When it comes down to it, deep learning is all about optimizing hyperparameters. These are the settings that you can tweak to achieve better results with your neural network model.
There are three main types of hyperparameter in deep learning: activation functions, regularization factors, and layer type and depths. Changing any one of these will have different effects on the performance of your model, so it’s important to know what they mean before picking values for them.
This article will go into more detail on each of these types of hyperparameters and how to optimize them using gradient descent! If you’re already familiar with gradient descent then you can move onto the next section.
If not, no worries – we’ll start from the basics here too. We’ll also talk about why optimization is an essential part of machine learning and how to do it properly. Hopefully after reading this you’ll be able to perform hyperparameter tuning yourself!
Continue reading below to learn more about each type of hyperparameter and how to optimize them.
Calculate the gradient of your loss function with respect to your hyperparameters
When optimizing neural networks, one important thing is calculating the gradients of your loss functions with respect to your model’s hyper-parameters.
The most common type of loss in deep learning are either binary or categorical cross entropy losses. With these types of losses, you will have two or more possible outcomes per example, respectively.
When backpropagating through such losses, it can be tricky to know what changes need to be made to improve the net performance since there may not be an easy way to determine which outcome was better than another!
This article will go into detail about how to calculate these gradients for both cases.
Use a gradient-based optimizer
When optimizing hyperparameters, most of them depend on the optimizer you use. Different optimizers bring different strengths, but almost every one has a cost parameter that tells you how expensive the optimization is going to be.
The more expensive an optimizer is, the longer it will take to train your model. The average cost per epoch varies between algorithms, so choose the one with the least amount of time!
Common optimizers include stochastic gradient descent (SGD), batch gradient decent (BGC) with momentum, adaptive moment estimation (AdaGrad), AdaDelta, Adam, and others. All of these have their pros and cons, and some are considered better than others.
A good rule of thumb is to try out several for yourself before choosing which one is best for your tasks. You can also check out our article about the basics of deep learning to learn more about optimizers.
Combine optimizers
There are two main categories of optimization strategies in deep learning, namely model-based and algorithm-specific. Model-based optimizations focus on improving the performance of your neural network by changing how you define the loss function or what kind of architecture you have.
Algorithm-specific optimizations look for speed improvements through parallelization or looking into whether it is worth investing time in GPU or CPU training.
Optimizer types include things like stochastic gradient descent (SGD), momentum SGD, Adagrad, Adam, etc. All of these optimize different parts of the cost functional of the neural net.
There are some general rules when optimizing hyper parameters in models such as dropout, regularization terms, batch size, number of epochs, and so forth. However, there isn’t one clear winner across all problems and tasks.
That’s why trying out several combinations is the best approach! You can test each parameter at a time while keeping the others constant, increase them, decrease them, and see which ones improve results the most.
Use data-driven methods
Having good hyperparameter settings is like having perfect eyesight – it depends on your vision of what you want the model to do.
There are many ways to pick your hyperparameters, but most depend on using datasets that have been properly labeled or structured for use with machine learning algorithms.
By doing this, we can determine which parameters work best for different models. Some examples of parameters include how much memory a computer has, how large batch sizes work, and whether dropout is needed or not.
Data driven optimization is very common in AI because it works! Most people these days learn about the importance of such strategies by exploring them themselves.
In this article, I will go over some tools to help you optimize your deep neural networks (DNNs)’ hyperparameters. These tools range from free to paid, so there is something for everyone.
You may also be familiar with some of these tools, but I will give my thoughts on why they are important and how to apply them in this setting.
Perform parameter tuning
One of the most important aspects of deep learning is optimizing hyperparameters. These are the settings that you can tweak to achieve better results, depending on what you are trying to train.
For example, when training image classification models like those mentioned above, there are several parameters such as batch size or number of epochs that can have significant impacts on performance.
By experimenting with different values, you will be able to find optimal settings for your model!
There are many free online tools that can help you do this. For instance, Google’s AI Platform has an optimization tool called AutoML which does all the hard work for you. You just need to upload your trained model and input examples and it automatically finds the best settings for you.
Another very popular way to do this is using the Nearest Neighbor algorithm. This looks at how well its neighboring settings worked and picks the one that performed the best in terms of accuracy.
This article will talk more about these types of optimizations in detail.
Know the differences between training and testing
When you are optimizing your network, you will come across terms like batch size, learning rate, momentum, and weight decay. All of these apply only during hyper-training!
After your model is trained successfully, it can be tested against one or more datasets in order to determine its accuracy. This process is referred to as validation or test time. During this stage, none of the aforementioned settings matter!
Simply changing any of the above mentioned parameters changes how well your model performs at test time! That’s why it is important to know the difference between training and testing.
When it comes down to it, all of the settings we talked about before (batch size, momentum, etc.) do not affect the performance of our models when they are being validated or tested! Only while training does every setting have an effect.
Sharpen your network
A neural network is made of several layers that are connected to each other. The number, type and order of these layers determine how it computes information. When optimizing hyperparameters for performance, you will typically try out different layer shapes, numbers of neurons or even whole networks!
There are many ways to perform this sharpening action. You can use either basic pre-trained models like VGG or ResNets, transfer learning by fine tuning an already trained model or training from scratch using one of the many available free software packages.
By experimenting with various architectures, you can find ones that work best in your problem domain. Besides improving overall accuracy, some configurations may be more efficient than others in terms of speed or memory usage!
General tips when trying new deep learning strategies
When starting off with any kind of machine learning technique, there are some general guidelines that can help make progress. These include things such as choosing good optimization methods and regularization techniques, working in parallel instead of sequentially, and knowing when to stop training.
All of these apply not only to optimising hyperparameters but also to early stages of model building where researchers must test limit settings and boundaries of their algorithms before moving onto bigger projects.
Use transfer learning
Transfer learning is an incredible tool for supervised learning. This technique works by taking a pre-trained network (that has been trained on similar data) and using it as a starting point to build your new model.
A common example of this is when you are trying to predict if something is positive or negative sentiment on social media. You can use what someone else built and tweak it slightly to get better results, or even completely change the predictions!
With deep learning, however, it’s easy to run into problems when there isn’t enough training data. That’s why using transfer learning becomes important – the networks that other researchers have developed often include hyperparameter settings like number of layers or batch size.
