Guidelines for Choosing an Optimizer and Loss Functions When Training Neural Networks Guidelines for Choosing an Optimizer and Loss Functions When Training Neural Networks
There’s no one right way to train a neural network. These models serve various functions with multiple data sets, so what... Guidelines for Choosing an Optimizer and Loss Functions When Training Neural Networks

There’s no one right way to train a neural network. These models serve various functions with multiple data sets, so what produces a high-performing model in one instance may not in another. As a result, effective training relies on a series of tools and strategies.

Two of the most important of these considerations are optimizers and loss functions. Without the right optimizer or an appropriate loss function, a neural network won’t likely produce ideal results.

Why Choosing an Optimizer and Loss Functions Matters

Optimizers generally fall into two main categories, with each one including multiple options. They take a different approach to minimize a neural network’s cost function, producing various results. They also fluctuate in speed and complexity, affecting training time and resources.

Loss functions present a similar issue. They calculate the distance between output and target variables, which guides how the neural network learns. Consequently, using the wrong one could make your network limit how effective your optimizer is.

As organizations rely more on machine learning, these potential inaccuracies, costs, and time requirements become more concerning. Analytics demand is outgrowing supply by 50%- 60%, so these algorithms must function as intended to avoid costly mistakes.

Here’s how you can choose the right optimizer and loss functions to make the most of your neural network.


Consider Your Data Set

One of the first considerations to work through is your training data set. An optimizer that does well with one type of data set may not be sufficient for another. Some of the most important factors in this area to keep in mind are your information’s size and how varied it is.

Some data sets may be too large or complex for simpler optimization algorithms. For example, since batch gradient descent computes parameters for the entire training set simultaneously, it’s not ideal for ones that are larger and more complex. You’ll need a more nuanced algorithm if you don’t want to push your training time into extremes.

The same concept applies to loss functions, too. Mean squared error (MSE) works for most regression problems, but it may punish mistakes too heavily if your target outputs have a large value spread. Mean squared logarithmic error (MSLE) may be more appropriate if you’re dealing with large, unscaled quantities.

Match Loss Functions to Output Units

Remember to match loss function to your output unit when choosing one. Different processes are better at certain tasks, and you can determine which type you’ll need by looking at your output unit.

For example, if you’re training an algorithm to classify something as one of two options, your output units will be binary. Cross-entropy is the default loss function for binary classification problems, so you should start there. Alternatively, if you’re trying to predict real-world quantities, you’ll want something that produces one node with linear activation like MSE.

Don’t Overlook Ease of Interpretation

One guideline that’s easy to overlook is producing results that are easy to interpret. Even if various loss functions and optimizers deliver comparable results, they may present them in different values. How easy they are to understand in a real-world context determines how helpful a neural network is.

Remember that being understood is just as important as being statistically accurate. Things like logarithmic loss can be difficult to interpret without a machine learning background, making them of little value to project stakeholders. Understand your ultimate audience and their needs to determine what you should use to report your findings in an easy-to-understand way.

Go From Simple to Complex

Regardless of your other considerations, always start simple before moving to something more complex if necessary. This applies to both optimizers and loss functions. Complexity typically means more time, resources, and difficulty understanding, so aim for the simplest solution applicable.

Stochastic gradient descent (SGD) is the most widely used optimizer for a reason: It’s the simplest. If an easier solution produces reliable, appropriate results, why bother trying to manage a more complex one?

Determine the simplest possible option for your given situation, and start there. If you find it’s insufficient, move to the next easiest, slowly becoming more complex as necessary. This process will help avoid unnecessary time and computational power consumption.

The Right Optimizer and Loss Function Makes a Big Difference

Choosing the right optimizer and loss function is crucial for training a neural network. This may seem like an intimidating choice, but you can follow these steps to narrow down the best option in less time. You can then be sure to produce the best neural network you can.

April Miller

April Miller

April Miller is a staff writer at ReHack Magazine who specializes in AI, machine learning while writing on topics across the technology sphere. You can find her work on ReHack.com and by following ReHack's Twitter page.