https://github.com/GPflow/GPflow
Tip revision: f56e5a2e9c20b744a699e975917fd11f33d84c90 authored by ST John on 13 April 2021, 15:46:17 UTC
make format
make format
Tip revision: f56e5a2
test_base_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 numpy as np
import pytest
import tensorflow as tf
import gpflow
from gpflow.utilities import leaf_components, multiple_assign, read_values, set_trainable
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 = 10.0
# ------------------------------------------
# Fixtures
# ------------------------------------------
@pytest.fixture
def model():
return gpflow.models.SVGP(
kernel=gpflow.kernels.SquaredExponential(lengthscales=Data.ls, variance=Data.var),
likelihood=gpflow.likelihoods.Gaussian(),
inducing_variable=Data.Z,
q_diag=True,
)
# ------------------------------------------
# Reference
# ------------------------------------------
model_param_updates = {
".kernel.lengthscales": 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.lengthscales": 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.numpy(), var_update_dict[path], decimal=7)
else:
np.testing.assert_equal(variable.numpy(), old_value_dict[path].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_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