tf.contrib.layers.embed_sequence

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`.

tf.contrib.layers.embed_sequence

Args

Returns

Raises