FastGRNNCell

FastGRNNCell(input_size => hidden_size, [activation];
    init_kernel = glorot_uniform,
    init_recurrent_kernel = glorot_uniform,
    bias = true)

Fast gated recurrent neural network cell ^{[Kusupati2018]}. See FastGRNN 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_zeta: Initializer for the zeta parameter. Default is 1.0.
• init_nu: Initializer for the nu parameter. Default is - 4.0.
• bias: include a bias or not. Default is true.

Equations

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

Forward

fastgrnncell(inp, state)
fastgrnncell(inp)

Arguments

• inp: The input to the fastgrnncell. 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 FastGRNN. 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.