Customize your own logging

In this section, you will learn about the following things:

After reading this section, you will be able to:

  • Write your own report.

What is Reporter?

chainer.Reporter is used to collect values that users want to watch. The reporter object manipulates a dictionary from value names to the actually observed values. We call this dictionary as observation.

See the following example:

>>> from chainer import Reporter, report, report_scope
>>> reporter = Reporter()
>>> observer = object()  # it can be an arbitrary (reference) object
>>> reporter.add_observer('my_observer:', observer)
>>> observation = {}
>>> with reporter.scope(observation):
...{'x': 1}, observer)
>>> observation
{'my_observer:/x': 1}

When a value is passed to the reporter, an object called observer can be optionally attached. In this case, the name of the observer is added as the prefix of the value name. The observer name should be registered beforehand. Using reporter.scope, you can select which observation to save the observed values.

There are also a global API, which reports observed values with the current reporter object. In this case, current means which with statement scope the current code line is in. This function calls the method of the current reporter.

>>> observation = {}
>>> with reporter.scope(observation):
...     report({'x': 1}, observer)
>>> observation
{'my_observer:/x': 1}

Naming rule for the reported values

So, you know almost everything about Reporter. However, there is one more thing. It is what is the naming rule for the reported values, especially when the values are reported from a link that is not the root of the link hierarchy.

As we explained in the previous section, the root of links is named as 'main' by the the StandardUpdater and the names of reported values in the root have the prefix 'main/'. When the values are reported from a link that is not the root of the link hierarchy, the prefix of the names are determined by the link hierarchy, or namedlinks().

See the following example:

>>> class MLP(Chain):
...     def __init__(self, n_units, n_out):
...         super(MLP, self).__init__()
...         with self.init_scope():
...             # the size of the inputs to each layer will be inferred
...             self.l1 = L.Linear(None, n_units)  # n_in -> n_units
...             self.l2 = L.Linear(None, n_units)  # n_units -> n_units
...             self.l3 = L.Linear(None, n_out)    # n_units -> n_out
...     def forward(self, x):
...         h1 = F.relu(self.l1(x))
...         h2 = F.relu(self.l2(h1))
...         y = self.l3(h2)
...         report({'sum_y': F.sum(y)}, self)
...         return y
>>> model = Classifier(MLP(100, 10))
>>> for name, observer in model.namedlinks(skipself=True):
...     print(name)  

You can get the parameters of the link hierarchy by namedlinks(). In this example, we report 'loss' and 'accuracy' in the root of links, and 'sum_y' in the link of '/predictor'. So, you can access the reported values by 'main/accuracy', 'main/accuracy', and 'main/predictor/sum_y'.

See what we explained is correct:

>>> train, test = datasets.get_mnist()
>>> train_iter = iterators.SerialIterator(train, batch_size=100, shuffle=True)
>>> test_iter = iterators.SerialIterator(test, batch_size=100, repeat=False, shuffle=False)
>>> optimizer = optimizers.SGD()
>>> optimizer.setup(model)
>>> updater = training.StandardUpdater(train_iter, optimizer)
>>> trainer = training.Trainer(updater, (1, 'epoch'), out='result')
>>> trainer.extend(extensions.Evaluator(test_iter, model))
>>> trainer.extend(extensions.LogReport())
>>> trainer.extend(extensions.PrintReport(
...     ['epoch', 'main/accuracy', 'main/loss', 'main/predictor/sum_y', 'validation/main/accuracy']))
epoch       main/accuracy  main/loss   main/predictor/sum_y  validation/main/accuracy
1           0.662317       1.38345     47.9927               0.8498