OriginalLSTM

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

Original long short term memory network (Hochreiter and Schmidhuber, 1997). See OriginalLSTMCell 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.

• bias: include input to recurrent bias or not. Default is true.

• recurrent_bias: include recurrent 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.

• return_state: Option to return the last state together with the output. Default is false.

Equations

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

Forward

originallstm(inp, (state, cstate))
originallstm(inp)

Arguments

• inp: The input to the originallstm. 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 OriginalLSTM. 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.