Determining number of epochs for model fitting in Keras

Question

I'm trying to automatically determine when a Keras autoencoder converges. For example, look at this link under "Let's build the simplest autoencoder possible." The number of epochs is hardcoded at 50 (when the loss value converges). However, how would you code this using Keras if you didn't know the number was 50? Would you just keep calling fit()?

Answer 1

This question is actually ridiculously wide and hard. There are many techniques on how to set the number of epochs:

• Early stopping- in this case you set the number of epochs to a really high number and you turn off the training when the improvement over next epochs is not satisfying. In Keras you have a special object called EarlyStopping which does the job for you.
• Model Checkpoint - here you once again set up a really high number of epochs and you simply save only the best model w.r.t. to a metric chosen. Once again you have a special callback for this scenario.

Of course, there are other scenarios like e.g. using Reinforcement learning to find the stopping time or more complexed scenarios when you choose this in a Bayesian hyperparameter set up but those are much harder methods which are often not introducing any improvement.

One sure thing is that restarting a fit method might end up in unexpected behaviour as many inner states of a model are reset which could cause instability. For this scenario I strongly advise you to use train_on_batch which is not resetting model states and makes a lot of fancy training scenarios possible.