def create_layout():
p = gpflow.Parameterized(name='p')
p.a = gpflow.Param(10.)
p.b = gpflow.Param(11.)
p.c = gpflow.Parameterized()
p.c.d = gpflow.Param(12., fix_shape=False)
p.c.e = gpflow.DataHolder(13.)
return p
def test_is_built(self):
with self.test_context():
p = gpflow.Parameterized()
self.assertTrue(p.is_built_coherence())
# TODO(@awav): Should it be NO?
self.assertEqual(p.is_built_coherence(tf.Graph()),
gpflow.Build.YES)
values = [None, "", 1.0, object()]
for v in values:
with self.assertRaises(ValueError,
msg='Passed value {}'.format(v)):
p.is_built(v)
p.a = gpflow.Param(1.0)
self.assertEqual(p.is_built_coherence(), gpflow.Build.NO)
p.compile()
not_compatible = gpflow.Build.NOT_COMPATIBLE_GRAPH
self.assertTrue(p.is_built_coherence())
self.assertEqual(p.is_built(tf.Graph()), not_compatible)
with self.assertRaises(GPflowError):
p.is_built_coherence(tf.Graph())
for v in values:
with self.assertRaises(ValueError,
msg='Passed value "{}"'.format(v)):
p.is_built(v)
def test_str(self):
with self.test_context():
def check_str(obj, expect_str):
expect = [
e for e in expect_str.format(name=p.name).split(' ')
if e != ''
]
got = [e for e in str(obj).split(' ') if e != '']
print(expect)
print(got)
self.assertEqual(expect, got)
p_str = (' class prior transform trainable shape '
'fixed_shape value\n{name} Parameter None (none)'
' True () True 1.0')
p = gpflow.Param(1., name="short")
check_str(p, p_str)
d_str = (' class shape fixed_shape value'
'\n{name} DataHolder () False 1.0')
d = gpflow.DataHolder(1., name="short")
check_str(d, d_str)
params_str = (
' class prior transform trainable shape'
' fixed_shape value\n{name}/p Parameter None'
' (none) True () True 1.0')
params = gpflow.Parameterized(name="short")
params.p = p
params.d = d
check_str(params, params_str)
def deep_structure():
a = gp.Param(1)
b = gp.Param(2)
c_a = gp.Param(3)
c_b = gp.Param(4)
with gp.defer_build():
p = gp.Parameterized()
p.c = gp.Parameterized()
p.c.c = gp.Parameterized()
p.c.c.a = gp.Param(3)
p.c.c.b = gp.Param(4)
p.a = a
p.b = b
p.c.a = c_a
p.c.b = c_b
p.compile()
return p
def test_nested_data_update(self):
with self.test_context():
p = gpflow.Parameterized()
p.m = self.get_model()
p.m.X = np.random.rand(11, 2)
p.m.Y = np.random.rand(11, 1)
p.clear()
p.compile()
self.assertTupleEqual(p.m.q_mu.shape, (11, 1))
self.assertTupleEqual(p.m.q_sqrt.shape, (1, 11, 11))
def test_anchor(self):
with self.test_context() as session:
p = gpflow.Parameterized()
p.a = gpflow.Param(1.0)
p.compile()
with self.assertRaises(ValueError):
p.anchor(None)
new_value = 2.0
p.a.parameter_tensor.load(new_value)
p.anchor(session)
assert_allclose(p.a.read_value(), new_value)
def test_change_variable_size(self):
with self.test_context() as session:
m = gpflow.Parameterized()
length = 10
arr = np.random.randn(length, 2)
m.X = gpflow.Minibatch(arr, shuffle=False)
for i in range(length):
assert_allclose(m.X.read_value(session=session), [arr[i]])
length = 20
arr = np.random.randn(length, 2)
m.X = arr
for i in range(length):
assert_allclose(m.X.read_value(session=session), [arr[i]])
def test_anchor(self):
with self.test_context() as session:
p = gpflow.Parameterized()
p.a = gpflow.Param(1.0)
p.compile()
with self.assertRaises(ValueError):
p.anchor(None)
new_value = 2.0
p.a.parameter_tensor.load(new_value)
ops_count_before = len(session.graph.get_operations())
p.anchor(session)
ops_count_after = len(session.graph.get_operations())
assert ops_count_before == ops_count_after, "regression test for #1001"
assert_allclose(p.a.read_value(), new_value)
def test_str(self):
with self.test_context():
p_str = (' class prior transform trainable shape '
'fixed_shape value\nParameter Parameter None (none)'
' True () True 1.0')
p = gpflow.Param(1.)
self.assertEqual(p_str.format('Parameter'), str(p))
d_str = (' class shape fixed_shape value'
'\nDataHolder DataHolder () False 1.0')
d = gpflow.DataHolder(1.)
self.assertEqual(d_str, str(d))
params_str = (' class prior transform trainable shape'
' fixed_shape value\nParameterized/p Parameter None'
' (none) True () True 1.0')
params = gpflow.Parameterized()
params.p = p
params.d = d
self.assertEqual(params_str, str(params))
def test_feeds_empty(self):
with self.test_context():
p = gpflow.Parameterized()
self.assertEqual(p.initializables, [])
self.assertEqual(p.initializable_feeds, {})
self.assertEqual(p.feeds, {})
def params_tree(session_tf):
p = gpflow.Parameterized()
p.a = gpflow.Param(1.)
return p
