GatedRecurrentLayer

GatedRecurrentLayer[n]

represents a trainable recurrent layer that takes a sequence of vectors and produces a sequence of vectors each of size n.

GatedRecurrentLayer[n,opts]

includes options for initial weights and other parameters.

Details and Options

  • GatedRecurrentLayer[n] represents a net that takes a sequence of vectors and outputs a sequence of the same length.
  • Each element of the input sequence is a vector of size k, and each element of the output sequence is a vector of size n.
  • The size k of the input vectors is usually inferred automatically within a NetGraph, NetChain, etc.
  • The input and output ports of the net represented by GatedRecurrentLayer[n] are:
  • "Input"a sequence of vectors of size k
    "Output"a sequence of vectors of size n
  • Given an input sequence {x1,x2,,xT}, a GatedRecurrentLayer outputs a sequence of states {s1,s2,,sT} using the following recurrence relation:
  • input gateit=LogisticSigmoid[Wix.xt+Wis.st-1+bi]
    reset gatert=LogisticSigmoid[Wrx.xt+Wrs.st-1+br]
    memory gatemt=Tanh[Wmx.xt+rt*(Wms.st-1)+bm]
    statest=(1-it)*mt+it*st-1
  • The above definition of GatedRecurrentLayer is based on the variant described in Chung et al., Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling, 2014.
  • GatedRecurrentLayer[n] also has a single state port, "State", which is a vector of size n.
  • Within a NetGraph, a connection of the form src->NetPort[layer,"State"] can be used to provide the initial value of the state for a GatedRecurrentLayer, corresponding to s0 in the recurrence relation. The default initial value is a zero vector.
  • Within a NetGraph, a connection of the form NetPort[layer,"State"]->dst can be used to obtain the final value of the state for a GatedRecurrentLayer, corresponding to sT in the recurrence relation.
  • An initialized GatedRecurrentLayer[] that operates on vectors of size k contains the following trainable arrays:
  • "InputGateInputWeights"Wixmatrix of size n×k
    "InputGateStateWeights"Wismatrix of size n×n
    "InputGateBiases"bivector of size n
    "ResetGateInputWeights"Wrxmatrix of size n×k
    "ResetGateStateWeights"Wrsmatrix of size n×n
    "ResetGateBiases"brvector of size n
    "MemoryGateInputWeights"Wmxmatrix of size n×k
    "MemoryGateStateWeights"Wmsmatrix of size n×n
    "MemoryGateBiases"bmvector of size n
  • In GatedRecurrentLayer[n,opts], initial values can be given to the trainable arrays using a rule of the form "array"->value.
  • GatedRecurrentLayer[n,"Dropout"->spec] indicates that dropout regularization should be applied during training, in which units are probabilistically set to zero.
  • Specifying "Dropout"->None disables dropout during training.
  • Specifying "Dropout"->p applies an automatically chosen dropout method with dropout probability p.
  • Specifying "Dropout"->{"method1"->p1,"method2"->p2,} can be used to combine specific methods of dropout with the corresponding dropout probabilities. Possible methods include:
  • "VariationalInput"dropout applied to the gate contributions from the input, using the same pattern of units at each sequence step
    "VariationalState"dropout applied to the gate contributions from the previous state, using the same pattern of units at each sequence step
    "StateUpdate"dropout applied to the state update vector prior to it being added to the previous state, using a different pattern of units at each sequence step
  • The dropout methods "VariationalInput" and "VariationalState" are based on the Gal et al. 2016 method, while "StateUpdate" is based on the Semeniuta et al. 2016 method.
  • GatedRecurrentLayer[n,"Input"->shape] allows the shape of the input to be specified. Possible forms for shape are:
  • NetEncoder[]encoder producing a sequence of vectors
    {len,k}sequence of len length-k vectors
    {len,Automatic}sequence of len vectors whose length is inferred
    {"Varying",k}varying number of vectors each of length k
    {"Varying",Automatic}varying number of vectors each of inferred length

Examples

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Basic Examples  (2)

Create a GatedRecurrentLayer that produces a sequence of length-3 vectors:

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Create a randomly initialized GatedRecurrentLayer that takes a sequence of length-2 vectors and produces a sequence of length-3 vectors:

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Apply the layer to an input sequence:

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Scope  (4)

Options  (2)

Applications  (2)

See Also

BasicRecurrentLayer  LongShortTermMemoryLayer  NetMapOperator  SequenceLastLayer  LinearLayer  NetChain  NetGraph  NetExtract

Introduced in 2017
(11.1)