layer_embedding: Turns positive integers (indexes) into dense vectors of fixed...
In keras: R Interface to 'Keras'

layer_embedding

R Documentation

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

Description

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

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,
  sparse = FALSE,
  ...
)

Arguments

`object`	Layer or Model object
`input_dim`	Integer. Size of the vocabulary, i.e. maximum integer index + 1.
`output_dim`	Integer. Dimension of the dense embedding.
`embeddings_initializer`	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`).
`embeddings_constraint`	Constraint function applied to the `embeddings` matrix (see `keras.constraints`).
`mask_zero`	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).
`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).
`sparse`	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.

Details

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.