# Write a callback¶

The places where each callback method gets called is demonstrated in this snippet:

def main_loop():
# ... create graph for the model ...
callbacks.setup_graph()
# ... create session, initialize session, finalize graph ...
# start training:
callbacks.before_train()
for epoch in range(epoch_start, epoch_end):
callbacks.before_epoch()
for step in range(steps_per_epoch):
run_one_step()  # callbacks.{before,after}_run are hooked with session
callbacks.trigger_step()
callbacks.after_epoch()
callbacks.trigger_epoch()
callbacks.after_train()


## Explain the callback methods¶

You can override any of the following methods to define a new callback:

• _setup_graph(self)

Setup the ops / tensors in the graph which you might need to use in the callback. You can use graph.get_tensor_by_name to access those already defined in the training tower. Or use self.trainer.get_predictor(..) to create a callable evaluation function in the predict tower.

This method is to separate between “define” and “run”, and also to avoid the common mistake to create ops inside loops. All changes to the graph should be made in this method.

• _before_train(self)

Can be used to run some manual initialization of variables, or start some services for the training.

• _after_train(self)

Usually some finalization work.

• _before_epoch(self), _after_epoch(self)

Use them only when you really need something to happen immediately before/after an epoch. Otherwise, _trigger_epoch should be enough.

• _before_run(self, ctx), _after_run(self, ctx, values)

This two are the equivlent of tf.train.SessionRunHook. Please refer to TensorFlow documentation for detailed API. They are used to run extra ops / eval extra tensors / feed extra values along with the actual training iterations.

Note the difference between running along with an iteration and running after an iteration. When you write

def _before_run(self, _):
return tf.train.SessionRunArgs(fetches=my_op)


The training loops would become sess.run([training_op, my_op]). This is different from sess.run(training_op); sess.run(my_op);, which is what you would get if you run the op in _trigger_step.

• _trigger_step(self)

Do something (e.g. running ops, print stuff) after each step has finished. Be careful to only do light work here because it could affect training speed.

• _trigger_epoch(self)

Do something after each epoch has finished. Will call self.trigger() by default.

• _trigger(self)

By default will get called by _trigger_epoch, but you can customize the scheduling of this callback by PeriodicTrigger, to let this method run every k steps or every k epochs.

## What you can do in the callback¶

• Access tensors / ops in either training / inference mode (need to create them in _setup_graph).

• Write stuff to the monitor backend, by self.trainer.monitors.put_xxx. The monitors might direct your events to TensorFlow events file, JSON file, stdout, etc. You can get history monitor data as well. See the docs for Monitors

• Access the current status of training, such as epoch_num, global_step. See here

• Anything else that can be done with plain python.