tf.contrib.bayesflow.stochastic_tensor.BinomialTensor.dtype

tf.contrib.distributions.Exponential.validate_args Python boolean indicated possibly expensive checks are enabled.

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

tf.contrib.bayesflow.stochastic_tensor.MultinomialTensor.input_dict

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.MultivariateNormalDiagWithSoftplusStDev.name Name prepended to all ops created by this Distribution.