tf.contrib.learn.monitors.EveryN.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.learn.TensorFlowEstimator.config

tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalDiagWithSoftplusStDevTensor.value_type

tf.TextLineReader.serialize_state(name=None) Produce a string tensor that encodes the state of a reader. Not all Readers support being serialized, so this can produce an Unimplemented error. Args: name: A name for the operation (optional). Returns: A string Tensor.

tf.contrib.learn.monitors.ValidationMonitor.post_step(step, session)

tf.contrib.learn.monitors.PrintTensor.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.

tf.contrib.distributions.Distribution.is_reparameterized

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

tf.contrib.distributions.InverseGamma.log_pdf(value, name='log_pdf') Log probability density function. Args: value: float or double Tensor. name: The name to give this op. Returns: log_prob: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype. Raises: TypeError: if not is_continuous.

tf.contrib.bayesflow.stochastic_tensor.WishartFullTensor.clone(name=None, **dist_args)