tf.contrib.distributions.DirichletMultinomial.log_pdf(value, name='log_pdf') Log probability density function.

tf.contrib.bayesflow.stochastic_tensor.CategoricalTensor.clone(name=None, **dist_args)

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

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

tf.contrib.learn.monitors.RunHookAdapterForMonitors.begin()

tf.contrib.learn.monitors.GraphDump.begin(max_steps=None)

tf.contrib.graph_editor.SubGraphView.__init__(inside_ops=(), passthrough_ts=()) Create a subgraph containing the given ops and

tf.contrib.bayesflow.stochastic_tensor.DirichletMultinomialTensor.distribution

tf.get_session_handle(data, name=None) Return the handle of data. This is EXPERIMENTAL

tf.asin(x, name=None) Computes asin of x element-wise. Args: