tf.contrib.learn.monitors.ExportMonitor.step_end(step, output) Overrides BaseMonitor.step_end. When overriding this method, you must call the super implementation. Args: step: int, the current value of the global step. output: dict mapping string values representing tensor names to the value resulted from running these tensors. Values may be either scalars, for scalar tensors, or Numpy array, for non-scalar tensors. Returns: bool, the result of every_n_step_end, if that was called this ste

tf.contrib.distributions.Mixture.is_reparameterized

tf.contrib.layers.summarize_activations(name_filter=None, summarizer=summarize_activation) Summarize activations, using summarize_activation to summarize.

tf.contrib.bayesflow.stochastic_tensor.DirichletTensor.distribution

tf.contrib.bayesflow.stochastic_tensor.StochasticTensor.value_type

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

tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalDiagPlusVDVTTensor.distribution

tf.contrib.distributions.BernoulliWithSigmoidP.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.learn.monitors.LoggingTrainable.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.

tf.contrib.distributions.Chi2WithAbsDf.mean(name='mean') Mean.