tf.contrib.distributions.TransformedDistribution.log_pdf(value, name='log_pdf') Log probability density function. Args: value: float or double Tensor. name: The name to give this op. Returns: log_prob: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype. Raises: TypeError: if not is_continuous.

tf.errors.CancelledError.__init__(node_def, op, message) Creates a CancelledError.

tf.contrib.distributions.TransformedDistribution.log_cdf(value, name='log_cdf') Log cumulative distribution function. Given random variable X, the cumulative distribution function cdf is: log_cdf(x) := Log[ P[X <= x] ] Often, a numerical approximation can be used for log_cdf(x) that yields a more accurate answer than simply taking the logarithm of the cdf when x << -1. Args: value: float or double Tensor. name: The name to give this op. Returns: logcdf: a Tensor of shape sample_

class tf.contrib.distributions.DirichletMultinomial DirichletMultinomial mixture distribution. This distribution is parameterized by a vector alpha of concentration parameters for k classes and n, the counts per each class..

class tf.contrib.bayesflow.stochastic_tensor.TransformedDistributionTensor TransformedDistributionTensor is a StochasticTensor backed by the distribution TransformedDistribution.

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

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

tf.contrib.bayesflow.stochastic_tensor.BetaWithSoftplusABTensor.value_type

tf.contrib.distributions.Exponential.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.framework.get_or_create_global_step(graph=None) Returns and create (if necessary) the global step variable. Args: graph: The graph in which to create the global step. If missing, use default graph. Returns: the tensor representing the global step variable.