tf.contrib.layers.embed_sequence(ids, vocab_size=None, embed_dim=None, unique=False, initializer=None, regularizer=None, trainable=True, scope=None, reuse=None)

tf.contrib.layers.embed_sequence(ids, vocab_size=None, embed_dim=None, unique=False, initializer=None, regularizer=None, trainable=True, scope=None, reuse=None)

See the guide: Layers (contrib) > Higher level ops for building neural network layers

Maps a sequence of symbols to a sequence of embeddings.

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 not reuse is None or False.

Defined in tensorflow/contrib/layers/python/layers/encoders.py.