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Keras vs TensorFlow: Which One Should I Use?
Table of Contents
- Keras and Tensorflow: What Are They Used For?
- Keras: A Neural Network Library on TensorFlow, CNTK, and Theano
- TensorFlow: An Open-Source Library for Machine Learning
- Which is Better: Keras or TensorFlow?
- The Difference Between Keras and TensorFlow
- Tensorflow Keras Examples and Use Cases
- The Bottom Line
Keras and TensorFlow are both popular machine learning neural network technologies. Depending on whether you’re approaching neural networks from a data scientist’s perspective or a programmer’s perspective, you might be more familiar with one or the other.
While the technologies may seem similar, Keras is dependent on TensorFlow, while the inverse is not true. Today, we’re going to take a deeper look at when you might use Keras vs TensorFlow and when TensorFlow alone might be enough.
Keras and Tensorflow: What Are They Used For?
Keras and TensorFlow are both neural network machine learning systems. But while TensorFlow is an end-to-end open-source library for machine learning, Keras is an interface or layer of abstraction that operates on top of TensorFlow (or another open-source library backend).
When you use Keras, you’re really using the TensorFlow library. But you’re using the TensorFlow library in a way that’s more intuitive, visual, and modular. Keras makes it easier to use the TensorFlow library, but with some trade-offs. Specifically, you may not be able to access more complex (and confusing) low-level utilities.
Keras: A Neural Network Library on TensorFlow, CNTK, and Theano
Keras is a neural network library written in Python that builds on top of TensorFlow — as well as other backends such as CNTK and Theano. It can be customized and extended, is modular and composable, and has an easy-to-use interface that’s perfect for those who are looking to rapidly deploy. It also has extremely extensive documentation.
Advantages of Keras include:
- User-Friendly Interfaces: Those who don’t have a lot of experience in developing for either Keras or TensorFlow will encounter significantly less of an initial hurdle. If you want to get a model up and running quickly, this helps.
- Easy Custom Building Blocks: Keras provides a layer of abstraction over TensorFlow’s high-level APIs and features, although the tradeoff is that low-level APIs are no longer accessible.
- General Ease of Use: Keras improves the troubleshooting process by providing more feedback for developer actions, making it easier for the entire system to be used, beyond what the interface provides.
You would use Keras if you wanted a faster, easier way to deploy, build, and train neural network models, along with the power of TensorFlow. If you understand Python, using Keras is even easier.
Is Keras Part of Tensorflow?
Keras is a wrapper for the TensorFlow open-source library for machine learning, intentionally built to make the process of developing a neural network easier. But Keras isn’t specifically a part of TensorFlow because Keras can be used with other open-source libraries or even proprietary libraries, as long as it’s integrated with them. Consider Keras a layer of abstraction that can be used with multiple backends.
Is Keras Good for Research?
Keras is particularly great for research, especially for data scientists who understand Python or who don’t want to dig too deeply into programming. Keras makes the process of developing neural networks and models much easier, which in turn means those who aren’t specialized in programming and development can rapidly build, train, and deploy models.
Which Companies Use Keras?
Uber, Yelp, Netflix, Instacart, and Square are among the companies that use Keras. Whenever deep learning, machine learning, or neural networks are needed, Keras can be used. For services such as Netflix, Keras can be used to build suggestion engines. For services such as Square, Keras could be used to detect fraud or identify purchasing patterns.
Of course, it should be noted that if a company is using Keras, they likely use TensorFlow as well. While the company could potentially be using Keras with a different backend, this is rare.
What Are the Advantages of Using Keras?
Apart from its intuitive interface, Keras has an array of error messages and warnings that are designed to give developers and scientists deeper insights into why a system may not work. This greatly improves the troubleshooting process and reduces development time.
The idea behind Keras is to achieve greater levels of visibility into the TensorFlow backend; if you feel frustrated that TensorFlow feels opaque, Keras is an excellent option.
Furthermore, Keras has extensive documentation available for those who are still learning how to develop neural networks and how to operate the TensorFlow end-to-end open source library.
TensorFlow: An Open-Source Library for Machine Learning
TensorFlow is an end-to-end open-source library designed to support machine learning applications. Included within TensorFlow are low-level and high-level APIs, which can be tied together to facilitate machine learning projects. Although it is a moderately complex solution, installing TensorFlow and getting started with it isn’t actually that difficult.
Advantages of TensorFlow include:
- Build Models Easily: TensorFlow is very accessible. If you are a skilled Python programmer and already understand TensorFlow, you will be able to build, train, and deploy a model faster in TensorFlow than Keras, much like a command-line utility can be faster to use than a GUI.
- Fast Model Deployment: Similarly, you’ll be able to deploy and test your models faster, as long as you understand the intricacies of machine learning. Because Keras provides a layer of abstraction, it can also provide a layer of work between the developer and TensorFlow’s features.
- Flexibility and Control: You have finer, granular control with TensorFlow than you do with Keras, as well as access to low-level APIs in addition to high-level APIs.
TensorFlow is an advanced library that demands that a developer deeply understand machine learning technologies and programming. TensorFlow, like Keras, is written in Python, giving Python programmers an edge. Python is one of the most (if not the most) popular programming languages for neural networks and machine learning.
Can You Use Tensorflow and Keras Separately?
You can use TensorFlow without Keras and you can use Keras with CNTK, Theano, or other machine learning libraries. While you can use Keras without TensorFlow, Keras is always going to need a backend; it’s simply an interface rather than a major processing utility. Meanwhile, TensorFlow can be used directly without the support of the Keras interface.
Is TensorFlow Faster than Keras?
Keras introduces a level of abstraction between TensorFlow and the individual. But it depends on what you’re talking about. It’s easier to use Keras, meaning you can deploy models faster. But Keras may introduce some inefficiencies in terms of raw machine speed because everything is filtered through the Keras layer before it hits the TensorFlow libraries.
Is TensorFlow a Keras?
Keras supports TensorFlow, CNTK, and Theano, but TensorFlow is its own open-source library. Keras is a wrapper for the TensorFlow library, focused on fast-paced, intuitive neural net development.
What Are the Advantages of TensorFlow?
While it may seem counterintuitive, directly using TensorFlow might be best for creating small projects. If you aren’t digging too deeply into TensorFlow, you can achieve a small project within 24 hours. But if you’re trying to do something moderately complex, you’ll run into issues of abstraction and complexity. This is where Keras can simplify the system.
At the same time, if you’re trying to do something very complex, you might then run into the boundaries of the Keras system. Thus, TensorFlow has U-shaped benefits; it’s best for those developing small, fast projects and those developing large, scalable projects. For many mid-sized projects, on the other hand, Keras may be superior.
Which is Better: Keras or TensorFlow?
It depends on what you’re trying to do with either. If you are trying to develop a simple neural network for rapid deployment, Keras makes sense. If you need to do in-depth customization or work with low-level APIs, going directly to TensorFlow makes sense.
Beginners may want to start with Keras for the clean, user-friendly interface. At the same time, those who learn Keras may not be directly exposed to all the possibilities of the TensorFlow library.
The Difference Between Keras and TensorFlow
As you can see, it’s difficult to compare Keras and TensorFlow, as Keras is essentially an application that runs on top of TensorFlow to make the TensorFlow deployment process faster and easier. TensorFlow is more difficult to use on its own, but there are some benefits, such as low-level API access. Either way, you can use either Keras or TensorFlow to build, train, and deploy models.
Keras is specifically designed for ease of use and simplicity, whereas many will find that TensorFlow provides for better access to deeper options. So, it depends on what you need to do. It’s always possible to start with Keras and to switch to TensorFlow if you need additional functionality exposed. Likewise, it’s possible to start with TensorFlow and switch to Keras if TensorFlow proves to be too complex.
Tensorflow Keras Examples and Use Cases
Both TensorFlow and Keras essentially have the same use cases: analyzing large volumes of big data. TensorFlow and Keras models can be “trained” on data and then used to model, analyze, and simulate data. Some use cases include:
- Image Recognition: TensorFlow Keras can be used to recognize images, such as identifying faces, or categorizing images, such as identifying cars, bikes, and trucks. This can be used to filter out undesirable data or to mine data for more information.
- Voice Recognition: TensorFlow Keras can also perform complex voice and audio recognition, such as identifying someone’s voice for security purposes or identifying audio for copyright checking.
- Natural Language Processing: Keras can be used to parse natural language, creating chatbots, AI, and suggestion engines that can parse a user’s sentences into something a machine can understand and respond to.
- Time Series Data: You can use complex time series to understand, analyze, and mine consumer data to determine how users behave. This is particularly useful for large-scale eCommerce marketplaces, streaming platforms, and even games.
- Video Detection: NASA is currently using TensorFlow models to model asteroids in space. TensorFlow Keras can be used to detect and extrapolate information within a video just as it can detect information from images and audio.
But new use cases for machine learning technologies are being developed all the time. Data scientists are finding that the pattern recognition and analysis fostered through neural networks and machine learning can be applied to just about any network.
The Bottom Line
Both Keras and TensorFlow are Python-based neural networks and machine learning technologies. This field is broad and constantly growing. If you want to learn more about developing neural networks, creating machine learning models, and analyzing and researching AI, it’s a good idea to learn Python — and to dig deeper into the open-source machine learning technologies available.
Consider enrolling in a machine learning boot camp, taking some lessons in Python, or otherwise learning more about Keras vs TensorFlow.
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