tf.contrib.distributions.Exponential.mean()

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

2016-10-14 12:52:15
tf.contrib.distributions.GammaWithSoftplusAlphaBeta.log_survival_function()

tf.contrib.distributions.GammaWithSoftplusAlphaBeta.log_survival_function(value, name='log_survival_function') Log survival function

2016-10-14 12:54:00
tf.contrib.distributions.GammaWithSoftplusAlphaBeta.survival_function()

tf.contrib.distributions.GammaWithSoftplusAlphaBeta.survival_function(value, name='survival_function') Survival function.

2016-10-14 12:54:07
tf.contrib.distributions.WishartFull.param_shapes()

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

2016-10-14 13:04:33
tf.contrib.distributions.MultivariateNormalFull.std()

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

2016-10-14 12:59:33
tf.contrib.distributions.Bernoulli.log_cdf()

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

2016-10-14 12:45:02
tf.contrib.distributions.Dirichlet.get_batch_shape()

tf.contrib.distributions.Dirichlet.get_batch_shape() Shape of a single sample from a single event index as a TensorShape

2016-10-14 12:50:02
tf.contrib.distributions.Poisson.sample()

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

2016-10-14 13:01:07
tf.contrib.distributions.ExponentialWithSoftplusLam.variance()

tf.contrib.distributions.ExponentialWithSoftplusLam.variance(name='variance') Variance.

2016-10-14 12:52:57
tf.contrib.distributions.StudentT.mode()

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

2016-10-14 13:02:08