def var_creator(*args, **kwargs):
# Record what collections this variable should be added to.
collections = kwargs.pop("collections", None)
if collections is None:
collections = [ops.GraphKeys.GLOBAL_VARIABLES]
kwargs["collections"] = []
# Create and wrap the variable.
v = next_creator(*args, **kwargs)
wrapped = values.AggregatingVariable(v, aggregation)
# Add the wrapped variable to the requested collections.
# The handling of eager mode and the global step matches
# ResourceVariable._init_from_args().
if not context.executing_eagerly():
g = ops.get_default_graph()
# If "trainable" is True, next_creator() will add the contained
# variable to the TRAINABLE_VARIABLES collection, so we manually
# remove it and replace with the wrapper. We can't set "trainable"
# to False for next_creator() since that causes functions like
# implicit_gradients to skip those variables.
if kwargs.get("trainable", True):
collections.append(ops.GraphKeys.TRAINABLE_VARIABLES)
l = g.get_collection_ref(
ops.GraphKeys.TRAINABLE_VARIABLES)
l.remove(v)
g.add_to_collections(collections, wrapped)
elif ops.GraphKeys.GLOBAL_STEP in collections:
ops.add_to_collections(ops.GraphKeys.GLOBAL_STEP, wrapped)
return wrapped
def var_creator(*args, **kwargs):
v = next_creator(*args, **kwargs)
return values.AggregatingVariable(v, aggregation)