def testReprWorksCorrectlyScalar(self):
normal = tfd.Normal(loc=np.float16(0), scale=np.float16(1))
self.assertEqual(
repr(normal), '<tfp.distributions.Normal'
' \'Normal\''
' batch_shape=[]'
' event_shape=[]'
' dtype=float16>')
chi2 = tfd.Chi2(df=np.float32([1., 2.]), name='silly')
self.assertEqual(
repr(chi2),
'<tfp.distributions.Chi2'
' \'silly\'' # What a silly name that is!
' batch_shape=[2]'
' event_shape=[]'
' dtype=float32>')
# There's no notion of partially known shapes in eager mode, so exit
# early.
if tf.executing_eagerly():
return
exp = tfd.Exponential(
rate=tf1.placeholder_with_default(1., shape=None))
self.assertEqual(
repr(exp), '<tfp.distributions.Exponential'
' \'Exponential\''
' batch_shape=?'
' event_shape=[]'
' dtype=float32>')
def testStrWorksCorrectlyScalar(self):
normal = tfd.Normal(loc=np.float16(0), scale=1, validate_args=True)
self.assertEqual(
str(normal), 'tfp.distributions.Normal('
'"Normal", '
'batch_shape=[], '
'event_shape=[], '
'dtype=float16)')
chi2 = tfd.Chi2(df=np.float32([1., 2.]),
name='silly',
validate_args=True)
self.assertEqual(
str(chi2),
'tfp.distributions.Chi2('
'"silly", ' # What a silly name that is!
'batch_shape=[2], '
'event_shape=[], '
'dtype=float32)')
# There's no notion of partially known shapes in eager mode, so exit
# early.
if tf.executing_eagerly():
return
exp = tfd.Exponential(rate=tf1.placeholder_with_default(1.,
shape=None),
validate_args=True)
self.assertEqual(
str(exp),
'tfp.distributions.Exponential("Exponential", '
# No batch shape.
'event_shape=[], '
'dtype=float32)')
def test_default_event_space_bijector(self):
dist = tfd.Chi2([1., 2., 3., 6.], validate_args=True)
batch_shape = [2, 2, 1]
reshape_dist = tfd.BatchReshape(dist, batch_shape, validate_args=True)
x = self.evaluate(dist._experimental_default_event_space_bijector()(
10. * tf.ones(dist.batch_shape)))
x_reshape = self.evaluate(
reshape_dist._experimental_default_event_space_bijector()(
10. * tf.ones(reshape_dist.batch_shape)))
self.assertAllEqual(tf.reshape(x, batch_shape), x_reshape)
