Turns positive integers (indexes) into dense vectors of fixed size
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,
...
)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).
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.
2D tensor with shape: (batch_size, sequence_length).
3D tensor with shape: (batch_size, sequence_length, output_dim).
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.