https://github.com/GPflow/GPflow
Revision 378d9e8a8ab4bd7e131cdf14b1df07504dcfa2b0 authored by Artem Artemev on 10 July 2019, 10:10:40 UTC, committed by Vincent Dutordoir on 10 July 2019, 10:10:40 UTC
Update docker-image to TF 1.14
1 parent 2970c07
Tip revision: 378d9e8a8ab4bd7e131cdf14b1df07504dcfa2b0 authored by Artem Artemev on 10 July 2019, 10:10:40 UTC
Upate Dockerfile version in CircleCI (#999)
Upate Dockerfile version in CircleCI (#999)
Tip revision: 378d9e8
test_autoflow.py
# Copyright 2017 the GPflow authors.
#
# 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
import numpy as np
from numpy.testing import assert_almost_equal, assert_allclose
import gpflow
from gpflow.test_util import GPflowTestCase
from gpflow.core import AutoFlow
class DumbModel(gpflow.models.Model):
def __init__(self):
gpflow.models.Model.__init__(self)
self.a = gpflow.Param(3.)
@gpflow.params_as_tensors
def _build_likelihood(self):
return -tf.square(self.a)
class NoArgsModel(DumbModel):
@gpflow.autoflow()
@gpflow.params_as_tensors
def function1(self):
return self.a
@gpflow.autoflow()
@gpflow.params_as_tensors
def function2(self):
return self.a + 1.0
class TestNoArgs(GPflowTestCase):
def test_autoflow_functioning(self):
with self.test_context():
m = NoArgsModel()
m.compile()
def get_keys():
return [k for k in m.__dict__ if k.startswith(AutoFlow.__autoflow_prefix__)]
names = [m.function1.__name__, m.function2.__name__]
names = [AutoFlow.__autoflow_prefix__ + name for name in names]
first_key = names[0]
second_key = names[1]
assert_allclose(m.function1(), 3.)
assert_allclose(m.function2(), 4.)
self.assertEqual(len(get_keys()), 2)
AutoFlow.clear_autoflow(m, name=first_key)
self.assertEqual(len(get_keys()), 1)
assert_allclose(m.function1(), 3.)
self.assertEqual(len(get_keys()), 2)
AutoFlow.clear_autoflow(m, name=second_key)
self.assertEqual(len(get_keys()), 1)
assert_allclose(m.function2(), 4.)
self.assertEqual(len(get_keys()), 2)
AutoFlow.clear_autoflow(m, name=first_key)
AutoFlow.clear_autoflow(m, name=second_key)
self.assertEqual(len(get_keys()), 0)
assert_allclose(m.function1(), 3.)
assert_allclose(m.function2(), 4.)
self.assertEqual(len(get_keys()), 2)
AutoFlow.clear_autoflow(m)
self.assertEqual(len(get_keys()), 0)
class AddModel(DumbModel):
@gpflow.autoflow((tf.float64,), (tf.float64,))
@gpflow.params_as_tensors
def add(self, x, y):
return tf.add(x, y)
class TestShareArgs(GPflowTestCase):
"""
This is designed to replicate bug #85, where having two models caused
autoflow functions to fail because the tf_args were shared over the
instances.
"""
def setUp(self):
with self.test_context():
self.m1 = AddModel()
self.m2 = AddModel()
def test_share_args(self):
with self.test_context():
rng = np.random.RandomState(0)
x = rng.randn(10, 20)
y = rng.randn(10, 20)
ans = x + y
self.m1.add(x, y)
self.m2.add(x, y)
assert_almost_equal(self.m1.add(x, y), ans)
assert_almost_equal(self.m2.add(x, y), ans)
assert_almost_equal(self.m1.add(y, x), ans)
class IncrementModel(DumbModel):
def __init__(self):
DumbModel.__init__(self)
self.b = gpflow.DataHolder(np.array([3.]))
@gpflow.autoflow((tf.float64,))
@gpflow.params_as_tensors
def inc(self, x):
return x + self.b
class TestDataHolder(GPflowTestCase):
def test_add(self):
with self.test_context():
m = IncrementModel()
x = np.random.randn(10, 20)
m.compile()
assert_almost_equal(x + m.a.read_value(), m.inc(x))
class TestGPmodel(GPflowTestCase):
def prepare(self):
rng = np.random.RandomState(0)
X, Y = rng.randn(2, 10, 1)
m = gpflow.models.SVGP(X, Y,
kern=gpflow.kernels.Matern32(1),
likelihood=gpflow.likelihoods.StudentT(),
Z=X[::2].copy())
m.compile()
xnew = np.random.randn(100, 1)
ynew = np.random.randn(100, 1)
return m, xnew, ynew
def test_predict_f(self):
with self.test_context():
m, x, _y = self.prepare()
_mu, _var = m.predict_f(x)
def test_predict_y(self):
with self.test_context():
m, x, _y = self.prepare()
_mu, _var = m.predict_y(x)
def test_predict_density(self):
with self.test_context():
m, x, y = self.prepare()
m.predict_density(x, y)
def test_multiple_AFs(self):
with self.test_context():
m, _x, _y = self.prepare()
m.compute_log_likelihood()
m.compute_log_prior()
m.compute_log_likelihood()
class TestFixAndPredict(GPflowTestCase):
"""
Bug #54 says that if a model parameter is fixed between calls to predict
(an autoflow fn) then the second call fails. This test ensures replicates
that and ensures that the bugfix remains in furure.
"""
def prepare(self):
rng = np.random.RandomState(0)
X, Y = rng.randn(2, 10, 1)
m = gpflow.models.SVGP(X, Y, kern=gpflow.kernels.Matern32(1),
likelihood=gpflow.likelihoods.StudentT(),
Z=X[::2].copy())
xtest = np.random.randn(100, 1)
ytest = np.random.randn(100, 1)
return m, xtest, ytest
def test(self):
with self.test_context():
m, x, y = self.prepare()
m.compile()
m.kern.variance.trainable = False
_, _ = m.predict_f(m.X.read_value())
if __name__ == '__main__':
tf.test.main()
Computing file changes ...