SCRN

RecurrentLayers.SCRN — Type

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

Structurally contraint recurrent unit (Mikolov et al., 2014). See SCRNCell 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.
init_context_kernel: initializer for the context to hidden weights. Default is glorot_uniform.
bias: include input to recurrent bias or not. Default is true.
recurrent_bias: include recurrent to recurrent bias or not. Default is true.
context_bias: include context to recurrent bias or not. Default is true.
independent_recurrence: flag to toggle independent recurrence. If true, the recurrent to recurrent weights are a vector instead of a matrix. Default false.
integration_mode: determines how the input and hidden projections are combined. The options are :addition and :multiplicative_integration. Defaults to :addition.
alpha: structural contraint. Default is 0.0.
return_state: Option to return the last state together with the output. Default is false.

Equations

\[\begin{aligned} \mathbf{s}(t) &= (1 - \alpha) \, \mathbf{W}_{ih}^{s} \mathbf{x}(t) + \alpha \, \mathbf{s}(t-1) \\ \mathbf{h}(t) &= \sigma\left( \mathbf{W}_{ih}^{h} \mathbf{s}(t) + \mathbf{W}_{hh}^{h} \mathbf{h}(t-1) + \mathbf{b}^{h} \right) \\ \mathbf{y}(t) &= f\left( \mathbf{W}_{hh}^{y} \mathbf{h}(t) + \mathbf{W}_{ih}^{y} \mathbf{s}(t) + \mathbf{b}^{y} \right) \end{aligned}\]

Forward

scrn(inp, (state, cstate))
scrn(inp)

Arguments

inp: The input to the scrn. It should be a vector of size input_size x len or a matrix of size input_size x len x batch_size.
(state, cstate): A tuple containing the hidden and cell states of the SCRN. They should be vectors of size hidden_size or matrices of size hidden_size x batch_size. If not provided, they are assumed to be vectors 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.

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