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).