FastRNN

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

Fast recurrent neural network ^{[Kusupati2018]}. See FastRNNCell for a layer that processes a single sequences.

Arguments

• input_size => hidden_size: input and inner dimension of the layer.
• activation: the activation function, defaults to tanh_fast.

Keyword arguments

• return_state: Option to return the last state together with the output. Default is false.

• 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_alpha: Initializer for the alpha parameter. Default is 3.0.

• init_beta: Initializer for the beta parameter. Default is - 3.0.

• bias: include a bias or not. Default is true.

Equations

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

Forward

fastrnn(inp, state)
fastrnn(inp)

Arguments

• inp: The input to the fastrnn. 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 FastRNN. 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.