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

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.

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