When training neural networks, over-fit can be a major problem. Neural network models are getting very popular because they often achieve excellent results when trained with enough data and layers.
However, along with their success come some issues related to over fitting. If your model is too good at predicting patterns that match the dataset, then you will not get great predictions for samples that were not included in the training set.
This effect becomes more pronounced as the complexity of the model increases. More complex models require more parameters which must be tuned or adjusted!
In this article we will discuss three strategies to reduce overfitting in deep learning. These strategies apply mostly to image classification tasks, but can easily be adapted for other NN architectures such as VGG or ResNet.
Strategy 1: Using Validation Sets
Validation sets play an important role in determining how well a ML algorithm works. By using a validation set during testing, we can determine whether our current model is overfitting by checking if it performs better on its internal test set than the external one.
If it does, then the model is likely overfitting, making it less accurate overall. We therefore need to find ways to prevent this!
Fortunately, there are several methods to do so, including ones mentioned in today’s article! So let’s dive in and see what tricks exist.
Avoid overfitting by using more data
Recent developments in deep learning have focused on how to get better accuracy-those pretty pictures of cats that people use as examples often! This is excellent, as most people can recognize a cat easily enough!
However, this kind of training becomes problematic when you apply the model to new situations where it only sees more like examples.
Since we’re usually relying on statistical correlations for understanding what goes into a picture of a cat, a trained network will learn increasingly complex patterns with each example dataset it is exposed to.
This effect is called overfitting. The network gets “smoothed out” due to these matching patterns, and performs well on the examples it was trained on, but doesn’t generalize beyond them.
Generalizing means predicting outcomes for cases that were not present during training, which is why overfit networks are sometimes referred to as having too much bias – they assume that more instances of the same thing mean more likely than not that it will happen again.
By avoiding overfitting, you reduce the risk of those biases creeping in and ensuring your models work across multiple scenarios.
A very common technique used in deep learning is called *regularization*. This method works by limiting how much the neural networks’ parameters can vary so that it does not overfit the data too much.
A parameter of a neural network refers to something like the number of neurons or layers, or the amount of dropout you apply during training. By having these get tighter limits as the model gets closer and closer to being perfectly fit for the data, you reduce the risk of the model starting to blindly depend on the patterns of the data instead of developing its own understanding of the problem.
Regularized models are often more difficult to train, however. You have to find effective ways to limit these parameters while still producing a good result, which can be tricky at times!
At the same time, hard-to-train models sometimes overfit less than untrained ones, making them worse overall performers but better at fitting the data well. Finding a balance between under- and overtraining is an important part of choosing a model and configuration.
Use early stopping
In any supervised learning situation, overfit models are disastrous. A very strong predictor that does not improve when given more data is called under-fitted or even worse, bad!
A good way to prevent this is using what’s known as an early stop. Early stops occur when you run into trouble while training your model. You can either use error metrics like mean squared error (MSE) or loss function metrics such as accuracy to determine if it’s time to give up and start over.
Alternatively, you can add check points during training where you take a break and reevaluate the model. This way you don’t waste too much time trying to train something that isn’t working and could potentially hurt your test set scores at the end.
Using early stopping is quite common in NN settings so there are lots of resources out there about how to do it. What I will talk about here however, is one specific technique for reducing overfitting in DNNs.
Use feature engineering
A common cause of overfit is including too many features in your model. As mentioned before, adding too many features can lead to over-learning, or having to add more parameters to prevent the model from performing poorly.
Feature engineering means changing the structure or content of existing data so that the results are meaningful. For instance, if your goal is to determine whether an image contains cats, then using an animal recognition tool to check for whiskers and fur would be considered feature engineering.
By introducing new variables into the problem space, you open up possibilities for different solutions to achieve the same result. This also makes it easier to evaluate how well the solution works because you can test it on new images with no prediction!
You should always do some amount of feature engineering before trying to train a deep learning model. Sometimes, people will try to learn directly from raw pixels (this is called pixelation), but this may not work depending on the input format. Feature engineering allows you to take advantage of other information already present in the dataset to improve accuracy.
Use data hashing
In neural networks, over-fit can occur when the model learns patterns that are too specific to the training set. This is called overfitting. When this happens, your model will perform very well on the training sets it was trained with, but may not work as well on new test datasets!
Overfitting is bad because it means your model does not generalize well. It will often perform poorly even if it has seen the same pattern before!
Data hashing is an efficient way to reduce overfitting in deep learning by taking away some of the power from the layers of the network. By reducing their capacity, these overfit layers cannot learn overly complex features or patterns.
In this article we will discuss what data hashing is, how to apply it to image classification problems, and examples using Keras (a popular open source machine learning framework for Python).
Use different network architectures
A common beginner mistake is using too many hyperparameters of your model. As you can probably tell, this over-optimization usually hurts performance!
By having lots of parameters, the neural networks are free to learn any possible pattern it finds interesting, which includes patterns that do not match the data we have. This sometimes happens when there is no clear best choice – the model will choose whatever shape works for it at this moment.
This effect is called overfit, as the model “fits” the dataset very well but does not generalize well to new datasets or tasks. When training a neural net with too many parameters, it becomes difficult to determine what the individual layers are learning, making it hard to reproduce the results next time around.
One way to avoid overfitting is by limiting the number of parameters in your model. You can do this by choosing simpler models — like those with only one hidden layer or less than 10% dropout (randomly removing units from the input or output layer) or by testing various settings and finding the right balance between accuracy and simplicity.
Another option is designing your own architecture and then optimizing each parameter separately before combining them all together. Doing so gives more control over how much information the model uses and keeps you in charge of deciding where to place importance in the function.
Combine neural networks with deep learning
When it comes down to it, overfitting is just when models are too good at predicting their own data. Because these models have trained themselves on all of the patterns in your dataset, they become very difficult to train elsewhere!
This can be limiting because not every model will work for every task. By incorporating what’s called transfer learning, you can reduce overengineering by taking a base or pre-trained network and tweaking it slightly to fit your needs more effectively.
By starting with an already well-tested framework, you can quickly improve your skills without wasting time re-creating the wheel.
Use transfer learning
In deep learning, over-fitting is very common. This happens when your model learns easy datasets really well and then fails to generalize onto new data or tasks.
In fact, it’s quite likely that you will run into this problem at some stage!
When it comes down to it, over-fit can be fun to watch. Every now and then someone creates an overly complex model that seems to work almost every time.
This kind of model often times wins awards for being the best performing one, but unfortunately it doesn’t actually work too well in practice.
Transferring knowledge from one context to another allows you to avoid this risk by using what’s known as ‘transfer learning.’ A good example of this would be if you wanted to predict whether something is edible or not. You could use computer vision to find patterns in fruits and vegetables and apply those to predicting whether an unknown piece of food is safe to eat, or not.
That way you don’t have to start with the more complicated task of classifying foods according to their nutritional value, etc., you can focus only on determining whether they look like they might taste okay first.
Here are 5 tips for how to reduce overfitting in neural networks.
Tip No 1: Avoid naive strategies like trying to increase depth or width
Deep nets were popular back in 2014–2015, and people experimented a lot with them.