
# Callbacks

Apart from the actual training iterations that minimizes the cost,
you almost surely would like to do something else during training.
Callbacks are such an interface to describe what to do besides the
training iterations defined by the trainers.

There are several places where you might want to do something else:

* Before the training has started (e.g. initialize the saver)
* Along with each training iteration (e.g. run some other operations in the graph)
* Between training iterations (e.g. update the progress bar, update hyperparameters)
* Between epochs (e.g. save the model, run some validation)
* After the training (e.g. send the model somewhere, send a message to your phone)

By writing callbacks to implement these tasks, you can reuse the code as long as
you're using tensorpack trainers. For example, these are the callbacks I used when training
a ResNet:

```python
TrainConfig(
  # ...
  callbacks=[
    # save the model every epoch
    ModelSaver(),
    # run inference on another Dataflow every epoch, compute top1/top5 classification error and save them in log
    InferenceRunner(dataset_val, [
        ClassificationError('wrong-top1', 'val-error-top1'),
        ClassificationError('wrong-top5', 'val-error-top5')]),
    # schedule the learning rate based on epoch number
    ScheduledHyperParamSetter('learning_rate',
                              [(30, 1e-2), (60, 1e-3), (85, 1e-4), (95, 1e-5)]),
    # allow manually setting the learning rate during training
    HumanHyperParamSetter('learning_rate'),
    # send validation error to my phone through pushbullet
    SendStat('curl -u your_id_xxx: https://api.pushbullet.com/v2/pushes \\
               -d type=note -d title="validation error" \\
               -d body={val-error-top1} > /dev/null 2>&1',
               'val-error-top1')
  ],
  extra_callbacks=[    # these callbacks are enabled by default already
    # maintain and summarize moving average of some tensors defined in the model (e.g. training loss, training error)
    MovingAverageSummary(),
    # draw a nice progress bar
    ProgressBar(),
    # run `tf.summary.merge_all` every epoch and send results to monitors
    MergeAllSummaries(),
  ],
	monitors=[				# monitors are a special kind of callbacks. these are also enabled by default
		# write all monitor data to tensorboard
		TFSummaryWriter(),
		# write all scalar data to a json file, for easy parsing
		JSONWriter(),
		# print all scalar data every epoch (can be configured differently)
		ScalarPrinter(),
	]
)
```

Notice that callbacks really cover every detail of training, ranging from graph operations to the progress bar.
This means you can customize every part of the training to your preference, e.g. display something
different in the progress bar, evaluating part of the summaries at a different frequency, etc.
