class tf.contrib.distributions.Exponential The Exponential distribution with rate parameter lam. The

tf.contrib.learn.monitors.LoggingTrainable.epoch_end(epoch) End epoch. Args:

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

tf.is_strictly_increasing(x, name=None) Returns True if x is strictly increasing.

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

tf.contrib.learn.monitors.get_default_monitors(loss_op=None, summary_op=None, save_summary_steps=100, output_dir=None, summary_writer=None)

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

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

tf.contrib.distributions.Mixture.get_event_shape() Shape of a single sample from a single batch as a TensorShape

tf.QueueBase.enqueue(vals, name=None) Enqueues one element to this queue. If the queue is full