# Bidirectional wrapper for RNNs.

Bidirectional wrapper for RNNs.

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.

Other layer wrappers: time_distributed()