def input_from_feature_columns(columns_to_tensors,
feature_columns,
weight_collections,
scope,
trainable=True,
output_rank=2,
default_name='input_from_feature_columns'):
columns_to_tensors = columns_to_tensors.copy()
feature_column_ops.check_feature_columns(feature_columns)
with variable_scope.variable_scope(scope,
default_name=default_name,
values=columns_to_tensors.values()):
output_tensors_dict = {}
transformer = feature_column_ops._Transformer(columns_to_tensors)
if weight_collections:
weight_collections = list(set(list(weight_collections) +
[ops.GraphKeys.GLOBAL_VARIABLES]))
for column in sorted(set(feature_columns), key=lambda x: x.key):
with variable_scope.variable_scope(None,
default_name=column.name,
values=columns_to_tensors.values()):
transformed_tensor = transformer.transform(column)
key = column.key
try:
# pylint: disable=protected-access
arguments = column._deep_embedding_lookup_arguments(
transformed_tensor)
output_tensors_dict[key] = \
fc._embeddings_from_arguments( # pylint: disable=protected-access
column,
arguments,
weight_collections,
trainable,
output_rank=output_rank)
except NotImplementedError as ee:
try:
# pylint: disable=protected-access
output_tensors_dict[key] = \
column._to_dnn_input_layer(
transformed_tensor,
weight_collections,
trainable,
output_rank=output_rank)
except ValueError as e:
raise ValueError('Error creating input layer for column: {}.\n'
'{}, {}'.format(column.name, e, ee))
return output_tensors_dict
