tf.div(x, y, name=None) Returns x / y element-wise. NOTE: Div supports broadcasting. More about broadcasting here Args: x: A Tensor. Must be one of the following types: half, float32, float64, uint8, int8, uint16, int16, int32, int64, complex64, complex128. y: A Tensor. Must have the same type as x. name: A name for the operation (optional). Returns: A Tensor. Has the same type as x.

tf.contrib.bayesflow.stochastic_tensor.DirichletTensor.distribution

tf.contrib.bayesflow.stochastic_tensor.StochasticTensor.value_type

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

tf.contrib.graph_editor.OpMatcher.control_input_ops(*args) Add input matches.

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

tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalDiagPlusVDVTTensor.distribution

tf.contrib.graph_editor.OpMatcher.output_ops(*args) Add output matches.

tf.contrib.bayesflow.stochastic_tensor.SampleValue.declare_inputs(unused_stochastic_tensor, unused_inputs_dict)

class tf.contrib.learn.monitors.LoggingTrainable Writes trainable variable values into log every N steps. Write the tensors in trainable variables every_n steps, starting with the first_nth step.