SCRN

SCRN(input_size => hidden_size;
    init_kernel = glorot_uniform,
    init_recurrent_kernel = glorot_uniform,
    bias = true, alpha = 0.0,
    return_state = false)

Structurally contraint recurrent unit ^{[Mikolov2015]}. 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.
• bias: include a bias or not. Default is true.
• 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.