IndRNNCell

IndRNNCell(in_dims => out_dims, [activation];
    use_bias=true, train_state=false, init_bias=nothing,
    init_weight=nothing, init_recurrent_weight=nothing,
    init_state=zeros32)

Independently recurrent cell.

Equations

\[\begin{equation} \mathbf{h}(t) &= \sigma\left( \mathbf{W}_{ih} \mathbf{x}(t) + \mathbf{b}_{ih} + \mathbf{w}_{hh} \circ \mathbf{h}(t-1) + \mathbf{b}_{hh} \right) \end{equation}\]

Arguments

• in_dims: Input Dimension
• out_dims: Output (Hidden State & Memory) Dimension
• 'activation': Activation function. Defaults to tanh_fast

Keyword Arguments

• use_bias: Flag to use bias in the computation. Default set to true.
• train_state: Flag to set the initial hidden state as trainable. Default set to false.
• init_bias: Initializer for bias $\mathbf{b}_{ih}$. If set to nothing, weights are initialized from a uniform distribution within [-bound, bound] where bound = inv(sqrt(out_dims)). Default is nothing.
• init_recurrent_bias: Initializer for bias $\mathbf{b}_{hh}$. If set to nothing, weights are initialized from a uniform distribution within [-bound, bound] where bound = inv(sqrt(out_dims)). Default is nothing.
• init_weight: Initializer for input to hidden weight $\mathbf{W}_{ih}$. If set to nothing, weights are initialized from a uniform distribution within [-bound, bound] where bound = inv(sqrt(out_dims)). Default is nothing.
• init_recurrent_weight: Initializer for hidden to hidden weight $\mathbf{w}_{hh}$. If set to nothing, weights are initialized from a uniform distribution within [-bound, bound] where bound = inv(sqrt(out_dims)). Default is nothing.
• init_state: Initializer for hidden state. Default set to zeros32.

Inputs

• Case 1a: Only a single input x of shape (in_dims, batch_size), train_state is set to false - Creates a hidden state using init_state and proceeds to Case 2.
• Case 1b: Only a single input x of shape (in_dims, batch_size), train_state is set to true - Repeats hidden_state from parameters to match the shape of x and proceeds to Case 2.
• Case 2: Tuple (x, (h, )) is provided, then the output and a tuple containing the updated hidden state is returned.

Returns

• Tuple containing

  • Output $h_{new}$ of shape (out_dims, batch_size)
  • Tuple containing new hidden state $h_{new}$

• Updated model state

Parameters

• weight_ih: Weights to map from input space $\mathbf{W}_{ih}$.
• weight_hh: Weights to map from hidden space $\mathbf{W}_{hh}$.
• bias_ih: Bias vector for the input-hidden connection $\mathbf{b}_{ih}$ (not present if use_bias=false)
• bias_hh: Bias vector for the hidden-hidden connection $\mathbf{b}_{hh}$ (not present if use_bias=false)
• hidden_state: Initial hidden state vector (not present if train_state=false)

States

• rng: Controls the randomness (if any) in the initial state generation