coRNNCell

coRNNCell(input_size => hidden_size, [dt];
    gamma=0.0, epsilon=0.0,
    init_kernel = glorot_uniform,
    init_recurrent_kernel = glorot_uniform,
    bias = true)

Coupled oscillatory recurrent neural unit ^[Rusch2021]. See coRNN for a layer that processes entire sequences.

Arguments

• input_size => hidden_size: input and inner dimension of the layer.
• dt: time step. Default is 1.0.

Keyword arguments

• gamma: damping for state. Default is 0.0.
• epsilon: damping for candidate state. Default is 0.0.
• 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

\[\begin{aligned} \mathbf{z}(t) &= \mathbf{z}(t-1) + \Delta t \, \sigma \left( \mathbf{W}_{hh} \mathbf{h}(t-1) + \mathbf{W}_{zh} \mathbf{z}(t-1) + \mathbf{W}_{ih} \mathbf{x}(t) + \mathbf{b} \right) - \Delta t \, \gamma \mathbf{h}(t-1) - \Delta t \, \epsilon \mathbf{z}(t), \\ \mathbf{h}(t) &= \mathbf{h}(t-1) + \Delta t \, \mathbf{z}(t), \end{aligned}\]

Forward

cornncell(inp, (state, cstate))
cornncell(inp)

Arguments

• inp: The input to the cornncell. It should be a vector of size input_size or a matrix of size input_size x batch_size.
• (state, cstate): A tuple containing the hidden and cell states of the coRNNCell. 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

• A tuple (output, state), where output = new_state is the new hidden state and state = (new_state, new_cstate) is the new hidden and cell state. They are tensors of size hidden_size or hidden_size x batch_size.