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

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

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..

tf.contrib.distributions.Uniform.param_static_shapes(cls, sample_shape) param_shapes with static (i.e. TensorShape) shapes. Args: sample_shape: TensorShape or python list/tuple. Desired shape of a call to sample(). Returns: dict of parameter name to TensorShape. Raises: ValueError: if sample_shape is a TensorShape and is not fully defined.

tf.QueueBase.name The name of the underlying queue.

tf.contrib.distributions.Binomial.name Name prepended to all ops created by this Distribution.

tf.contrib.distributions.Laplace.dtype The DType of Tensors handled by this Distribution.