TRNNCell

TRNNCell(in_dims => out_dims;
    use_bias=true, train_state=false, init_bias=nothing,
    init_weight=nothing, init_state=zeros32)

Strongly typed recurrent unit.

Equations

\[\begin{aligned} \mathbf{z}(t) &= \mathbf{W}_{ih}^{z} \mathbf{x}(t) + \mathbf{b}_{ih}^{z}, \\ \mathbf{f}(t) &= \sigma\left( \mathbf{W}_{ih}^{f} \mathbf{x}(t) + \mathbf{b}_{ih}^{f} \right), \\ \mathbf{h}(t) &= \mathbf{f}(t) \circ \mathbf{h}(t-1) + \left(1 - \mathbf{f}(t)\right) \circ \mathbf{z}(t). \end{aligned}\]

Arguments

• in_dims: Input Dimension
• out_dims: Output (Hidden State & Memory) Dimension

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 input-to-hidden biases $\mathbf{b}_{ih}^{z}, \mathbf{b}_{ih}^{f}$. Must be a tuple containing 2 functions. If a single value is passed, it is copied into a 2-element tuple. If set to nothing, biases are initialized from a uniform distribution within [-bound, bound], where bound = \mathrm{inv}(\sqrt{\mathrm{out\_dims}}). The functions are applied in order: the first initializes $\mathbf{b}_{ih}^{z}$, the second $\mathbf{b}_{ih}^{f}$. Default set to nothing.
• init_weight: Initializer for input-to-hidden weights $\mathbf{W}_{ih}^{z}, \mathbf{W}_{ih}^{f}$. Must be a tuple containing 2 functions. If a single value is passed, it is copied into a 2-element tuple. If set to nothing, weights are initialized from a uniform distribution within [-bound, bound], where bound = \mathrm{inv}(\sqrt{\mathrm{out\_dims}}). The functions are applied in order: the first initializes $\mathbf{W}_{ih}^{z}$, the second $\mathbf{W}_{ih}^{f}$. Default set to nothing.
• init_state: Initializer for hidden state. Default set to zeros32.
• init_memory: Initializer for memory. 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: Concatenated weights to map from input space $\{ \mathbf{W}_{ih}^{z}, \mathbf{W}_{ih}^{f} \}$
• bias_ih: Concatenated bias vector for input-hidden connections $\{ \mathbf{b}_{ih}^{z}, \mathbf{b}_{ih}^{f} \}$ (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