def _embeddings_from_arguments(column,
args,
weight_collections,
trainable,
output_rank=2):
"""Returns embeddings for a column based on the computed arguments.
Args:
column: the column name.
args: the _DeepEmbeddingLookupArguments for this column.
weight_collections: collections to store weights in.
trainable: whether these embeddings should be trainable.
output_rank: the desired rank of the returned `Tensor`. Inner dimensions will
be combined to produce the desired rank.
Returns:
the embeddings.
Raises:
ValueError: if not possible to create.
"""
# pylint: disable=protected-access
input_tensor = layers._inner_flatten(args.input_tensor, output_rank)
weight_tensor = None
if args.weight_tensor is not None:
weight_tensor = layers._inner_flatten(args.weight_tensor, output_rank)
# pylint: enable=protected-access
if args.hashed:
embeddings = contrib_variables.model_variable(
name='weights',
shape=[args.vocab_size],
dtype=dtypes.float32,
initializer=args.initializer,
trainable=trainable,
collections=weight_collections)
return embedding_ops.hashed_embedding_lookup_sparse(
embeddings, input_tensor, args.dimension,
combiner=args.combiner, name='lookup')
if args.shared_embedding_name is not None:
shared_embedding_collection_name = (
'SHARED_EMBEDDING_COLLECTION_' + args.shared_embedding_name.upper())
graph = ops.get_default_graph()
shared_embedding_collection = (
graph.get_collection_ref(shared_embedding_collection_name))
shape = [args.vocab_size, args.dimension]
if shared_embedding_collection:
if len(shared_embedding_collection) > 1:
raise ValueError('Collection %s can only contain one '
'(partitioned) variable.'
% shared_embedding_collection_name)
else:
embeddings = shared_embedding_collection[0]
if embeddings.get_shape() != shape:
raise ValueError('The embedding variable with name {} already '
'exists, but its shape does not match required '
'embedding shape here. Please make sure to use '
'different shared_embedding_name for different '
'shared embeddings.'.format(
args.shared_embedding_name))
else:
embeddings = contrib_variables.model_variable(
name=args.shared_embedding_name,
shape=shape,
dtype=dtypes.float32,
initializer=args.initializer,
trainable=trainable,
collections=weight_collections)
graph.add_to_collection(shared_embedding_collection_name, embeddings)
else:
embeddings = contrib_variables.model_variable(
name='weights',
shape=[args.vocab_size, args.dimension],
dtype=dtypes.float32,
initializer=args.initializer,
trainable=trainable,
collections=weight_collections)
if isinstance(embeddings, variables.Variable):
embeddings = [embeddings]
else:
embeddings = embeddings._get_variable_list() # pylint: disable=protected-access
# pylint: disable=protected-access
_maybe_restore_from_checkpoint(
column._checkpoint_path(), embeddings)
return embedding_ops.safe_embedding_lookup_sparse(
embeddings,
input_tensor,
sparse_weights=weight_tensor,
combiner=args.combiner,
name=column.name + 'weights',
max_norm=args.max_norm)
