BRCell

BRCell(input_size => hidden_size;
    init_kernel = glorot_uniform,
    init_recurrent_kernel = glorot_uniform
    bias = true, recurrent_bias = true,
    independent_recurrence = false, integration_mode = :addition)

Bistable recurrent cell (Vecoven et al., 2021). See BR 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.
• recurrent_bias: include recurrent to recurrent bias or not. Default is true.
• independent_recurrence: hard-coded to true in this architecture. For the architecture without independent recurrence plese refer to NBRCell
• integration_mode: determines how the input and hidden projections are combined. The options are :addition and :multiplicative_integration. Defaults to :addition.

Equations

\[\begin{aligned} \mathbf{a}(t) &= 1 + \tanh\left( \mathbf{W}^{a}_{ih} \mathbf{x}(t) + \mathbf{w}^{a} \circ \mathbf{h}(t-1) + \mathbf{b}^{a} \right), \\ \mathbf{c}(t) &= \sigma\left( \mathbf{W}^{c}_{ih} \mathbf{x}(t) + \mathbf{w}^{c} \circ \mathbf{h}(t-1) + \mathbf{b}^{c} \right), \\ \mathbf{h}(t) &= \mathbf{c}(t) \circ \mathbf{h}(t-1) + \left(1 - \mathbf{c}(t)\right) \circ \tanh\left( \mathbf{W}^{h}_{ih} \mathbf{x}(t) + \mathbf{a}(t) \circ \mathbf{h}(t-1) + \mathbf{b}^{h} \right), \end{aligned}\]

Forward

brcell(inp, state)
brcell(inp)

Arguments

• inp: The input to the brcell. 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 BRCell. 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.