tf.contrib.bayesflow.stochastic_tensor.Chi2WithAbsDfTensor.graph

tf.contrib.distributions.MultivariateNormalDiag.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.distributions.WishartFull.variance(name='variance') Variance.

tf.contrib.util.make_ndarray(tensor) Create a numpy ndarray from a tensor. Create a numpy ndarray with the same shape and data as the tensor. Args: tensor: A TensorProto. Returns: A numpy array with the tensor contents. Raises: TypeError: if tensor has unsupported type.

tf.contrib.distributions.NormalWithSoftplusSigma.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

tf.TensorArray.flow The flow Tensor forcing ops leading to this TensorArray state.

tf.contrib.learn.TensorFlowRNNClassifier.config

tf.contrib.learn.monitors.ExportMonitor.epoch_begin(epoch) Begin epoch. Args: epoch: int, the epoch number. Raises: ValueError: if we've already begun an epoch, or epoch < 0.

tf.contrib.distributions.Chi2.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.bayesflow.stochastic_tensor.ObservedStochasticTensor.entropy(name='entropy')