I am reading the a deep learning with python book. After reading chapter 4, Fighting Overfitting, I have two questions.
Why might increasing the number of epochs cause overfitting? I know increasing increasing the number of epochs will involve more attempts at gradient descent, will this cause overfitting?
During the process of fighting overfitting, will the accuracy be reduced ?
I'm not sure which book you are reading, so some background information may help before I answer the questions specifically.
Firstly, increasing the number of epochs won't necessarily cause overfitting, but it certainly can do. If the learning rate and model parameters are small, it may take many epochs to cause measurable overfitting. That said, it is common for more training to do so.
To keep the question in perspective, it's important to remember that we most commonly use neural networks to build models we can use for prediction (e.g. predicting whether an image contains a particular object or what the value of a variable will be in the next time step).
We build the model by iteratively adjusting weights and biases so that the network can act as a function to translate between input data and predicted outputs. We turn to such models for a number of reasons, often because we just don't know what the function is/should be or the function is too complex to develop analytically. In order for the network to be able to model such complex functions, it must be capable of being highly-complex itself. Whilst this complexity is powerful, it is dangerous! The model can become so complex that it can effectively remember the training data very precisely but then fail to act as an effective, general function that works for data outside of the training set. I.e. it can overfit.
You can think of it as being a bit like someone (the model) who learns to bake by only baking fruit cake (training data) over and over again – soon they'll be able to bake an excellent fruit cake without using a recipe (training), but they probably won't be able to bake a sponge cake (unseen data) very well.
Back to neural networks! Because the risk of overfitting is high with a neural network there are many tools and tricks available to the deep learning engineer to prevent overfitting, such as the use of dropout. These tools and tricks are collectively known as 'regularisation'.
This is why we use development and training strategies involving test datasets – we pretend that the test data is unseen and monitor it during training. You can see an example of this in the plot below (image credit). After about 50 epochs the test error begins to increase as the model has started to 'memorise the training set', despite the training error remaining at its minimum value (often training error will continue to improve).
So, to answer your questions:
Allowing the model to continue training (i.e. more epochs) increases the risk of the weights and biases being tuned to such an extent that the model performs poorly on unseen (or test/validation) data. The model is now just 'memorising the training set'.
Continued epochs may well increase training accuracy, but this doesn't necessarily mean the model's predictions from new data will be accurate – often it actually gets worse. To prevent this, we use a test data set and monitor the test accuracy during training. This allows us to make a more informed decision on whether the model is becoming more accurate for unseen data.
We can use a technique called early stopping, whereby we stop training the model once test accuracy has stopped improving after a small number of epochs. Early stopping can be thought of as another regularisation technique.
More attempts of decent(large number of epochs) can take you very close to the global minima of the loss function ideally, Now since we don't know anything about the test data, fitting the model so precisely to predict the class labels of the train data may cause the model to lose it generalization capabilities(error over unseen data). In a way, no doubt we want to learn the input-output relationship from the train data, but we must not forget that the end goal is for the model to perform well over the unseen data. So, it is a good idea to stay close but not very close to the global minima. But still, we can ask what if I reach the global minima, what can be the problem with that, why would it cause the model to perform badly on unseen data? The answer to this can be that in order to reach the global minima we would be trying to fit the maximum amount of train data, this will result in a very complex model(since it is less probable to have a simpler spatial distribution of the selected number of train data that is fortunately available with us). But what we can assume is that a large amount of unseen data(say for facial recognition) will have a simpler spatial distribution and will need a simpler Model for better classification(I mean the entire world of unseen data, will definitely have a pattern that we can't observe just because we have an access small fraction of it in the form of training data)
If you incrementally observe points from a distribution(say 50,100,500, 1000 ...), we will definitely find the structure of the data complex until we have observed a sufficiently large number of points (max: the entire distribution), but once we have observed enough points we can expect to observe the simpler pattern present in the data that can be easily classified.
In short, a small fraction of train data should have a complex structure as compared to the entire dataset. And overfitting to the train data may cause our model to perform worse on the test data.
One analogous example to emphasize the above phenomenon from day to day life is as follows:-
Say we meet N number of people till date in our lifetime, while meeting them we naturally learn from them(we become what we are surrounded with). Now if we are heavily influenced by each individual and try to tune to the behaviour of all the people very closely, we develop a personality that closely resembles the people we have met but on the other hand we start judging every individual who is unlike me -> unlike the people we have already met. Becoming judgemental takes a toll on our capability to tune in with new groups since we trained very hard to minimize the differences with the people we have already met(the training data). This according to me is an excellent example of overfitting and loss in genralazition capabilities.
