tf.contrib.distributions.NormalWithSoftplusSigma.log_prob(value, name='log_prob') Log probability density/mass function (depending

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

tf.contrib.bayesflow.stochastic_tensor.LaplaceTensor.mean(name='mean')

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

class tf.contrib.learn.monitors.CheckpointSaver Saves checkpoints every N steps.

tf.RandomShuffleQueue.__init__(capacity, min_after_dequeue, dtypes, shapes=None, names=None, seed=None, shared_name=None, name='random_shuffle_queue')

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

tf.contrib.bayesflow.stochastic_tensor.WishartCholeskyTensor.__init__(name=None, dist_value_type=None, loss_fn=score_function, **dist_args)

class tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev MultivariateNormalDiag with diag_stddev = softpl

tf.contrib.learn.monitors.StepCounter.every_n_step_end(current_step, outputs)