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.