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.

tf.contrib.distributions.WishartFull.mean_log_det(name='mean_log_det') Computes E[log(det(X))] under this Wishart distribution.

tf.contrib.graph_editor.ControlOutputs.update() Update the control outputs if the graph has changed.

tf.contrib.distributions.Chi2WithAbsDf.validate_args Python boolean indicated possibly expensive checks are enabled.

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.distributions.StudentT.cdf(value, name='cdf') Cumulative distribution function. Given random variable X, the cumulative distribution function cdf is: cdf(x) := P[X <= x] Args: value: float or double Tensor. name: The name to give this op. Returns: cdf: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.