Before moving to our topic of Keras vs TensorFlow, let us first know about what Keras and TensorFlow are and their features so that we can compare them in greater clarity. Let’s begin our discussion!
What is Keras?
Keras is one of the well-known APIs that is open-source, with a neural network library written in Python. It can run on the leading Deep Learning tool kits such as Microsoft Cognitive, TensorFlow, and Theano. It allows for faster analysis with deep neural networks.
Some of the important features of Keras:
- User friendly: It is easy to understand since it is a completely Python-based framework and has a high-level interface.
- Modular in nature: It is suitable for inventive research that is flexible and expressive. It also relays on both CPU and GPU.
- Supports arbitrary networks: It supports model sharing, multiple input-output models, layer sharing, and many more.
To get a clear explanation on Keras vs TensorFlow, check out our video:
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What is TensorFlow?
The next topic of discussion in this Keras vs TensorFlow blog is TensorFlow. TensorFlow is an open-source Machine Learning library meant for analytical computing. It is a cross-platform tool. It can run on both the Graphical Processing Unit (GPU) and the Central Processing Unit (CPU), including TPUs and embedded platforms. What are TPUs? Let us understand them, before jumping into the features of TensorFlow.
Tensor Processing Units (TPUs) that were developed by Google are used to run Machine Learning workloads. They are designed to accelerate flexibility and performance that help developers and researchers create TensorFlow clusters that make use of TPUs and GPUs.
Here are some of the features of TensorFlow:
- Supports model building: It offers various levels of concepts to train and build models.
- Effective research: TensorFlow allows us to control model subclassing API and Keras functional API to create tough topologies.
- Robust and platform-independent: It helps deploy our applications very easily, irrespective of the language or platform we use.
Now, let’s move to our topic of discussion, ’Keras vs TensorFlow.’ Keep reading!
Difference Between Keras and TensorFlow
The basic differences between TensorFlow and Keras are based on the below parameters:
|Origin||A simple Python library for Deep Learning, developed by Francois Chollet|| A set of libraries for Machine Learning, developed by Google Brain|
|Architecture||Concise and readable with pure architecture||Readable and modifiable with scale-flexible architecture|
|Speed||Slow performance as it uses Theano or TensorFlow in the backend for processing|| Fast performance because of the profiler in backend processing|
|APIs|| Has a high-level API and can run on Theano and CNTK||Has both high-level and low-level APIs|
|Debugging|| Easy to debug||Complex to debug|
|Dataset|| Used for small datasets|| Used for massive datasets|
|Capability|| Capable of rapid prototyping and multiple back-end support|| Capable of object detection and functionality|
In this Keras vs TensorFlow blog, you have got an understanding of Keras, TensorFlow, and some of their differences. Now, let’s discuss the newer version of TensorFlow, i.e., TensorFlow 2.0. Let’s begin!
When Google had stiff competition with top frameworks such as PyTorch and Keras, to ensure to get back on top, Google came up with a second iteration: TensorFlow 2.0—the most popular open-source library offering. It was officially released in the year 2019.
With TensorFlow 2.0, beginners can easily understand and execute Deep Learning tasks. Let’s check out the newly added features in TensorFlow 2.0.
TensorFlow 2.0 Framework
Tensorflow 2.0 is one of the most effective frameworks that can easily be integrated with the Python runtime by eager execution. It also removes irrelevant APIs and makes it more stable.
What is eager execution? Let’s check it out.
Eager execution is a define-by-run interface in which operations have to be executed as and when they are called from Python. Here are some of the benefits of eager execution:
- Fast debugging
- Supports dynamic models
- Strong high-order gradients
- Supports all operations present in TensorFlow
Let’s check out the various changes made in the TensorFlow 2.0 version in this Keras vs TensorFlow blog.
Learn about the difference between PyTorch and TensorFlow in our comparison blog on PyTorch vs TensorFlow.
Major Changes in TensorFlow 2.0
Here are some of the important changes that are present in the new TensorFlow 2.0 that made it user-friendly and easy to use and understand.
- API clear up: As in TensorFlow 1.x, there are multiple APIs that are shown-up and ready to expand their libraries. In TensorFlow 1.x, there were multiple options to select from tf.layers (TensorFlow.layers), tf.slim, and tf.contrib.layers. Besides, there were many code patterns for math, ML functions, and debugging. We can say that it was quite messy! In TensorFlow 2.0, there is an elimination of unnecessary APIs to make it simple. Some of the APIs such as tf.flags, tf.logging, and tf.app are removed in TensorFlow 2.0. It is replaced with a simple Keras version.
- Keras as the high-level API: In TensorFlow 1.x, there were both high-level and low-level APIs that used to be complex and confusing to understand and work with. It was also tough to run Deep Learning tasks. In the release of TensorFlow 2.0, Keras has become an official high-level API. Keras will come along when we install TF2.0 (TensorFlow). Hence, the integration of Keras with TensorFlow does not need any code bridge. We can also use Keras code in TensorFlow, which makes it easy to build something unique.
- TensorFlow datasets: In TensorFlow 1.x, it was not so easy to train large datasets. So, there was a need for queue runners. The queue runners are completely replaced in the newer version of TensorFlow. In TensorFlow 2.0, the queue runners are replaced with tf.data. The huge training datasets can now be read clearly by making use of input pipelines. It also supports NumPy arrays to input memory data. It is easy to use and handle the flow functions of Keras in TensorFlow 2.0.
- Easy to run THE older version code in the newer version: This gives the biggest relief to developers to know that they can use TensorFlow 1.x code while running TF 2.0. It makes the transition from version 1.x to version 2.0 smooth, without any crucial break. It also allows developers to build stable changes by switching a part of code as required by 2.0 enhancements.
Keras vs Tensorflow – Which one should you learn?
I hope this blog on TensorFlow vs Keras has helped you with useful information on Keras and TensorFlow. We need to understand that instead of comparing Keras and TensorFlow, we have to learn how to leverage both as each framework has its own positives and negatives. Choosing between Keras or TensorFlow depends on their unique features and the various tasks in which these frameworks are used. Developers or researchers have to choose their frameworks according to the requirements of their tasks.
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