chainer.training.extensions.ProgressBar

class chainer.training.extensions.ProgressBar(training_length=None, update_interval=100, bar_length=50, out=<open file '<stdout>', mode 'w'>)

Trainer extension to print a progress bar and recent training status.

This extension prints a progress bar at every call. It watches the current iteration and epoch to print the bar.

Parameters:
  • training_length (tuple) – Length of whole training. It consists of an integer and either 'epoch' or 'iteration'. If this value is omitted and the stop trigger of the trainer is IntervalTrigger, this extension uses its attributes to determine the length of the training.
  • update_interval (int) – Number of iterations to skip printing the progress bar.
  • bar_length (int) – Length of the progress bar in characters.
  • out – Stream to print the bar. Standard output is used by default.

Methods

__call__(trainer)
finalize()
initialize(trainer)

Initializes up the trainer state.

This method is called before entering the training loop. An extension that modifies the state of Trainer can override this method to initialize it.

When the trainer has been restored from a snapshot, this method has to recover an appropriate part of the state of the trainer.

For example, ExponentialShift extension changes the optimizer’s hyperparameter at each invocation. Note that the hyperparameter is not saved to the snapshot; it is the responsibility of the extension to recover the hyperparameter. The ExponentialShift extension recovers it in its initialize method if it has been loaded from a snapshot, or just setting the initial value otherwise.

Parameters:trainer (Trainer) – Trainer object that runs the training loop.
serialize(serializer)

Serializes the extension state.

It is called when a trainer that owns this extension is serialized. It serializes nothing by default.

Attributes

default_name

Default name of the extension.

It is the name of the class by default. Implementation can override this property, or provide a class attribute to hide it.

priority = 100
trigger = (1, 'iteration')