When training neural networks, or deep learning models, you can run into issues known as over-fit. This happens when your model is performing very well on the datasets it was trained with, but cannot apply this knowledge to new data.
Overfitted models will often perform extremely poorly on unseen test sets!
To avoid this, you must add more layers or nodes to the network, reduce the complexity of the problem, use less powerful optimizers like Adagrad instead of Adam, and/or increase the size of the dataset being used for testing.
In this article, we’ll be looking at some strategies to help prevent overfitting in CNNs.
Become an expert at validation
One of the most important things you can do to prevent over-optimization is to learn how to validate your model properly. This not only helps mitigate overfitting, but also gives you some insight into whether or not your model works well.
Validation is simply testing your model by using different examples to determine if it functions as expected. For instance, let’s say you are trying to predict if something will succeed based on its price. You could use the test set to determine accuracy by comparing the prices against the predictions.
Alternatively, you could choose to compare the average cost of items that sold versus those that did not sell. By doing this, you would be able to tell whether or not people were willing to spend more money on products that succeeded, which may give you some clues about what types of goods successful sellers market.
This kind of evaluation isn’t just done for predictive models either. It can be applied to explanatory ones as well. For example, you might want to evaluate how good your model is at explaining why an item failed.
When it comes to deep learning, overfitting is very common. This term refers to when your model performs well on your training set but does not perform as well on new data or tests of the model’s generalization ability.
When you train a neural network, such as an image classifier, it will learn internal representations that are good at predicting for the examples it was trained on. However, this may not be the best representation for other patterns and concepts it encounters later on.
Since most people do not have large amounts of labeled data, there is usually an emphasis on having a robust model that can achieve high accuracy on the training dataset.
However, these more accurate models often fail to improve upon their performance when tested on unseen datasets or environments. In fact, they sometimes even perform worse than simpler models!
Cross validation is one way to avoid overfit by testing how well your model works on external sets. By doing this several times, you can average out any trends in the data and determine whether your model is improving or degrading in its performance.
Use a holdout set
A very common technique in machine learning is called over- or underfitting. When you fit your model too tightly, you risk not generalizing well because you’ve trained it only using part of the data.
On the other hand, when you use too large an input space, you can lose accuracy due to overfit. This happens when the algorithm gets “trapped” in local minima where it learns patterns that are specific to the training dataset.
By adding additional test sets at some later time, we can prevent overfitting. If our model already has good performance on one test set, then we don’t need to worry about overfitting!
However, this approach may be unusable if you want to compare results across different models since each group will have its own test set. For example, let’s say there were two groups who both had perfect scores on their first test set but one was much more accurate than the other on the second.
You would probably not trust the better predictor because it overfitted on the first test set. The worse one would also suffer from underfitting, however, so it wouldn’t predict as accurately on new examples.
Use a different training dataset
When using deep learning, overfitting can occur when your model is too good at predicting things for the given set of data.
A common way to prevent this is to use a larger test set. If you have a neural network that is not working, try changing the input values to see if it will work better or worse.
By having a bigger test set, your model will be more generalizeable. You could also shuffle or change the order of the test set to make the results even harder to predict!
Another solution to avoid overfitting is to use an early stopping method. This stops the training process when the accuracy no longer increases or decreases.
One of the most important things for your model to learn is when it should stop looking at features that do not contribute to predicting the target variable and dropping out, or limiting how much weight those features have in determining your outcome.
When you are creating your deep learning network, there is an optimal balance between using too many parameters (which can overfit the data) and not having enough parameters (predict nothing more than chance guesses).
Regularization is one of the key strategies used to find this balance. There are three main types of regularization in neural networks: batch normalization, dropout, and L2-regularization, which we will discuss further down!
Description: Batch normalization modifies the input layer’s statistics so that they get normalized across different training sets. This helps prevent overfitting by ensuring all layers of the net use similar feature extractors.
Example: Let’s say you wanted to predict whether someone would go into debt within one year due to financial problems. You could create a network with two fully connected hidden layers and an output layer as per usual.
But what if some of the features indicate people who go into debt quickly are likely to also go into debt later? A lot of times, companies recruit managers based off of past behaviors, so these traits might be seen before being promoted.
Practice and get expert at debugging
A deep learning algorithm that performs poorly is often said to be suffering from overtraining. This term refers to when the model becomes too dependent on the data it was trained with.
When this happens, the algorithm will perform very well on its initial training set, but eventually it will fail as it cannot extrapolate how to apply its learned skills outside of the bounds of the original dataset.
Extrapolation means taking what has been learned so far and applying it to new situations or materials. In other words, it is predicting things for people or documents you have never seen before!
Because these models seem to work magically most of the time, users may forget they are not generalizing properly.
Debugging such a system is therefore crucial to ensure it does not continue to believe it knows everything even after being shown evidence otherwise. It must learn to do this if it wants to achieve true mastery.
Use confidence intervals
A common way to prevent over-optimistic predictions is using what’s called a confidence interval. This sets a range of values within which you are certain that your model will perform well, helping mitigate overestimation.
A confidence interval can be applied at either an individual layer or whole network level. When applying it at a network level, you would apply the same formula across all layers.
When calculating confidence intervals, there is an assumption made about how good or bad the current set of data is. Because we have more datasets than models, we use this difference between the best and worst cases as a measure of how good our model is!
This means if your model performs poorly on most examples in the dataset, then assuming it will always do so is not ideal. To fix this, we must calculate whether the average performance of the model is better or worse than the norm.
If it’s better, then we can assume our model will work just as well in future, but if it’s lower we may want to try improving the underlying components of the architecture.
Use hypothesis tests
A common way to prevent over-fit is by using regularization, which means limiting how much parameters of your model can change. This helps ensure that your machine learning algorithm does not get stuck in local optima or modes where it always predicts the same thing because it has limited ability to modify those parameters.
One type of regularization is called “lasso” regression. In lasso regression, an additional term is included in the cost function to weigh down the coefficients of the polynomial functions (or linear parts) of your model. This forces some coefficients to be zero, effectively removing this feature from your model.
When performing lasso regression with Python’s scikit-learn library, there is an option for alpha_value, which sets the strength of the sparsity penalty. You can test different values of alpha and see whether that improves your accuracy or decreases accuracy by adding unnecessary complexity to the model.
You can also use a method known as cross validation to determine the best value for alpha.