tf.contrib.learn.Estimator.get_variable_names() Returns list of all variable names in this model. Returns: List of names.

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

tf.contrib.distributions.Multinomial.pmf(value, name='pmf') Probability mass function. Args: value: float or double Tensor. name: The name to give this op. Returns: pmf: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype. Raises: TypeError: if is_continuous.

tf.random_uniform(shape, minval=0, maxval=None, dtype=tf.float32, seed=None, name=None) Outputs random values from a uniform distribution. The generated values follow a uniform distribution in the range [minval, maxval). The lower bound minval is included in the range, while the upper bound maxval is excluded. For floats, the default range is [0, 1). For ints, at least maxval must be specified explicitly. In the integer case, the random integers are slightly biased unless maxval - minval is an

tf.contrib.bayesflow.stochastic_tensor.DirichletMultinomialTensor.value_type

tf.contrib.distributions.BernoulliWithSigmoidP.batch_shape(name='batch_shape') Shape of a single sample from a single event index as a 1-D Tensor. The product of the dimensions of the batch_shape is the number of independent distributions of this kind the instance represents. Args: name: name to give to the op Returns: batch_shape: Tensor.

tf.contrib.distributions.Chi2WithAbsDf.survival_function(value, name='survival_function') Survival function. Given random variable X, the survival function is defined: survival_function(x) = P[X > x] = 1 - P[X <= x] = 1 - cdf(x). Args: value: float or double Tensor. name: The name to give this op. Returns: Tensorof shapesample_shape(x) + self.batch_shapewith values of typeself.dtype`.

tf.contrib.bayesflow.stochastic_tensor.Chi2Tensor.dtype

tf.contrib.bayesflow.stochastic_tensor.WishartFullTensor.input_dict

tf.contrib.learn.monitors.LoggingTrainable.post_step(step, session)