tf.contrib.learn.monitors.NanLoss.every_n_step_end(step, outputs)

tf.contrib.learn.monitors.PrintTensor.epoch_end(epoch) End epoch. Args: epoch: int, the epoch number. Raises: ValueError: if we've not begun an epoch, or epoch number does not match.

class tf.contrib.bayesflow.stochastic_tensor.Chi2WithAbsDfTensor Chi2WithAbsDfTensor is a StochasticTensor backed by the distribution Chi2WithAbsDf.

static bool tensorflow::TensorShapeUtils::IsMatrix(const TensorShape &shape)

class tf.contrib.learn.monitors.EveryN Base class for monitors that execute callbacks every N steps. This class adds three new callbacks: - every_n_step_begin - every_n_step_end - every_n_post_step The callbacks are executed every n steps, or optionally every step for the first m steps, where m and n can both be user-specified. When extending this class, note that if you wish to use any of the BaseMonitor callbacks, you must call their respective super implementation: def step_begin(self, step

tf.contrib.learn.monitors.EveryN.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.learn.monitors.BaseMonitor.post_step(step, session) Callback after the step is finished. Called after step_end and receives session to perform extra session.run calls. If failure occurred in the process, will be called as well. Args: step: int, global step of the model. session: Session object.

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

tf.contrib.learn.monitors.GraphDump.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.graph_editor.SubGraphView.copy() Return a copy of itself. Note that this class is a "view", copying it only create another view and does not copy the underlying part of the tf.Graph. Returns: A new instance identical to the original one.