tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.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 S

tf.contrib.distributions.Poisson.log_pdf(value, name='log_pdf') Log probability density function. Args: value: float or double Tensor. name: The name to give this op. Returns: log_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.Laplace.name Name prepended to all ops created by this Distribution.

tf.contrib.training.NextQueuedSequenceBatch.sequence An int32 vector, length batch_size: the sequence index of each entry. When an input is split up, the sequence values 0, 1, ..., sequence_count - 1 are assigned to each split. Returns: An int32 vector Tensor.

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

tf.contrib.distributions.Distribution.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.SparseTensorValue.values Alias for field number 1

tf.contrib.bayesflow.stochastic_tensor.DirichletMultinomialTensor.distribution

tf.contrib.distributions.ExponentialWithSoftplusLam.log_pmf(value, name='log_pmf') Log probability mass function. Args: value: float or double Tensor. name: The name to give this op. Returns: log_pmf: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype. Raises: TypeError: if is_continuous.

tf.contrib.learn.monitors.SummarySaver.every_n_step_end(step, outputs)