tf.contrib.learn.monitors.LoggingTrainable.every_n_step_end(step, outputs)

tf.contrib.distributions.NormalWithSoftplusSigma.is_continuous

tf.as_string(input, precision=None, scientific=None, shortest=None, width=None, fill=None, name=None) Converts each entry in the

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

tf.contrib.bayesflow.stochastic_tensor.DirichletMultinomialTensor.value_type

tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.prob(value, name='prob') Probability density/mass function (depending

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

tf.segment_min(data, segment_ids, name=None) Computes the minimum along segments of a tensor. Read

tf.contrib.losses.cosine_distance(predictions, targets, dim, weight=1.0, scope=None) Adds a cosine-distance loss to the training

tf.is_non_decreasing(x, name=None) Returns True if x is non-decreasing. Elements