Maps a sequence of symbols to a sequence of embeddings.
tf.contrib.layers.embed_sequence(
ids, vocab_size=None, embed_dim=None, unique=False, initializer=None,
regularizer=None, trainable=True, scope=None, reuse=None
)
Typical use case would be reusing embeddings between an encoder and decoder.
Args |
ids
|
[batch_size, doc_length] Tensor of type int32 or int64
with symbol ids.
|
vocab_size
|
Integer number of symbols in vocabulary.
|
embed_dim
|
Integer number of dimensions for embedding matrix.
|
unique
|
If True , will first compute the unique set of indices, and then
lookup each embedding once, repeating them in the output as needed.
|
initializer
|
An initializer for the embeddings, if None default for
current scope is used.
|
regularizer
|
Optional regularizer for the embeddings.
|
trainable
|
If True also add variables to the graph collection
GraphKeys.TRAINABLE_VARIABLES (see tf.Variable ).
|
scope
|
Optional string specifying the variable scope for the op, required
if reuse=True .
|
reuse
|
If True , variables inside the op will be reused.
|
Returns |
Tensor of [batch_size, doc_length, embed_dim] with embedded sequences.
|
Raises |
ValueError
|
if embed_dim or vocab_size are not specified when
reuse is None or False .
|