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

tf.contrib.distributions.BetaWithSoftplusAB.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.

static bool tensorflow::TensorShapeUtils::IsVector(const TensorShape &shape)

tf.contrib.bayesflow.stochastic_tensor.LaplaceTensor.clone(name=None, **dist_args)

tf.contrib.distributions.BernoulliWithSigmoidP.survival_function(value, name='survival_function') Survival function. Given random variable X, the survival function is defined: survival_function(x) = P[X > x] = 1 - P[X <= x] = 1 - cdf(x). Args: value: float or double Tensor. name: The name to give this op. Returns: Tensorof shapesample_shape(x) + self.batch_shapewith values of typeself.dtype`.

tf.contrib.bayesflow.stochastic_tensor.StudentTTensor.name

tf.contrib.learn.monitors.CheckpointSaver.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.bayesflow.stochastic_tensor.Chi2Tensor.distribution

tf.contrib.learn.TensorFlowEstimator.model_dir

tensorflow::TensorShapeDim::TensorShapeDim(int64 s)