def testShapeGetterRaisesException(self):
x = tf.Variable(-1.)
self.evaluate(x.initializer)
with self.assertRaisesOpError(
'Argument `concentration1` must be positive.'):
b = tfb.KumaraswamyCDF(concentration0=1.,
concentration1=x,
validate_args=True)
self.evaluate(b.forward_event_shape_tensor([1, 2, 3]))
with self.assertRaisesOpError(
'Argument `concentration0` must be positive.'):
b = tfb.KumaraswamyCDF(concentration0=x,
concentration1=1.,
validate_args=True)
self.evaluate(b.forward_event_shape_tensor(tf.constant([1, 2, 3])))
def testScalarCongruency(self):
bijector_test_util.assert_scalar_congruency(tfb.KumaraswamyCDF(
concentration1=0.5, concentration0=1.1),
lower_x=0.,
upper_x=1.,
eval_func=self.evaluate,
n=int(10e3),
rtol=0.05)
def testBijectorConcentration1LogDetJacobianFiniteAtZero(self):
# When concentration = 1., forward_log_det_jacobian should be finite at
# zero.
concentration0 = np.logspace(0.1, 10., num=20).astype(np.float32)
bijector = tfb.KumaraswamyCDF(
concentration1=1., concentration0=concentration0)
ildj = self.evaluate(
bijector.inverse_log_det_jacobian(0., event_ndims=0))
self.assertAllEqual(np.ones_like(ildj, dtype=np.bool_), np.isfinite(ildj))
def testGradient(self):
x = tf.Variable(1.)
y = tf.Variable(2.)
b = tfb.KumaraswamyCDF(
concentration0=x, concentration1=y, validate_args=True)
with tf.GradientTape() as tape:
loss = b.forward(1.)
g = tape.gradient(loss, b.trainable_variables)
self.assertLen(g, 2)
self.assertAllNotNone(g)
def testVariableConcentration1(self):
x = tf.Variable(1.)
b = tfb.KumaraswamyCDF(
concentration0=1., concentration1=x, validate_args=True)
self.evaluate(x.initializer)
self.assertIs(x, b.concentration1)
self.assertEqual((), self.evaluate(b.forward(1.)).shape)
with self.assertRaisesOpError(
'Argument `concentration1` must be positive.'):
with tf.control_dependencies([x.assign(-1.)]):
self.evaluate(b.forward(1.))
def testBijectiveAndFinite(self):
concentration1 = 1.2
concentration0 = 2.
bijector = tfb.KumaraswamyCDF(
concentration1=concentration1,
concentration0=concentration0,
validate_args=True)
# Omitting the endpoints 0 and 1, since idlj will be infinity at these
# endpoints.
x = np.linspace(.01, 0.99, num=10).astype(np.float32)
y = 1 - (1 - x ** concentration1) ** concentration0
bijector_test_util.assert_bijective_and_finite(
bijector, x, y, eval_func=self.evaluate, event_ndims=0,
rtol=1e-3)
def testBijector(self):
a = 2.
b = 0.3
bijector = tfb.KumaraswamyCDF(
concentration1=a, concentration0=b, validate_args=True)
self.assertStartsWith(bijector.name, 'kumaraswamy')
x = np.array([[[0.1], [0.2], [0.3], [0.4], [0.5]]], dtype=np.float32)
# Kumaraswamy cdf. This is the same as forward(x).
y = 1. - (1. - x ** a) ** b
self.assertAllClose(y, self.evaluate(bijector.forward(x)))
self.assertAllClose(x, self.evaluate(bijector.inverse(y)))
kumaraswamy_log_pdf = (np.log(a) + np.log(b) + (a - 1) * np.log(x) +
(b - 1) * np.log1p(-x ** a))
self.assertAllClose(
np.squeeze(kumaraswamy_log_pdf, axis=-1),
self.evaluate(bijector.forward_log_det_jacobian(x, event_ndims=1)))
self.assertAllClose(
self.evaluate(-bijector.forward_log_det_jacobian(x, event_ndims=1)),
self.evaluate(bijector.inverse_log_det_jacobian(y, event_ndims=1)),
rtol=1e-4,
atol=0.)
