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

tf.contrib.learn.monitors.LoggingTrainable.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.DirichletMultinomial.parameters Dictionary of parameters used by this Distribution.

tf.contrib.distributions.BetaWithSoftplusAB.dtype The DType of Tensors handled by this Distribution.

tf.contrib.learn.Estimator.set_params(**params) Set the parameters of this estimator. The method works on simple estimators as well as on nested objects (such as pipelines). The former have parameters of the form <component>__<parameter> so that it's possible to update each component of a nested object. Args: **params: Parameters. Returns: self Raises: ValueError: If params contain invalid names.

tf.contrib.graph_editor.matcher.output_ops(*args) Add output matches.

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

tf.contrib.distributions.Mixture.is_reparameterized

tf.contrib.distributions.Bernoulli.parameters Dictionary of parameters used by this Distribution.