class tf.contrib.distributions.Dirichlet Dirichlet distribution. This distribution is parameterized by a vector alpha of concentration parameters for k classes.

tf.contrib.bayesflow.stochastic_tensor.TransformedDistributionTensor.distribution

tf.test.get_temp_dir() Returns a temporary directory for use during tests. There is no need to delete the directory after the test. Returns: The temporary directory.

tf.contrib.distributions.Dirichlet.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.distributions.StudentTWithAbsDfSoftplusSigma.__init__(df, mu, sigma, validate_args=False, allow_nan_stats=True, name='StudentTWithAbsDfSoftplusSigma')

tf.contrib.distributions.ExponentialWithSoftplusLam.is_reparameterized

tf.python_io.TFRecordWriter.__exit__(unused_type, unused_value, unused_traceback) Exit a with block, closing the file.

tf.contrib.learn.TensorFlowEstimator.__repr__()

Status tensorflow::PartialTensorShape::MergeWith(const PartialTensorShape &shape, PartialTensorShape *result) const Merges all the dimensions from shape. Returns InvalidArgument error if either shape has a different rank or if any of the dimensions are incompatible.

tf.contrib.bayesflow.stochastic_tensor.StochasticTensor.value(name='value')