MinimalRNN

MinimalRNN(input_size => hidden_size;
    return_state = false, kwargs...)

Minimal recurrent neural network (Chen, 2017). See MinimalRNNCell for a layer that processes a single sequence.

Arguments

• input_size => hidden_size: input and inner dimension of the layer.

Keyword arguments

• return_state: Option to return the last state together with the output. Default is false.
• init_encoder_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_memory_kernel: initializer for the memory to hidden weights. Default is glorot_uniform.
• encoder_bias: include a bias in the encoder or not. Default is true.
• recurrent_bias: include recurrent to recurrent bias or not. Default is true.
• memory_bias: include memory to recurrent bias or not. Default is true.
• independent_recurrence: flag to toggle independent recurrence. If true, the recurrent to recurrent weights are a vector instead of a matrix. Default false.
• integration_mode: determines how the input and hidden projections are combined. The options are :addition and :multiplicative_integration. Defaults to :addition.

Equations

\[\begin{aligned} \mathbf{z}(t) &= \Phi(\mathbf{x}(t)) = \tanh\left( \mathbf{W}_{xz} \mathbf{x}(t) + \mathbf{b}^{z} \right), \\ \mathbf{u}(t) &= \sigma\left( \mathbf{W}_{hh}^{u} \mathbf{h}(t-1) + \mathbf{W}_{zh}^{u} \mathbf{z}(t) + \mathbf{b}^{u} \right), \\ \mathbf{h}(t) &= \mathbf{u}(t) \circ \mathbf{h}(t-1) + \left(1 - \mathbf{u}(t)\right) \circ \mathbf{z}(t) \end{aligned}\]

Forward

minimalrnn(inp, (state, c_state))
minimalrnn(inp)

Arguments

• inp: The input to the minimalrnn. It should be a vector of size input_size x len or a matrix of size input_size x len x batch_size.
• (state, cstate): A tuple containing the hidden and cell states of the MinimalRNN. They should be vectors of size hidden_size or matrices of size hidden_size x batch_size. If not provided, they are assumed to be vectors 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.