Deep learning is a part of artificial intelligence has been growing for the last few decades. Like any new concept, it is important to iron out the details and questions before you take it to use in real-world applications. If you want to use deep learning, there are many frameworks that you are able to focus on. There are a few options that are popular and can help you get the work done and these include Keras, TensorFlow, and Pytorch.
All three of these are great platforms to rely on and it is important to understand how they work, what features they provide, and when you would want to use each one. Let’s get started!
What is Keras?
The first option for deep learning frameworks that you can choose is Keras. This is a neural network interface that was written to work with the Python coding language. This one is a great option if you would like to create some deep neural networks and you want to experiment with it quickly. It also works on top of other options like Theano, TensorFlow, and CNTK.
Keras is great because it focuses more on being user friendly for beginners, extensible, and modular as well. However, it is not able to handle any of the low-level computations that you may want to do. It will do this through another library that is known as Backend.
During 2017, Keras was adopted and then integrated into TensorFlow. Users are able to use it through this module as well and you can also take the time to operate and use this library separately and independently.
What is Pytorch?
The other option that you have is Pytorch. This is a newer option for deep learning that works with Torch. It was first developed by Facebook’s AI department but can be used like other choices. It is used for lots of processes, but it is best for natural language processing applications. Pytorch is a good option when you want something that is simple, easy to use, flexible, and provides some good computational graphs and efficient memory usage. It also feels native, which helps you to get the process done.
What is TensorFlow?
TensorFlow is the third option that you can work on. This is a deep learning framework that you can use, one that was released in 2015 and developed by Google. It is one of the most popular options for deep learning because it is easy to scale projects on, good for documentation, provides multiple abstraction, and support for a lot of platforms when you want to get things done.
To keep it simple, TensorFlow is a type of math library that developers can use for neural networks and works great for dataflow programming across a range of tasks. It can also offer a few levels for abstraction when it comes to building up and training your models.
Should I Choose Pytorch or TensorFlow?
Now that we have some of the basis of those three, we need to explore which one should be used on which types of projects. Pytorch is used for a lot of deep learning projects already, but it is seen as one of the least popular choices out of the three. This may change in the future. When you are doing a project that needs the capabilities of debugging, flexibility, and a short training process, Pytorch is the best option. It also works on all three of the major operating systems.
However, there are more situations where you will work on TensorFlow. Thanks to the fact that it has a great framework and lots of methods to help you train your data, it is one of the top tools for deep learning out there. It also helps provide better visualization, which will allow developers a chance to debug better while tracking the training process.
Developers will find that TensorFlow is able to beat Pytorch is better when it comes to deploying some of the trained models to production. Pytorch doesn’t have the framework for this so you will need to implement another program to get this done. PyTorch is better in working with data parallelism though, so if that is a feature that you need, this is the right library. TensorFlow can do this, but the developer will need to manually code and handle the optimization themselves.
Should I Choose Pytorch or Keras?
Developers also have a choice between Pytorch and Keras. Both of these are great options for those who are just getting introduced to deep learning and their various frameworks. Experienced researchers and many mathematicians will find that Pytorch is the best option. But for developers who want to work with a framework that lets them build up, train, and then evaluate their models quickly, then Keras is a better option.
Pytorch is a better option for debugging the program, but both have lots of popularity when it comes to their learning resources. Keras is able to provide many tutorials and reusable codes while Pytorch is better with active development and providing the community support that many developers need. Keras does work well with smaller datasets and when you need to do some rapid prototyping as well.
Should I Use TensorFlow or Keras?
And finally, we need to take a look at whether TensorFlow or Keras is a better option to rely on. This is kind of complicated because Tensorflow is an open-sourced platform with a library that can handle many different tasks for machine learning. However, Keras will need to run on top of TensorFlow in order to work. Both are going to be great when it comes to making your own training models, though Keras is seen as the best for user-friendliness because it relies on the Python coding language.
Many times researchers are going to turn to TensorFlow more when they work with large sets of data and object detection, especially when they need a high level of performance. TensorFlow is also able to work on Windows, MacOS, Linux, and Android so there are plenty of options.
It will depend on the type of project that you want to do with deep learning. However, since Keras is a wrapper that goes to TensorFlow’s framework, it is hard to do a good comparison on which one to use over the other.
All three of these frameworks are great options to use when it comes to deep learning and helping you to collect data, train it, and even test it to make sure that it will work the way that you want. Choosing the right one will often depend on the features you want, the type of functionality needed, and the type of project as well.