tf.contrib.bayesflow.stochastic_tensor.ObservedStochasticTensor.input_dict

tf.contrib.training.NextQueuedSequenceBatch.insertion_index The insertion indices of the examples (when they were first added). These indices start with the value -2**63 and increase with every call to the prefetch op. Each whole example gets its own insertion index, and this is used to prioritize the example so that its truncated segments appear in adjacent iterations, even if new examples are inserted by the prefetch op between iterations. Returns: An int64 vector of length batch_size, the i

tf.contrib.learn.monitors.NanLoss.set_estimator(estimator) A setter called automatically by the target estimator. If the estimator is locked, this method does nothing. Args: estimator: the estimator that this monitor monitors. Raises: ValueError: if the estimator is None.

Status tensorflow::Env::RenameFile(const string &src, const string &target) Renames file src to target. If target already exists, it will be replaced.

tf.contrib.learn.monitors.RunHookAdapterForMonitors.begin()

tf.contrib.learn.monitors.CaptureVariable.run_on_all_workers

tf.contrib.learn.monitors.NanLoss.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.

tf.contrib.distributions.Poisson.log_prob(value, name='log_prob') Log probability density/mass function (depending on is_continuous). Additional documentation from Poisson: Note thet the input value must be a non-negative floating point tensor with dtype dtype and whose shape can be broadcast with self.lam. x is only legal if it is non-negative and its components are equal to integer values. Args: value: float or double Tensor. name: The name to give this op. Returns: log_prob: a Tensor o

tf.contrib.learn.monitors.SummarySaver.set_estimator(estimator)

tf.contrib.distributions.Poisson.pmf(value, name='pmf') Probability mass function. Args: value: float or double Tensor. name: The name to give this op. Returns: pmf: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype. Raises: TypeError: if is_continuous.