tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalDiagPlusVDVTTensor.loss(final_loss, name='Loss')

tf.contrib.bayesflow.stochastic_tensor.ExponentialWithSoftplusLamTensor.entropy(name='entropy')

class tf.contrib.bayesflow.stochastic_tensor.BernoulliWithSigmoidPTensor BernoulliWithSigmoidPTensor is a StochasticTensor backed by the distribution BernoulliWithSigmoidP.

tf.train.batch(tensors, batch_size, num_threads=1, capacity=32, enqueue_many=False, shapes=None, dynamic_pad=False, allow_smaller_final_batch=False, shared_name=None, name=None) Creates batches of tensors in tensors. The argument tensors can be a list or a dictionary of tensors. The value returned by the function will be of the same type as tensors. This function is implemented using a queue. A QueueRunner for the queue is added to the current Graph's QUEUE_RUNNER collection. If enqueue_many i

tf.contrib.graph_editor.SubGraphView.__nonzero__() Allows for implicit boolean conversion.

tf.contrib.distributions.MultivariateNormalFull.get_batch_shape() Shape of a single sample from a single event index as a TensorShape. Same meaning as batch_shape. May be only partially defined. Returns: batch_shape: TensorShape, possibly unknown.

tf.contrib.distributions.Multinomial.param_shapes(cls, sample_shape, name='DistributionParamShapes') Shapes of parameters given the desired shape of a call to sample(). Subclasses should override static method _param_shapes. Args: sample_shape: Tensor or python list/tuple. Desired shape of a call to sample(). name: name to prepend ops with. Returns: dict of parameter name to Tensor shapes.

class tf.contrib.bayesflow.stochastic_tensor.StudentTTensor StudentTTensor is a StochasticTensor backed by the distribution StudentT.

tf.contrib.bayesflow.stochastic_tensor.InverseGammaWithSoftplusAlphaBetaTensor.graph

tf.contrib.framework.assert_scalar_int(tensor) Assert tensor is 0-D, of type tf.int32 or tf.int64. Args: tensor: Tensor to test. Returns: tensor, for chaining. Raises: ValueError: if tensor is not 0-D, of type tf.int32 or tf.int64.