def onehot_categorical(self,
name,
logits,
n_samples=None,
group_ndims=0,
dtype=tf.int32,
**kwargs):
"""
Add a stochastic node in this :class:`BayesianNet` that follows the
OnehotCategorical distribution.
:param name: The name of the stochastic node. Must be unique in a
:class:`BayesianNet`.
See
:class:`~zhusuan.distributions.multivariate.OnehotCategorical`
for more information about the other arguments.
:return: A :class:`StochasticTensor` instance.
"""
dist = distributions.OnehotCategorical(logits,
group_ndims=group_ndims,
dtype=dtype,
**kwargs)
return self.stochastic(name, dist, n_samples=n_samples, **kwargs)
def __init__(self,
name,
logits,
n_samples=None,
group_ndims=0,
dtype=None,
**kwargs):
onehot_cat = distributions.OnehotCategorical(logits,
group_ndims=group_ndims,
dtype=dtype,
**kwargs)
super(OnehotCategorical, self).__init__(name, onehot_cat, n_samples)
def __init__(self, logits, dtype=None):
"""
Construct the :class:`OnehotCategorical`.
Args:
logits: An N-D (N >= 1) `float` Tensor of shape
``(..., n_categories)``. Each slice `[i, j,..., k, :]`
represents the un-normalized log probabilities for all
categories. :math:`\\mathrm{logits} \\propto \\log p`
dtype: The value type of samples from the distribution.
(default ``tf.int32``)
"""
if dtype is None:
dtype = tf.int32
super(OnehotCategorical,
self).__init__(zd.OnehotCategorical(logits=logits, dtype=dtype))
def test_proxied_props_and_methods(self):
zs_distrib = zd.OnehotCategorical(tf.zeros([3, 4, 5]),
dtype=tf.int64,
group_ndims=1)
distrib = ZhuSuanDistribution(zs_distrib)
for attr in ['dtype', 'is_continuous', 'is_reparameterized']:
self.assertEqual(getattr(zs_distrib, attr),
getattr(distrib, attr),
msg='Attribute `{}` does not equal'.format(attr))
for meth in ['get_value_shape', 'get_batch_shape']:
self.assertEqual(
getattr(zs_distrib, meth)(),
getattr(distrib, meth)(),
msg='Output of method `{}` does not equal'.format(meth))
with self.test_session():
for attr in ['value_shape', 'batch_shape']:
np.testing.assert_equal(
getattr(zs_distrib, attr).eval(),
getattr(distrib, attr).eval(),
err_msg='Value of attribute `{}` does not equal'.format(
attr))
