def test_context_stack(self):
def_collector = default_summary_collector()
self.assertIsNotNone(def_collector)
c = SummaryCollector()
self.assertIsNot(c, def_collector)
with scoped_set_config(settings, auto_histogram=False):
with c.as_default() as cc:
self.assertIs(cc, c)
self.assertIs(default_summary_collector(), c)
self.assertFalse(settings.auto_histogram)
with c.as_default(auto_histogram=True) as cc:
self.assertIs(cc, c)
self.assertIs(default_summary_collector(), c)
self.assertTrue(settings.auto_histogram)
with scoped_set_config(settings, auto_histogram=True):
with c.as_default() as cc:
self.assertIs(cc, c)
self.assertIs(default_summary_collector(), c)
self.assertTrue(settings.auto_histogram)
with c.as_default(auto_histogram=False) as cc:
self.assertIs(cc, c)
self.assertIs(default_summary_collector(), c)
self.assertFalse(settings.auto_histogram)
self.assertIs(default_summary_collector(), def_collector)
def test_check_numerics(self):
with scoped_set_config(settings, check_numerics=True):
uniform = Uniform(minval=-1e100, maxval=1e100)
with self.test_session():
with pytest.raises(Exception,
match=r'log_p : Tensor had Inf values'):
_ = uniform.log_prob(0.).eval()
def test_check_numerics(self):
with scoped_set_config(settings, check_numerics=True):
normal = Normal(mean=0., std=-1.)
with self.test_session():
with pytest.raises(
Exception,
match=r'log\(std\) : Tensor had NaN values'):
_ = normal.logstd.eval()
def test_multi_layer_flow(self):
class _MyFlow(MultiLayerFlow):
def __init__(self, log_det_list, **kwargs):
self._log_det_list = list(log_det_list)
super(_MyFlow, self).__init__(
len(self._log_det_list), x_value_ndims=0, y_value_ndims=0,
**kwargs
)
def explicitly_invertible(self):
return True
def _build(self, input=None):
pass
def _transform_layer(self, layer_id, x, compute_y, compute_log_det):
return x, self._log_det_list[layer_id]
def _inverse_transform_layer(self, layer_id, y, compute_x,
compute_log_det):
return y, self._log_det_list[layer_id]
np.random.seed(1234)
with self.test_session() as sess, \
scoped_set_config(settings, enable_assertions=False):
x = np.random.normal(size=[2, 3, 4]).astype(np.float32)
log_det = np.zeros_like(x, dtype=np.float32)
log_det_2 = np.random.normal(size=log_det.shape).astype(np.float32)
# ZeroLogDet is the only element, test transform
zero_log_det = ZeroLogDet(x.shape, tf.float32)
flow = _MyFlow([
ZeroLogDet(x.shape, tf.float32),
])
_, log_det_out = flow.transform(x)
self.assertIsNone(zero_log_det._self_tensor)
self.assertIsInstance(log_det_out, ZeroLogDet)
np.testing.assert_allclose(sess.run(log_det_out), log_det)
# test ZeroLogDet is first element, test transform
zero_log_det = ZeroLogDet(x.shape, tf.float32)
flow = _MyFlow([
ZeroLogDet(x.shape, tf.float32),
tf.constant(log_det_2),
])
_, log_det_out = flow.transform(x)
self.assertIsNone(zero_log_det._self_tensor)
np.testing.assert_allclose(sess.run(log_det_out), log_det_2)
# test ZeroLogDet is second element, test transform
zero_log_det = ZeroLogDet(x.shape, tf.float32)
flow = _MyFlow([
tf.constant(log_det_2),
ZeroLogDet(x.shape, tf.float32),
])
_, log_det_out = flow.transform(x)
self.assertIsNone(zero_log_det._self_tensor)
np.testing.assert_allclose(sess.run(log_det_out), log_det_2)