell(inp)

Arguments

• inp: The input to the ugrnncell. It should be a vector of size input_size or a matrix of size input_size x batch_size.
• state: The hidden state of the UGRNNCell. It should be 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

• A tuple (output, state), where both elements are given by the updated state new_state, a tensor of size hidden_size or hidden_size x batch_size.