AntysimmetricRNN

AntisymmetricRNN(input_size, hidden_size, [activation];
    return_state = false, kwargs...)

Antisymmetric recurrent neural network ^[Chang2019]. See AntisymmetricRNNCell for a layer that processes a single sequence.

Arguments

• input_size => hidden_size: input and inner dimension of the layer.
• activation: activation function. Default is tanh.

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
• epsilon: step size. Default is 1.0.
• gamma: strength of diffusion. Default is 0.0.

Equations

\[ \mathbf{h}(t) = \mathbf{h}(t-1) + \epsilon \tanh \left( \mathbf{W}_{ih} \mathbf{x}(t) + \left( \mathbf{W}_{hh} - \mathbf{W}_{hh}^\top - \gamma \mathbf{I} \right) \mathbf{h}(t-1) + \mathbf{b} \right)\]

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 AntisymmetricRNN. 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.