swh:1:snp:00bd25be6ef59fb8210ef6a4c842086902e04451
Tip revision: 816a41f081fb5f8d1c33aaf7b7866fcf393b0898 authored by thevincentadam on 02 February 2020, 21:14:05 UTC
update models and parse
update models and parse
Tip revision: 816a41f
test_training.py
# Copyright 2018 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 gpflow
import numpy as np
import pytest
import tensorflow as tf
from gpflow.utilities import multiple_assign, set_trainable, leaf_components, read_values
rng = np.random.RandomState(0)
class Data:
H0 = 5
H1 = 2
M = 10
D = 1
Z = rng.rand(M, 1)
ls = 2.0
ls_new = 1.5
var = 1.0
var_new = 10.
# ------------------------------------------
# Fixtures
# ------------------------------------------
@pytest.fixture
def model():
return gpflow.models.SVGP(
kernel=gpflow.kernels.SquaredExponential(lengthscale=Data.ls, variance=Data.var),
likelihood=gpflow.likelihoods.Gaussian(),
inducing_variable=Data.Z,
q_diag=True
)
# ------------------------------------------
# Reference
# ------------------------------------------
model_param_updates = {
'.kernel.lengthscale': Data.ls_new,
'.likelihood.variance': Data.var_new,
'.inducing_variable.Z': np.zeros_like(Data.Z),
'.q_sqrt': 0.5 * np.ones((Data.M, 1))
}
model_wrong_path = [
{'kernel.lengthscale': Data.ls_new},
{'.Gaussian.variance': Data.var_new},
{'inducing_variable.Z': np.zeros_like(Data.Z)},
{'.q_std': 0.5 * np.ones((Data.M, 1))}
]
model_wrong_value = [
{'.likelihood.variance': np.ones((2, 1), dtype=np.int32)},
{'.inducing_variable.Z': [1, 2, 3]}
]
@pytest.mark.parametrize('var_update_dict', [model_param_updates])
def test_multiple_assign_updates_correct_values(model, var_update_dict):
old_value_dict = leaf_components(model).copy()
multiple_assign(model, var_update_dict)
for path, variable in leaf_components(model).items():
if path in var_update_dict.keys():
np.testing.assert_almost_equal(variable.value().numpy(), var_update_dict[path],
decimal=7)
else:
np.testing.assert_equal(variable.value().numpy(), old_value_dict[path].value().numpy())
@pytest.mark.parametrize('wrong_var_update_dict', model_wrong_path)
def test_multiple_assign_fails_with_invalid_path(model, wrong_var_update_dict):
with pytest.raises(KeyError):
multiple_assign(model, wrong_var_update_dict)
@pytest.mark.parametrize('wrong_var_update_dict', model_wrong_value)
def test_multiple_assign_fails_with_invalid_values(model, wrong_var_update_dict):
with pytest.raises(ValueError):
multiple_assign(model, wrong_var_update_dict)
def test_make_trainable(model):
"""
Checks whether we `set_trainable()` can make parameters which are *not*
trainable trainable again.
"""
set_trainable(model, False)
assert len(model.trainable_variables) == 0
set_trainable(model, True)
assert len(model.trainable_variables) == len(model.parameters)
def test_dict_utilities(model):
"""
Test both `parameter_dict()` and `read_values()`
"""
class SubModule(tf.Module):
def __init__(self):
self.parameter = gpflow.Parameter(1.0)
self.variable = tf.Variable(1.0)
class Module(tf.Module):
def __init__(self):
self.submodule = SubModule()
self.top_parameter = gpflow.Parameter(3.0)
m = Module()
params = gpflow.utilities.parameter_dict(m)
# {
# ".submodule.parameter": <parameter object>,
# ".submodule.variable": <variable object>
# }
assert list(params.keys()) == [".submodule.parameter", ".submodule.variable", ".top_parameter"]
assert list(params.values()) == [m.submodule.parameter, m.submodule.variable, m.top_parameter]
for k, v in read_values(m).items():
assert params[k].numpy() == v