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.

Examples

Create a GRU layer with 64 hidden units:

GatedRecurrentLayer[64]

Use in a neural network:

NetChain[{GatedRecurrentLayer[32], LinearLayer[10]}]

Please visit the official Wolfram Language Reference for more details.