def testScalarCongruency(self):
bijector_test_util.assert_scalar_congruency(tfb.Gumbel(loc=0.3,
scale=20.),
lower_x=1.,
upper_x=100.,
eval_func=self.evaluate,
rtol=0.05)
def testVariableScale(self):
x = tf.Variable(1.)
b = tfb.Gumbel(loc=0., scale=x, validate_args=True)
self.evaluate(x.initializer)
self.assertIs(x, b.scale)
self.assertEqual((), self.evaluate(b.forward(-3.)).shape)
with self.assertRaisesOpError("Argument `scale` must be positive."):
with tf.control_dependencies([x.assign(-1.)]):
self.evaluate(b.forward(-3.))
def testBijectiveAndFinite(self):
bijector = tfb.Gumbel(loc=0., scale=3.0, validate_args=True)
x = np.linspace(-10., 10., num=10).astype(np.float32)
y = np.linspace(0.01, 0.99, num=10).astype(np.float32)
bijector_test_util.assert_bijective_and_finite(bijector,
x,
y,
eval_func=self.evaluate,
event_ndims=0,
rtol=1e-3)
def __init__(self,
loc,
scale,
validate_args=False,
allow_nan_stats=True,
name="Gumbel"):
"""Construct Gumbel distributions with location and scale `loc` and `scale`.
The parameters `loc` and `scale` must be shaped in a way that supports
broadcasting (e.g. `loc + scale` is a valid operation).
Args:
loc: Floating point tensor, the means of the distribution(s).
scale: Floating point tensor, the scales of the distribution(s).
scale must contain only positive values.
validate_args: Python `bool`, default `False`. When `True` distribution
parameters are checked for validity despite possibly degrading runtime
performance. When `False` invalid inputs may silently render incorrect
outputs.
Default value: `False`.
allow_nan_stats: Python `bool`, default `True`. When `True`,
statistics (e.g., mean, mode, variance) use the value "`NaN`" to
indicate the result is undefined. When `False`, an exception is raised
if one or more of the statistic's batch members are undefined.
Default value: `True`.
name: Python `str` name prefixed to Ops created by this class.
Default value: `'Gumbel'`.
Raises:
TypeError: if loc and scale are different dtypes.
"""
with tf.name_scope(name, values=[loc, scale]) as name:
dtype = dtype_util.common_dtype([loc, scale],
preferred_dtype=tf.float32)
loc = tf.convert_to_tensor(loc, name="loc", dtype=dtype)
scale = tf.convert_to_tensor(scale, name="scale", dtype=dtype)
with tf.control_dependencies(
[tf.assert_positive(scale)] if validate_args else []):
loc = tf.identity(loc, name="loc")
scale = tf.identity(scale, name="scale")
tf.assert_same_float_dtype([loc, scale])
self._gumbel_bijector = bijectors.Gumbel(
loc=loc, scale=scale, validate_args=validate_args)
super(Gumbel, self).__init__(
distribution=uniform.Uniform(low=tf.zeros([], dtype=loc.dtype),
high=tf.ones([], dtype=loc.dtype),
allow_nan_stats=allow_nan_stats),
# The Gumbel bijector encodes the quantile
# function as the forward, and hence needs to
# be inverted.
bijector=bijectors.Invert(self._gumbel_bijector),
batch_shape=distribution_util.get_broadcast_shape(loc, scale),
name=name)
def testBijector(self):
loc = 0.3
scale = 5.
bijector = tfb.Gumbel(loc=loc, scale=scale, validate_args=True)
self.assertStartsWith(bijector.name, "gumbel")
x = np.array([[[-3.], [0.], [0.5], [4.2], [12.]]], dtype=np.float32)
# Gumbel distribution
gumbel_dist = stats.gumbel_r(loc=loc, scale=scale)
y = gumbel_dist.cdf(x).astype(np.float32)
self.assertAllClose(y, self.evaluate(bijector.forward(x)))
self.assertAllClose(x, self.evaluate(bijector.inverse(y)))
self.assertAllClose(
np.squeeze(gumbel_dist.logpdf(x), axis=-1),
self.evaluate(bijector.forward_log_det_jacobian(x, event_ndims=1)))
self.assertAllClose(
self.evaluate(-bijector.inverse_log_det_jacobian(y, event_ndims=1)),
self.evaluate(bijector.forward_log_det_jacobian(x, event_ndims=1)),
rtol=1e-4,
atol=0.)
def testScalarCongruency(self):
with self.test_session():
assert_scalar_congruency(tfb.Gumbel(loc=0.3, scale=20.),
lower_x=1.,
upper_x=100.,
rtol=0.02)
