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

class tf.contrib.training.NextQueuedSequenceBatch NextQueuedSequenceBatch stores deferred SequenceQueueingStateSaver data. This class is instantiated by SequenceQueueingStateSaver and is accessible via its next_batch property.

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

tf.contrib.distributions.WishartCholesky.df Wishart distribution degree(s) of freedom.

Member Details tensorflow::Thread::Thread() tensorflow::Thread::~Thread() Blocks until the thread of control stops running.

tf.contrib.losses.hinge_loss(logits, target, scope=None) Method that returns the loss tensor for hinge loss. Args: logits: The logits, a float tensor. target: The ground truth output tensor. Its shape should match the shape of logits. The values of the tensor are expected to be 0.0 or 1.0. scope: The scope for the operations performed in computing the loss. Returns: A Tensor of same shape as logits and target representing the loss values across the batch. Raises: ValueError: If the shape

tf.contrib.bayesflow.stochastic_tensor.CategoricalTensor.name

tf.nn.rnn_cell.EmbeddingWrapper.output_size

tf.contrib.bayesflow.stochastic_tensor.BetaWithSoftplusABTensor.dtype

tf.contrib.distributions.Mixture.allow_nan_stats Python boolean describing behavior when a stat is undefined. Stats return +/- infinity when it makes sense. E.g., the variance of a Cauchy distribution is infinity. However, sometimes the statistic is undefined, e.g., if a distribution's pdf does not achieve a maximum within the support of the distribution, the mode is undefined. If the mean is undefined, then by definition the variance is undefined. E.g. the mean for Student's T for df = 1 is u