GatedAntisymmetricRNN

RecurrentLayers.GatedAntisymmetricRNN — Type

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

Antisymmetric recurrent neural network with gating (Chang et al., 2019). See GatedAntisymmetricRNNCell 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.
recurrent_bias: include recurrent 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.
epsilon: step size. Default is 1.0.
gamma: strength of diffusion. Default is 0.0.

Equations

\[\begin{aligned} \mathbf{z}(t) &= \sigma\left( \left( \mathbf{W}_{hh} - \mathbf{W}_{hh}^\top - \gamma \mathbf{I} \right) \mathbf{h}(t-1) + \mathbf{W}^{z}_{ih} \mathbf{x}(t) + \mathbf{b}^{z} \right), \\ \mathbf{h}(t) &= \mathbf{h}(t-1) + \epsilon \, \mathbf{z}(t) \odot \tanh\left( \left( \mathbf{W}_{hh} - \mathbf{W}_{hh}^\top - \gamma \mathbf{I} \right) \mathbf{h}(t-1) + \mathbf{W}^{h}_{ih} \mathbf{x}(t) + \mathbf{b}^{h} \right). \end{aligned}\]

Forward

asymrnn(inp, state)
asymrnn(inp)

Arguments

inp: The input to the asymrnn. 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 GatedAntisymmetricRNN. 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.

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