tf.contrib.learn.monitors.CaptureVariable.every_n_post_step(step, session) Callback after a step is finished or end() is called. Args: step: int, the current value of the global step. session: Session object.

tf.contrib.learn.monitors.CaptureVariable.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.

tf.contrib.learn.monitors.CaptureVariable.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.CaptureVariable.end(session=None)

tf.contrib.learn.monitors.CaptureVariable.begin(max_steps=None) Called at the beginning of training. When called, the default graph is the one we are executing. Args: max_steps: int, the maximum global step this training will run until. Raises: ValueError: if we've already begun a run.

class tf.contrib.learn.monitors.CaptureVariable Captures a variable's values into a collection. This monitor is useful for unit testing. You should exercise caution when using this monitor in production, since it never discards values. This is an EveryN monitor and has consistent semantic for every_n and first_n.

tf.contrib.learn.monitors.BaseMonitor.__init__()

tf.contrib.learn.monitors.BaseMonitor.step_end(step, output) Callback after training step finished. This callback provides access to the tensors/ops evaluated at this step, including the additional tensors for which evaluation was requested in step_begin. In addition, the callback has the opportunity to stop training by returning True. This is useful for early stopping, for example. Note that this method is not called if the call to Session.run() that followed the last call to step_begin() fai

tf.contrib.learn.monitors.BaseMonitor.step_begin(step) Callback before training step begins. You may use this callback to request evaluation of additional tensors in the graph. Args: step: int, the current value of the global step. Returns: List of Tensor objects or string tensor names to be run. Raises: ValueError: if we've already begun a step, or step < 0, or step > max_steps.

tf.contrib.learn.monitors.BaseMonitor.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.