https://github.com/GPflow/GPflow
Tip revision: 6fd1a26809a4c754b73c1a645b48d7cda35b2cd6 authored by John Bradshaw on 24 October 2017, 10:29:09 UTC
Merge remote-tracking branch 'origin/GPflow-1.0-RC' into john-bradshaw/linear-features-for-kernels-gpflow1.0
Merge remote-tracking branch 'origin/GPflow-1.0-RC' into john-bradshaw/linear-features-for-kernels-gpflow1.0
Tip revision: 6fd1a26
scipy_optimizer.py
# Copyright 2017 Artem Artemev @awav
#
# 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.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from gpflow.core.base import GPflowError
from gpflow.core.base import Build
from gpflow.models.model import Model
from . import optimizer
from . import external_optimizer
class ScipyOptimizer(optimizer.Optimizer):
def __init__(self, **kwargs):
self._optimizer_kwargs = kwargs
self._optimizer = None
self._model = None
def minimize(self, model, **kwargs):
if model is None or not isinstance(model, Model):
raise ValueError('Unknown type passed for optimization.')
if model.is_built_coherence() is Build.NO:
raise GPflowError('Model is not built.')
session = self._pop_session(model, kwargs)
self._model = model
var_list = self._pop_var_list(model, kwargs)
with model.graph.as_default():
objective = model.objective
self._optimizer = external_optimizer.ScipyOptimizerInterface(
objective, var_list=var_list, **self._optimizer_kwargs)
feed_dict = self._pop_feed_dict(kwargs)
if model.feeds:
feed_dict.update(model.feeds)
self._optimizer.minimize(session=session, feed_dict=feed_dict, **kwargs)
@property
def model(self):
return self._model
@property
def optimizer(self):
return self._optimizer