tf.contrib.learn.monitors.StepCounter.end(session=None)

tf.contrib.distributions.QuantizedDistribution.mode(name='mode') Mode.

tf.contrib.learn.monitors.NanLoss.post_step(step, session)

string tensorflow::PartialTensorShape::DebugString() const For error messages.

tf.contrib.bayesflow.stochastic_tensor.GammaWithSoftplusAlphaBetaTensor.input_dict

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

tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.get_event_shape() Shape of a single sample from a single batch as a TensorShape. Same meaning as event_shape. May be only partially defined. Returns: event_shape: TensorShape, possibly unknown.

tf.contrib.distributions.BetaWithSoftplusAB.name Name prepended to all ops created by this Distribution.

tf.contrib.distributions.QuantizedDistribution.std(name='std') Standard deviation.

tf.contrib.metrics.accuracy(predictions, labels, weights=None) Computes the percentage of times that predictions matches labels. Args: predictions: the predicted values, a Tensor whose dtype and shape matches 'labels'. labels: the ground truth values, a Tensor of any shape and bool, integer, or string dtype. weights: None or Tensor of float values to reweight the accuracy. Returns: Accuracy Tensor. Raises: ValueError: if dtypes don't match or if dtype is not bool, integer, or string.