tf.contrib.distributions.LaplaceWithSoftplusScale.is_reparameterized

tf.contrib.distributions.LaplaceWithSoftplusScale.is_reparameterized

2016-10-14 12:56:04
tf.contrib.distributions.Laplace.param_shapes()

tf.contrib.distributions.Laplace.param_shapes(cls, sample_shape, name='DistributionParamShapes') Shapes of parameters given the

2016-10-14 12:55:42
tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.mode()

tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.mode(name='mode') Mode.

2016-10-14 12:58:39
tf.contrib.distributions.BetaWithSoftplusAB.log_pmf()

tf.contrib.distributions.BetaWithSoftplusAB.log_pmf(value, name='log_pmf') Log probability mass function.

2016-10-14 12:46:57
tf.contrib.distributions.Categorical.event_shape()

tf.contrib.distributions.Categorical.event_shape(name='event_shape') Shape of a single sample from a single batch as a 1-D int32

2016-10-14 12:48:11
tf.contrib.distributions.Beta.validate_args

tf.contrib.distributions.Beta.validate_args Python boolean indicated possibly expensive checks are enabled.

2016-10-14 12:46:45
tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.std()

tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.std(name='std') Standard deviation.

2016-10-14 12:55:12
tf.contrib.distributions.BetaWithSoftplusAB.is_reparameterized

tf.contrib.distributions.BetaWithSoftplusAB.is_reparameterized

2016-10-14 12:46:55
tf.contrib.distributions.MultivariateNormalCholesky.

tf.contrib.distributions.MultivariateNormalCholesky.__init__(mu, chol, validate_args=False, allow_nan_stats=True, name='MultivariateNormalCholesky')

2016-10-14 12:57:50
tf.contrib.distributions.Multinomial.sample()

tf.contrib.distributions.Multinomial.sample(sample_shape=(), seed=None, name='sample') Generate samples of the specified shape

2016-10-14 12:57:09