https://github.com/GPflow/GPflow
Revision 0adad1379d94928cfd22d5a737ae354526cc6c93 authored by John Bradshaw on 03 October 2017, 15:58:18 UTC, committed by John Bradshaw on 03 October 2017, 15:58:18 UTC
* Random features for SVGP model * Changed interface for random features so that returns whether given back variance or precisions -- stops unnecessary converting stuff. * Fixed tests for GPR with this new interface.
1 parent c29ef9c
Tip revision: 0adad1379d94928cfd22d5a737ae354526cc6c93 authored by John Bradshaw on 03 October 2017, 15:58:18 UTC
SVGP random features
SVGP random features
Tip revision: 0adad13
scoping.py
# Copyright 2016 James Hensman
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import tensorflow as tf
from functools import wraps
class NameScoped(object):
"""
A decorator for functions, so that they can be executed within tensorflow
scopes. Usage:
>>> @NameScoped('foo_scope'):
>>> def foobar(x, y=3):
>>> return x + y
or
>>> def foobaz(x, y=4):
return x + y
>>> myfunc = NameScoped('my_fave_scope')(foobaz)
"""
def __init__(self, name):
self.name = name
def __call__(self, f):
@wraps(f)
def runnable(*args, **kwargs):
with tf.name_scope(self.name):
return f(*args, **kwargs)
return runnable
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