tf.contrib.learn.monitors.PrintTensor.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.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.

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

tf.contrib.learn.monitors.PrintTensor.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.NanLoss.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 step, or

class tf.contrib.learn.monitors.PrintTensor Prints given tensors every N steps. This is an EveryN monitor and has consistent semantic for every_n and first_n. The tensors will be printed to the log, with INFO severity.

tf.contrib.learn.monitors.NanLoss.__init__(loss_tensor, every_n_steps=100, fail_on_nan_loss=True) Initializes NanLoss monitor. Args: loss_tensor: Tensor, the loss tensor. every_n_steps: int, run check every this many steps. fail_on_nan_loss: bool, whether to raise exception when loss is NaN.

tf.contrib.learn.monitors.PrintTensor.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.

tf.contrib.learn.monitors.NanLoss.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.