tf.contrib.bayesflow.stochastic_tensor.InverseGammaTensor.name

tf.contrib.bayesflow.stochastic_tensor.Chi2WithAbsDfTensor.clone(name=None, **dist_args)

tf.is_numeric_tensor(tensor)

tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalDiagTensor.name

tf.contrib.distributions.Uniform.__init__(a=0.0, b=1.0, validate_args=False, allow_nan_stats=True, name='Uniform') Construct Uniform distributions with a and b. The parameters a and b must be shaped in a way that supports broadcasting (e.g. b - a is a valid operation). Here are examples without broadcasting: # Without broadcasting u1 = Uniform(3.0, 4.0) # a single uniform distribution [3, 4] u2 = Uniform([1.0, 2.0], [3.0, 4.0]) # 2 distributions [1, 3], [2, 4] u3 = Uniform([[1.0, 2.0],

tf.contrib.training.SequenceQueueingStateSaver.next_batch The NextQueuedSequenceBatch providing access to batched output data. Also provides access to the state and save_state methods. The first time this gets called, it additionally prepares barrier reads and creates NextQueuedSequenceBatch / next_batch objects. Subsequent calls simply return the previously created next_batch. In order to access data in next_batch without blocking, the prefetch_op must have been run at least batch_size times

tf.contrib.distributions.Uniform.event_shape(name='event_shape') Shape of a single sample from a single batch as a 1-D int32 Tensor. Args: name: name to give to the op Returns: event_shape: Tensor.

tf.contrib.distributions.Binomial.mean(name='mean') Mean.

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

tf.contrib.distributions.TransformedDistribution.get_event_shape() Shape of a single sample from a single batch as a TensorShape. Same meaning as event_shape. May be only partially defined. Returns: event_shape: TensorShape, possibly unknown.