WMCLSTM

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

Long short term memory cell with working memory connections ^[Landi2021]. See WMCLSTM for a layer that processes a single sequence.

Arguments

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

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_memory_kernel: initializer for the hidden to hidden weights. Default is glorot_uniform.
• bias: include a bias or not. Default is true.
• return_state: Option to return the last state together with the output. Default is false.

Equations

\[\begin{aligned} \mathbf{i}(t) &= \sigma\left( \mathbf{W}^{i}_{ih} \mathbf{x}(t) + \mathbf{W}^{i}_{hh} \mathbf{h}(t-1) + \tanh\left( \mathbf{W}^{i}_{ch} \mathbf{c}(t-1) \right) + \mathbf{b}^{i} \right) \\ \mathbf{f}(t) &= \sigma\left( \mathbf{W}^{f}_{ih} \mathbf{x}(t) + \mathbf{W}^{f}_{hh} \mathbf{h}(t-1) + \tanh\left( \mathbf{W^{f}_{ch}} \mathbf{c}(t-1) \right) + \mathbf{b}^{f} \right) \\ \mathbf{o}(t) &= \sigma\left( \mathbf{W}^{o}_{ih} \mathbf{x}(t) + \mathbf{W}^{o}_{hh} \mathbf{h}(t-1) + \tanh\left( \mathbf{W}^{o}_{ch} \mathbf{c}(t) \right) + \mathbf{b}^{o} \right) \\ \mathbf{c}(t) &= \mathbf{f}(t) \circ \mathbf{c}(t-1) + \mathbf{i}(t) \circ \sigma_{c}\left( \mathbf{W}^{c}_{ih} \mathbf{x}(t) + \mathbf{b}^{c} \right) \\ \mathbf{h}(t) &= \mathbf{o}(t) \circ \sigma_{h}\left( \mathbf{c}(t) \right) \end{aligned}\]

Forward

wmclstm(inp, (state, cstate))
wmclstm(inp)

Arguments

• inp: The input to the wmclstm. 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 WMCLSTM. 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.