Finding efficient ways to train your neural network can save you significant time. While there are some quick tricks like using online-only training or reducing image resolution, this article will go into more detail about how to reduce the overall duration of training by looking at when certain parts of the model are being trained.

There are two main reasons that most models take so long to train: either the layers need lots of training data or the hyperparameters (the settings for each layer) need to be tuned properly.

This article will focus only on the second one since they do not require any special software or expertise! By identifying which layers of the model use very little training data and then just updating them with less data, you can cut down the total amount of training needed significantly.

This article is also going to assume that you have already done the hard work of loading all the necessary packages into your computer and setting up the environment correctly.

Reduce the number of training samples

how to reduce training time in deep learning

One of the biggest factors in creating faster deep learning models is reducing the amount of data needed during training. If you have too many examples, the network will not be able to learn because it does not get enough information from each example.

One common way to do this is to use over-sampling techniques. Over-sampling means taking some parts of the dataset and putting them into your train set while leaving some out! This makes your net think that there are more instances of certain types of objects than there really are, helping it learn how to recognize those classes better.

There are several ways to perform over-sampling, such as random under-sample or using k-means clustering to find empty space and inserting examples from one class where there is already an instance of that class.

Use data augmentation

how to reduce training time in deep learning

Data augmentation is an integral part of any deep learning experiment. It helps ensure your model does not get overwhelmed by overfitting on the training dataset.

Data augmentation works by introducing random changes into your images or videos. These can be things like horizontal flips, vertical flips, translations, rotations, and so on.

By adding these distortions, your neural network will have to learn how to correct them later when it comes time to test its accuracy. Because there are so many possibilities, the number of examples in your training set will increase dramatically!

This article has a lot more information about some specific ways to add data augmentation to your image classification experiments. Make sure to check that out before diving deeper. But for our purposes here, we’ll simply assume you’ve done that already and now you’re ready to reduce your train-test loop time!

Reduce your train-test cycle time via batch size

Batch sizes refer to the amount of computing power given per epoch (the complete run of a machine learning algorithm). A larger batch size means more computational work gets completed in one go instead of being broken up across multiple GPU cards and CPUs.

That’s great if you’ve got lots of GPUs and/or CPU cores to devote to AI, but not necessarily practical if you don’t.

Try using a different learning rate

how to reduce training time in deep learning

A good way to reduce training time is by trying out various settings of your neural network’s learning rate.

Most of us use default values for these that work well, but you can always try experimenting with other ones to see if they perform better!

By reducing the amount of time it takes to get a result, this also gives you more opportunities to train longer due to less wasted time.

You should be careful though as changing the learning rate too quickly could cause your model to not converge or even crash!

It’s best to do a small test run with the current setting first, then gradually lower the value until you find one that works well. Then, keep that value for the rest of the training!

There are several ways to do this. You can either go down linearly, or use an exponential decay.

With linear decays, we start at our initial value and decrease fractionally per batch (each set of input-output examples used in training). This method usually requires a larger starting value than the others because it takes a little bit to get going.

Exponential decays work similarly to rising exponentials. We begin very slowly and speed up over time instead of staying constant like normal decays.

Reduce the impact of random effects

how to reduce training time in deep learning

A common beginner mistake is to run into issues when trying to reduce training time is investing too much effort in reducing unnecessary parameters or lowering learning rates.

A parameter is anything that shapes how your neural network learns, such as the number of layers or the amount of dropout you apply before each layer. The more settings you have, the longer it takes for your neural net to get “good” at its task!

However, having too many settings can actually hurt performance instead of helping it- this is called overfitting.

Use gradient descent

how to reduce training time in deep learning

Recent developments in deep learning have focused on optimizing neural networks using an algorithm called gradient descent. Gradient descent is an optimization technique that works by taking small steps towards your goal, then evaluating how much you should change each parameter of the network.

The most common way to use gradient descend is through stochastic (randomized) optimization. Rather than picking one step size for every parameter, it picks several and changes them all at once. This allows the method to find local optima more quickly, as well as finding better ones later because the system has chance to “explore” different areas.

Stochastic optimization can also be done with momentum or adaptive stepsizes, where the stepsize is adjusted over time depending on whether the model is improving or deteriorating. Both of these strategies require additional hyperparameters that must be tuned, but are less likely to get stuck in poor local minima due to their randomness.

Focus on learning key concepts

how to reduce training time in deep learning

There is no need to focus too much on becoming an expert in every aspect of deep learning. This is not possible or practical, even if you spend your whole life training!

Instead, choose few areas that are well-studied and learn those inside out. For example, there are many good books available about how to use neural networks for image recognition. You can probably find one book that teaches you all of the basics very quickly!

By focusing only on the essentials, you will save time. And once you have learned these fundamentals, you can move onto new things more efficiently!

There are also several free resources online that contain knowledge that could help you hone your skills. By taking advantage of these, you do not need to invest large sums of money in education either.

Furthermore, some people may be able to help you develop your skills further by talking to you. A lot of universities now offer students groups so that they can chat with each other and get help from peers.

Break down complex tasks into smaller pieces

how to reduce training time in deep learning

A growing trend in deep learning is breaking down very large, computationally intensive projects into small, modular chunks that can be tackled separately.

This approach has two benefits. First, it gives you more freedom when it comes to choosing your training strategies. For example, you may not need to use all of the modules every time you train, so you can save money by using them in batch-mode or online settings.

Second, this way of working makes it much easier to test different components out. You no longer have to run the whole algorithm because one module failed!

There are many tools and techniques for achieving this goal. In fact, some people even build entire networks this way! Here we will discuss five ways to break down larger projects.

Disclaimer: The contents of this article should be used with caution as they may not be appropriate for individuals who are new to neural network studying. Moreover, these tips cannot guarantee you will achieve the same results as experienced practitioners, nor do I imply that these methods are the best for every situation.

Everyone learns at their own pace, and there is no wrong way to learn anything.

Use quick feedback to identify weaknesses

how to reduce training time in deep learning

A common way to test your neural network is by putting it through what’s called a validation set. This is an internal representation of the model that you can use to determine how well it works.

You would take some samples that have not been seen before by the model, run them through the network, and then compare the results with known labels or answers.

If the result matches the expected outcome almost all of the time, then great! The model seems to be working. But if there are sometimes incorrect predictions, then you need to fix the parts of the model that may be overfitting the training data.

One easy way to do this is via early stopping. You can add a cost to each epoch (each round of the algorithm) where you decrease the batch size or increase the number of iterations. Or you can just pick a minimum amount of iterations for each epoch as a cost.

This way, the model will stop when it reaches a certain accuracy on its own validation set which helps reduce overengineering. It also gives you more chance to observe whether the model is improving or not because you don’t necessarily have to train until you reach a specific goal.

Caroline Shaw is a blogger and social media manager. She enjoys blogging about current events, lifehacks, and her experiences as a millennial working in New York.