Keras has provided several builtin classes/callbacks that serves our purpose for most of the cases. But let’s say we want to stop training when the accuracy has reached a benchmark or save the model at each batch. These tasks cannot be achieved using the builtin callbacks. In that case, we need to create our own callback function. In Keras, we can easily create custom callbacks using keras.callbacks.Callback() as our base class. But for that case, you need to create a class and write some amount of code.
As an alternative, Keras also provides us with an option to creates simple, custom callbacks on-the-fly. This can be done with ease using the LambdaCallback. So, in this blog, let’s discuss how to use this callback.
Note: For Python 3.8 or higher, lambda function will start supporting assignment expressions and this will make this callback even more powerful.
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keras.callbacks.LambdaCallback(on_epoch_begin=None, on_epoch_end=None, on_batch_begin=None, on_batch_end=None, on_train_begin=None, on_train_end=None) |
Here, all the arguments are aptly named according to their work. For instance, in “on_epoch_end” argument, we pass the function that will be called at the end of each epoch.
Now, all of these arguments expect functions with fixed positional arguments which are mentioned below.
Now, let’s see how to use this callback. Below I created a simple callback that saves the model weights when the accuracy is beyond some limit.
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from keras.callbacks import LambdaCallback call = LambdaCallback(on_epoch_end= lambda epochs, logs: model.save_weights('kang.hdf5') if logs.get('acc')>0.99 else None) |
Let’s take another example. Here, I’m stopping the training whenever the accuracy has reached a certain point. (For Python 3.8 or higher)
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from keras.callbacks import LambdaCallback call = LambdaCallback(on_epoch_end= lambda epochs, logs: (model.stop_training:=True) if logs.get('acc')>0.99 else None) |
Similarly, you can create any custom 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.