tf.contrib.layers.bow_encoder

Maps a sequence of symbols to a vector per example by averaging embeddings.

tf.contrib.layers.bow_encoder(
    ids, vocab_size, embed_dim, sparse_lookup=True, initializer=None,
    regularizer=None, trainable=True, scope=None, reuse=None
)

Args
`ids`	`[batch_size, doc_length]` `Tensor` or `SparseTensor` 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.
`sparse_lookup`	`bool`, if `True`, converts ids to a `SparseTensor` and performs a sparse embedding lookup. This is usually faster, but not desirable if padding tokens should have an embedding. Empty rows are assigned a special embedding.
`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
Encoding `Tensor` `[batch_size, embed_dim]` produced by averaging embeddings.

Raises
`ValueError`	If `embed_dim` or `vocab_size` are not specified.

tf.contrib.layers.bow_encoder

Args

Returns

Raises