tf.contrib.distributions.MultivariateNormalCholesky.event_shape()

tf.contrib.distributions.MultivariateNormalCholesky.event_shape(name='event_shape') Shape of a single sample from a single batch

2016-10-14 12:57:20
tf.contrib.distributions.Mixture.sample_n()

tf.contrib.distributions.Mixture.sample_n(n, seed=None, name='sample_n') Generate n samples.

2016-10-14 12:56:38
tf.contrib.distributions.Gamma.cdf()

tf.contrib.distributions.Gamma.cdf(value, name='cdf') Cumulative distribution function. Given

2016-10-14 12:53:07
tf.contrib.distributions.Binomial.std()

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

2016-10-14 12:47:56
tf.contrib.distributions.WishartFull.is_reparameterized

tf.contrib.distributions.WishartFull.is_reparameterized

2016-10-14 13:04:24
tf.contrib.distributions.Binomial.validate_args

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

2016-10-14 12:47:59
tf.contrib.distributions.Exponential.is_continuous

tf.contrib.distributions.Exponential.is_continuous

2016-10-14 12:52:03
tf.contrib.distributions.BetaWithSoftplusAB.batch_shape()

tf.contrib.distributions.BetaWithSoftplusAB.batch_shape(name='batch_shape') Shape of a single sample from a single event index

2016-10-14 12:46:50
tf.contrib.distributions.ExponentialWithSoftplusLam.log_cdf()

tf.contrib.distributions.ExponentialWithSoftplusLam.log_cdf(value, name='log_cdf') Log cumulative distribution function.

2016-10-14 12:52:44
tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.mean()

tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.mean(name='mean') Mean.

2016-10-14 12:58:39