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

View source: R/layers-embedding.R

layer_embeddingR Documentation

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


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


  embeddings_initializer = "uniform",
  embeddings_regularizer = NULL,
  activity_regularizer = NULL,
  embeddings_constraint = NULL,
  mask_zero = FALSE,
  input_length = NULL,
  sparse = FALSE,



Layer or Model object


Integer. Size of the vocabulary, i.e. maximum integer index + 1.


Integer. Dimension of the dense embedding.


Initializer for the embeddings matrix (see keras.initializers).

embeddings_regularizer, activity_regularizer

Regularizer function applied to the embeddings matrix or to the activations (see keras.regularizers).


Constraint function applied to the embeddings matrix (see keras.constraints).


Boolean, 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 input. 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).


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).


If TRUE, calling this layer returns a tf.SparseTensor. If FALSE, the layer returns a dense tf.Tensor. For an entry with no features in a sparse tensor (entry with value 0), the embedding vector of index 0 is returned by default.


standard layer arguments.


For example, list(4L, 20L) -> list(c(0.25, 0.1), c(0.6, -0.2)).

This layer can only be used on positive integer inputs of a fixed range. The layer_text_vectorization(), layer_string_lookup(), and layer_integer_lookup() preprocessing layers can help prepare inputs for an Embedding layer.

This layer accepts tf.Tensor, tf.RaggedTensor and tf.SparseTensor input.

Input shape

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

Output shape

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

See Also

keras documentation built on Aug. 16, 2023, 1:07 a.m.