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.