tf.contrib.learn.monitors.ExportMonitor.step_begin(step) Overrides BaseMonitor.step_begin. When overriding this method, you must call the super implementation. Args: step: int, the current value of the global step. Returns: A list, the result of every_n_step_begin, if that was called this step, or an empty list otherwise. Raises: ValueError: if called more than once during a step.

class tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalDiagTensor MultivariateNormalDiagTensor is a StochasticTensor backed by the distribution MultivariateNormalDiag.

tf.TensorArray.size(name=None) Return the size of the TensorArray.

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

tf.contrib.bayesflow.stochastic_tensor.BernoulliTensor.name

tf.contrib.bayesflow.stochastic_tensor.DirichletTensor.name

tf.contrib.distributions.QuantizedDistribution.param_shapes(cls, sample_shape, name='DistributionParamShapes') Shapes of parameters given the desired shape of a call to sample(). Subclasses should override static method _param_shapes. Args: sample_shape: Tensor or python list/tuple. Desired shape of a call to sample(). name: name to prepend ops with. Returns: dict of parameter name to Tensor shapes.

tf.contrib.distributions.Laplace.mean(name='mean') Mean.

tf.contrib.distributions.MultivariateNormalDiag.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.contrib.learn.monitors.StepCounter.every_n_post_step(step, session) Callback after a step is finished or end() is called. Args: step: int, the current value of the global step. session: Session object.