keras: layer_embedding – R documentation

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layer_embedding

Turns positive integers (indexes) into dense vectors of fixed size.

Description

For example, list(4L, 20L) -> list(c(0.25, 0.1), c(0.6, -0.2)) This layer can only be used as the first layer in a model.

Usage

layer_embedding(
  object,
  input_dim,
  output_dim,
  embeddings_initializer = "uniform",
  embeddings_regularizer = NULL,
  activity_regularizer = NULL,
  embeddings_constraint = NULL,
  mask_zero = FALSE,
  input_length = NULL,
  batch_size = NULL,
  name = NULL,
  trainable = NULL,
  weights = NULL
)

Arguments

`object`	Model or layer object
`input_dim`	int > 0. Size of the vocabulary, i.e. maximum integer index + 1.
`output_dim`	int >= 0. Dimension of the dense embedding.
`embeddings_initializer`	Initializer for the `embeddings` matrix.
`embeddings_regularizer`	Regularizer function applied to the `embeddings` matrix.
`activity_regularizer`	activity_regularizer
`embeddings_constraint`	Constraint function applied to the `embeddings` matrix.
`mask_zero`	Whether or not the input value 0 is a special "padding" value that should be masked out. This is useful when using recurrent layers, which may take variable length inputs. If this is `TRUE` then all subsequent layers in the model need to support masking or an exception will be raised. If mask_zero is set to TRUE, as a consequence, index 0 cannot be used in the vocabulary (input_dim should equal size of vocabulary + 1).
`input_length`	Length of input sequences, when it is constant. This argument is required if you are going to connect `Flatten` then `Dense` layers upstream (without it, the shape of the dense outputs cannot be computed).
`batch_size`	Fixed batch size for layer
`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.

Input shape

2D tensor with shape: (batch_size, sequence_length).

Output shape

3D tensor with shape: (batch_size, sequence_length, output_dim).

References

A Theoretically Grounded Application of Dropout in Recurrent Neural Networks

keras

R Interface to 'Keras'

v2.4.0

MIT + file LICENSE

Authors

Daniel Falbel [ctb, cph, cre], JJ Allaire [aut, cph], François Chollet [aut, cph], RStudio [ctb, cph, fnd], Google [ctb, cph, fnd], Yuan Tang [ctb, cph] (<https://orcid.org/0000-0001-5243-233X>), Wouter Van Der Bijl [ctb, cph], Martin Studer [ctb, cph], Sigrid Keydana [ctb]

Initial release