SGRN

SGRN(input_size, hidden_size;
    return_state = false, kwargs...)

Simple gated recurrent network ^[Zu2020]. See SGRNCell for a layer that processes a single sequence.

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

\[\begin{aligned} \mathbf{f}(t) &= \sigma\left( \mathbf{W}_{ih} \mathbf{x}(t) + \mathbf{W}_{hh} \mathbf{h}(t-1) + \mathbf{b} \right) \\ \mathbf{i}(t) &= 1 - \mathbf{f}(t) \\ \mathbf{h}(t) &= \tanh\left( \mathbf{i}(t) \circ \left( \mathbf{W}_{ih} \mathbf{x}(t) \right) + \mathbf{f}(t) \circ \mathbf{h}(t-1) \right) \end{aligned}\]

Forward

sgrn(inp, state)
sgrn(inp)

Arguments

• inp: The input to the sgrn. 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 SGRN. 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.