FastRNNCell

FastRNNCell(input_size => hidden_size, [activation];
    init_kernel = glorot_uniform,
    init_recurrent_kernel = glorot_uniform,
    init_alpha = 3.0, init_beta = - 3.0,
    bias = true)

Fast recurrent neural network cell ^{[Kusupati2018]}. See FastRNN for a layer that processes entire sequences.

Arguments

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

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.
• 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

fastrnncell(inp, state)
fastrnncell(inp)

Arguments

• inp: The input to the fastrnncell. It should be a vector of size input_size or a matrix of size input_size x batch_size.
• state: The hidden state of the FastRNN. It should be 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

• A tuple (output, state), where both elements are given by the updated state new_state, a tensor of size hidden_size or hidden_size x batch_size.