Recent developments in deep learning have led to **incredible success across many industries**, but there is a cost associated with this success. When companies use these applications more frequently, they *need bigger computer clusters* that *require additional electricity* to power them.

As powerful as AI can be, it also requires a large amount of processing power which takes up energy. Companies are increasingly investing in AI because it helps their business run more efficiently, but those investments add up over time!

If you work for such a company, you should know how to reduce loss in deep learning so that your organization will get the most out of its **machine learning software**.

Here are some ways to do that. You can learn one or all of these tips, depending on what types of losses you’re trying to avoid and what level of efficiency you want to achieve.

## Choose your loss function based on the nature of your problem

When determining how to reduce loss in deep learning, **one must first determine** what type of problem they are trying to solve. There are two main types of problems that *people use neural networks* for: regression problems and classification problems.

In a regression problem, you want to find an output (in this case, a number) that is close to the actual value of the dependent variable.

For example, let’s say you wanted to predict someone’s weight based off their height. Your model would calculate an average weight for every person who was within 1% of their height. The model with have the lowest average weight would be the most likely winner!

A lot of things can be framed as a *regression problem including predicting revenue* or sales volumes, optimizing battery life, etc. So, knowing how to reduce loss in regression models is very important.

In a **binary prediction task like image classification**, we want our algorithm to identify whether an input picture contains an object or not. This way, it can determine if the picture contains nothing, a dog, a cat, or even both.

This article will focus exclusively on ways to reduce loss during training time in classification tasks only. That means there will no depth discussed other than adding more parameters into the network and regularization.

## Calculate and graph Brier loss

The other **major cost component** of **deep learning models** is their Brier score or loss, which calculates how poorly each *model predicts individual outcomes*.

The lower the brier score of an individual prediction, the better the predictive power of that model! This makes sense because it takes into account both very accurate (hence the higher score) as well as inaccurate predictions.

By adding up these additional scores across all individuals, we get a total loss value for the model. The lower this total, the more accurately the model predicts overall.

With that said, there are *two main types* of losses used in neural networks- cross entropy and categorical probability. Both have separate formulas, but they work off of similar metrics.

## Calculate and graph dice loss

Dice loss is an interesting metric that was originally designed for use as a binary classifier where it would determine how many chances each class has by calculating the ratio of its area under the curve (AUC) over the AUC of one category alone.

In other words, if you have two classes with the same total amount of probability mass, the one with more areas under the curve will win!

Dice losses were adapted slightly so that they can be used for multiclass classification instead of only 2-class cases. Instead of having just **1 ratio per class like** before, now there are k ratios depending on how *many different categories exist*.

The *k values must add* up to 1, and the final value is the average of all of them. One way to think about this is that every category gets a chance to win half the time which makes sense since some cannot possibly win. The overall goal is to **make sure every possible winner gets enough opportunities** to occur during training.

Another cool thing about dice loss is that it can easily be calculated at test time using the formula mentioned above. Just remember to divide by k instead of averaging because we want individual terms to contribute independently.

## Calculate and graph cross entropy loss

One of the most important losses for deep learning is the cross-entropy (CE) or classification loss. This loss calculates how likely it was that an example would be predicted as **one label versus another**.

For instance, if your model learned to predict every picture of dog as being cute then it would have poor CE because everything is labeled as cute!

By adding in more difficult examples such as pictures of dogs with ugly faces, the CE becomes better able to distinguish what kind of animal it is. Having a lower CE means the model will try to classify things without too much differentiation between them.

You can think of this like trying to determine whether something is apple pie or chocolate chip cookies by looking at their shapes only. A **sharper eye could tell** you but having harder samples makes it easier to identify which one it is!

With CE, the system does not learn enough about the types of animals so they do not get differentiated correctly. By reducing these losses, the models are able to focus less on *distinguishing small differences instead* of knowing what a cow, horse, and sheep look like!

There are many ways to reduce CE in neural networks depending on the task the network is set up to perform. Here we will discuss some strategies for redoing image recognition tasks using CNNs.

## Calculate and graph logistic loss

One of the most important tasks when it comes to deep learning is reducing loss. You will almost always have more losses than gains, which can make your *model completely lose motivation* to improve!

If you look at the diagram for binary classification (seen here) then you get the sense that even if one class was way better than the other, the *algorithm would never actually tell* which was which because they are both equally likely.

This **makes total sense** as we know that there is no perfect pattern or strategy to determine what category an image belongs to, so why should there be for *deciding whether something* is good or bad?

That being said, there are ways to reduce this loss by changing how well each class performs in relation to the others. This article will go into detail about some strategies.

## Calculate and graph hinge loss

One of the most important components in deep learning is loss. A loss function determines how well your model trained with given data!

A very common loss used in neural networks is called cross-entropy or binary cross entropy. This loss calculates the difference between what you want your model to predict, and what it actually predicts, then weights this difference by a *factor dependent upon whether* the prediction is more likely than not correct.

The first term in the equation is referred to as the classification error while the second term is referred to as the regression error. The ratio of these *two terms depends* on the complexity of the problem being solved; the harder the task, the higher the ratio.

In practice, people often ignore the regression part because it is small compared to the other term. However, we can use this information to our advantage and determine if our models are overfitting!

By looking at the ratio of the regression term to the whole thing, we get an indication of how much weight the model gives to **accuracy versus fitting new features**. If the ratio is large, then the *model puts less importance* on having accurate predictions, making it possible to have overfit.

If however, the ratio is smaller, then the model will suffer from underfitting. In that case, it may be trying so hard to fit every detail that it misses key parts of the picture.

## Calculate and graph exponential loss

A very common beginner mistake is trying to reduce the loss by simply changing the values of one or more of the variables that make up the loss function. This can sometimes work, but only if there are no serious changes being made to the model!

By adding new layers, for example, then reducing the number of neurons in each layer, it is possible to get a better result than before. However, this may not be what you want!

If you randomly add or remove features from your neural network, your model will likely fail to converge or even worse, **produce completely wrong results**.

One important reason why this is bad is because your **feature importance may change drastically depending** on how well the model works. For instance, if your model does not perform well, then probably removing some features helped!

Instead of actively deleting or adding features, however, we can *instead try finding ways* to lower the loss using calculus. Doing so allows us to find critical points where altering the input would have an effect on the output, and determine whether lowering the **input would help increase** the output or decrease it!

This article will go into detail about different types of losses, as well as strategies for calculating and graphing them.

## Make your loss function more robust

In deep learning, as with any area of machine learning, you will run into overfitting. Overfitting occurs when **models learn simple tasks** very well, but cannot apply that knowledge to new situations.

For example, if your model is trained to recognize all instances of “cat” then it will probably not work at testing time because there is no longer such a term!

Similarly, if your model has been programmed to identify everything as red then it would be difficult to distinguish real from false positives (saying something is always red) so it would never know when an object is actually red.

In both cases, the model does not generalize properly – it knows too much information about the data it was given. You can easily get rid of excessive fitting by incorporating some kind of regularization into the training process.

Regularization is a way to restrict how much a *neural network learns*. There are many types of regularization, but one of the most common is called dropout. Dropouts occur during training when certain neurons are set to 0 or 100 percent inactive for random periods of time.

This makes sense because if we have a neuron which responds only to a small number of features, then we do not want it to **keep firing every feature independently**. Neurons like this are said to be overfit.

By adding dropout, the algorithm gets rid of these overly specific neurons and replaces them with a less-specialized one.