layer_embedding {keras}

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.

Input shape

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

Output shape

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

See Also

• https://www.tensorflow.org/api_docs/python/tf/keras/layers/Embedding

• https://keras.io/api/layers