When training neural networks, or deep learning systems, how you configure your network can have an enormous effect on how well it functions. If your system over-relies on overfitted features, then it will not work when there are no longer enough of those features to function correctly.
A feature is something like a pattern that the neurons use to identify objects. For instance, if I asked you to identify all fruits starting with the word “apple”, then “apple” would be a feature for me to add to my dictionary.
By using this feature as part of a larger identification process, I could determine what kind of fruit someone was identifying. So now, knowing that “apple” is an apple, I could determine that it is probably due to seasonality or flavor.
Deep learning algorithms do the same thing except they try to apply these features automatically!
If a trained algorithm does not have the right number of features, then it may not work properly. This risk becomes even higher as we increase depth, or the number of layers in the network. As the net gets deeper, it can become harder to tell where individual features begin and end.
Luckily, there are some easy ways to avoid overfit issues in deep learning. In this article, we will go through several tips to help you achieve this.
Causes of overfitting
A common cause of overfit is assuming too much about the data. You can assume that because there are more instances of class 1 that every instance in your dataset belongs to class 1. Or you can assume that since there were several examples of class 2, then all instances must also belong to class 2.
By doing this, you’re giving your model very specific rules for what classes it should recognize, which may or may not be correct depending on how many samples of each class you have.
If you do have enough examples of one category, then your model will probably work well if you use hard-coded categories instead of using an algorithm to determine them.
However, if you don’t have as many examples of the other category, then your model will likely fail by making wrong assumptions. In situations like these, deep learning algorithms become less powerful than they could be.
Overfitted models sometimes perform better than non-overfitted ones when there are no major changes to the input data, but they won’t generalize well to new inputs.
Examples of overfitting
A lot of people get stuck on just using the “rules” for avoiding overfitng as mentioned earlier, but they fail to apply those rules properly.
A big example of this is when people use too many parameters (which can lead to overfitting) but then they pick a very small dataset to test their model on!
By picking a very limited data set, their model will almost certainly under fit that data, and therefore not work at all!
This is actually much more common than people think, and even worse, some people choose very large datasets to test on so their models perform poorly because it has already learned the way that dataset works!
We have discussed before what happens when your model learns only one type of pattern or shape, and now you know how to avoid having that happen with excessive parameterization!
So, make sure to mix up where you place neural networks into your architecture and don’t be afraid to use simpler architectures if you feel like your current one is doing fine.
Solutions to overfitting
One of the most important things for any model to learn is how to test its internal representation. This process, called generalization, happens when the model learns simple patterns or features that apply to many examples.
However, too much generalization can sometimes cause your model to lose specificity, which means it won’t be able to recognize what you have trained it to find.
Generalizing too much is especially common in deep learning models because they use lots of layers and neurons to explore different concepts.
In this article, we will discuss some strategies to mitigate overfitting in neural networks. You will also learn about three basic types of overfit and why they occur.
Use visualization tools
A common beginner mistake is over-reliance of statistical models or software packages for internalizing the underlying structure of your data. Using too many such tools can sometimes lead to an overly complex model that does not improve as it should due to overfit.
In deep learning, overfitting often occurs when there are too many layers or features in the network. By including more parameters than necessary, the algorithm will learn unnecessary details so much that it no longer applies general rules to new examples.
By using external visual aids, you can help prevent this by ensuring your neural networks only apply learned skills that hold up under test cases outside of the trained set.
There are several free and paid applications with graphical interfaces that facilitate this process. Some well-known ones include:
* DataViz – An interface which allows users to drag and drop attributes to create nodes and edges in their graph schema. You then have the option to edit these settings or start from scratch.
This app also has some additional features like creating node groups and clustering modes. It’s easy to use and very interactive.
Data viz is great because you don’t need any programming knowledge to do anything beyond just dragging and dropping. Even if you know nothing about graphs, you can easily pick up how to use it.
* UGraph – This one doesn’t require you to make your own graphs either; you can instead upload yours or create new ones.
Reduce the complexity of your model
A common way to achieve overfit is by adding too many layers or features to your neural network. Technically, this is not wrong, but it can hurt your performance and make your net learn simpler things instead of more complex ones.
By limiting how much you add to your networks, you reduce their chance of overfitting. There are several ways to do this, and depending on what kind of problem you’re trying to solve, one may be better than others.
In this article we will discuss three methods for reducing internal layer depth, number of nodes per layer, and total layers used to prevent overfitting.
Use a validation set
A common way to avoid over-fit is using a validation dataset or test set. This allows you to validate how well your model is learning by testing it on another set.
By having a separate test set, you can determine whether your model is improving as you add more data or not!
If it is, then that means your current training settings are working effectively and you should keep them! But if the accuracy stays constant (or even decreases) while adding data, then you may need to make some changes to your model.
For example, when you have a very large number of features, your model may be relying too much on individual features instead of identifying patterns between groups of features.
You can also use the test set to assess whether your feature transformations work effectively. For instance, if you choose to apply an additional step to reduce noise before applying a classifier, you could check whether that reduced the performance significantly.
Try different hyperparameters
When it comes to optimizing neural networks, one of the most important things is defining your network architecture. This process is known as architectural design!
When it comes down to it, there are two main types of architectures that you can use for deep learning. One is called “convolutional” and the other is called “recurrent.”
The difference between these two types of architectures is how information flows through the model. In convolutional models, every layer learns about its directly adjacent layers. For example, if the next layer has 1/8th of a layer thick square channel (feature) filters, then those channels get multiplied by the layer before and summed together.
In recurrent or sequence-to-sequence models, one layer gets input from the previous layer and uses that data to generate the next set of outputs. For instance, let’s say we have an article written by someone else and we want the machine to write a new article based off this first one. The way this would work is the system would take each word and create an array of words and numbers representing how many times each word was said in the given context. It would do the same with numbers and when they were put together, they formed another array which represented the whole sentence!
That being said, here at Smart Analytics, we prefer using residual networks over normal CNNs. What are residual networks? Well, think back to our initial analogy.
Use a transfer learning approach
In deep learning, over-fit is very common when your model learns only specific tasks directly from examples with little generalization or extrapolation. When this happens, your model will learn bad behaviors that do not apply to new situations.
Deep neural networks are complex mathematical functions that rely on lots of parameters (weights) to achieve their goal. These weights must be tuned so that the network performs well on the task for which they were trained.
When you initially train a DNN, it may work well because it has enough data to accurately identify its categories. But as we mentioned before, when there are too few examples, the model can’t differentiate between them easily, and therefore it needs more instances to do so.
That’s where overfitting comes into play. Since it takes longer to acquire the knowledge needed to perform the task, most models will try to get through as many steps towards achieving that goal as possible during training.
However, once again, there aren’t always enough instances to evaluate how well the model works, making it difficult to assess whether the model worked properly.
In order to mitigate this, researchers have found that transferring the knowledge gained while solving one problem to solve others is an effective way to prevent overfitting.
