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CFN

RecurrentLayers.CFN — Type
CFN(input_size => hidden_size;
    return_state = false, kwargs...)

Chaos free network unit (Laurent and Brecht, 2017). See CFNCell 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_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.

Equations

\[\begin{aligned} \mathbf{h}(t) &= \boldsymbol{\theta}(t) \odot \tanh\left( \mathbf{h}(t-1) \right) + \boldsymbol{\eta}(t) \odot \tanh\left( \mathbf{W}_{ih} \mathbf{x}(t) \right), \\ \boldsymbol{\theta}(t) &= \sigma\left( \mathbf{W}^{\theta}_{hh} \mathbf{h}(t-1) + \mathbf{W}^{\theta}_{ih} \mathbf{x}(t) + \mathbf{b}^{\theta} \right), \\ \boldsymbol{\eta}(t) &= \sigma\left( \mathbf{W}^{\eta}_{hh} \mathbf{h}(t-1) + \mathbf{W}^{\eta}_{ih} \mathbf{x}(t) + \mathbf{b}^{\eta} \right). \end{aligned}\]

Forward

cfn(inp, state)
cfn(inp)

Arguments

  • inp: The input to the cfn. It should be a vector of size input_size x len or a matrix of size input_size x len x batch_size.
  • state: The hidden state of the CFN. If given, it is a vector of size hidden_size or a matrix of size hidden_size x batch_size. If not provided, it is assumed to be a vector 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.
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