bidirectional: Bidirectional wrapper for RNNs.

In dfalbel/keras: R Interface to 'Keras'

Description

Bidirectional wrapper for RNNs.

Usage

bidirectional(
  object,
  layer,
  merge_mode = "concat",
  input_shape = NULL,
  batch_input_shape = NULL,
  batch_size = NULL,
  dtype = NULL,
  name = NULL,
  trainable = NULL,
  weights = NULL
)

Arguments

object	Model or layer object
layer	Recurrent instance.
merge_mode	Mode by which outputs of the forward and backward RNNs will be combined. One of 'sum', 'mul', 'concat', 'ave', NULL. If NULL, the outputs will not be combined, they will be returned as a list.
input_shape	Dimensionality of the input (integer) not including the samples axis. This argument is required when using this layer as the first layer in a model.
batch_input_shape	Shapes, including the batch size. For instance, batch_input_shape=c(10, 32) indicates that the expected input will be batches of 10 32-dimensional vectors. batch_input_shape=list(NULL, 32) indicates batches of an arbitrary number of 32-dimensional vectors.
batch_size	Fixed batch size for layer
dtype	The data type expected by the input, as a string (float32, float64, int32...)
name	An optional name string for the layer. Should be unique in a model (do not reuse the same name twice). It will be autogenerated if it isn't provided.
trainable	Whether the layer weights will be updated during training.
weights	Initial weights for layer.

See Also

Other layer wrappers: time_distributed()

dfalbel/keras documentation built on Nov. 27, 2019, 8:16 p.m.