As we already know that the values of the metrics such as loss, acc, etc. that we get during the training are the averaged values over the epoch. This averaging of the values are automatically applied to every Keras model using the BaseLogger class present under the Keras callbacks. This class also provides us with the flexibility of not averaging the metrics over an epoch. So, in this blog, let’s discuss this BaseLogger class in more detail.

Keras API

Similar to the History callback, this callback is also automatically applied to every Keras model with the default set of arguments. Only if you want to change the arguments, you need to apply it similar to how we applied other callbacks i.e. pass in the fit() method.

Here, stateful_metrics are the name of the metrics that you don’t want to average over an epoch. All the metrics names should be passed in the form of iterable like lists etc. For instance, stateful_metrics=[‘acc’,’loss’].

The value of the stateful_metrics will be saved as-is in on_epoch_end. In other words, the value of the stateful_metric in the last batch before the epoch end will be saved as the final value. All the other remaining metrics will be averaged over the epoch ends. Let’s take a look at the image below.

Here, I trained the model for 1 epoch on the mnist data. The stateful_metrics used is ‘acc’. Clearly, the final logged accuracy (See record.history) is similar to the last batch accuracy (0.9392) and not the average accuracy obtained on the epoch end (0.9405). Hope this makes everything clear. Let’s see how to apply baselogger callback.

That’s all for this blog. Hope you enjoy reading.

If you have any doubt/suggestion please feel free to ask and I will do my best to help or improve myself. Good-bye until next time.