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Difference between TensorFlow and Keras
In this article, you will understand the significant differences between Tensorflow and Keras libraries.
TensorFlow
TensorFlow is an open-source end-to-end platform to build machine learning applications. It was developed by researchers and developers at Google Brain.
Let us now see the features of TensorFlow −
Build and Train models easily − TensorFlow offers multiple levels of abstraction to make it quick for you to choose the correct one. Build and train models by using the high-level Keras API, which makes beginning with TensorFlow and machine learning easy. Eager execution lets immediate iteration and intuitive debugging. For large ML training tasks, use the Distribution Strategy API for distributed training on different hardware configurations without changing the model definition.
TensorFlow Serving − It is a flexible and high-performance serving system for machine learning models, designed for production environments. It runs ML models at production scale on the most advanced processors in the world, including Google's custom Tensor Processing Units (TPUs).
TensorFlow Extended − TensorFlow Extended (TFX) is an end-to-end platform for deploying production Machine Learning pipelines. If you need a full production ML pipeline, use the TensorFlow Extended.
TensorFlow Lite − TensorFlow Lite is a mobile library for deploying models on mobile, microcontrollers and other edge devices. For running inference on mobile and edge devices, use TensorFlow Lite.
TensorFlow.js − Train and deploy models in JavaScript environments using TensorFlow.js. TensorFlow.js is a library for machine learning in JavaScript Develop ML models in JavaScript, and use ML directly in the browser or in Node.js.
State-of-the-art models − Build and train state-of-the-art models without sacrificing speed or performance. TensorFlow gives you the control with features like the Keras Functional API and Model Subclassing API for creation of complex topologies.
Ecosystem of powerful add-on libraries − TensorFlow also supports an ecosystem of powerful add-on libraries and models to experiment with, including Ragged Tensors, TensorFlow Probability, Tensor2Tensor and BERT.
Robust ML Production − TensorFlow brings a direct path to production. Even if it is on servers, edge devices, or the web, TensorFlow lets you train and deploy your model easily, irrespective what language or platform you use.
Following are the Advantages of Tensorflow
It is a general framework, and can be applied to other domains too.
It provides Python, and C++ APIs.
It has comprehensive and flexible tools, libraries, and community to build and deploy state-of-the-art Machine Learning applications.
It is available on Linux, Windows, Android, iOS, and macOS.
It also provides support for reinforcement learning, deep learning, NLP, image recognition, time series, and video detection.
It has excellent documentation, and a supportive community for contributors.
It provides parallelism in terms of data and models.
It supports execution on CPU and GPU.
Disadvantages
Matrix operations cannot be performed.
It takes time to execute operations in comparison to other frameworks.
Dynamic typing is prone to errors in high scalability development.
Keras
Keras is an open-source neural network library that has the ability to run on Theano and Tensorflow. It helps construct deep learning algorithms and other Machine Learning algorithms.
Following is the Advantages of Keano
It has an API with user-friendly features that can be easily understood.
You can choose Keras to pick any library that it supports for the backend.
It provides pre-trained models that help users to improve models further.
It has a strong community of users that help contribute and improve on models.
Following is the Disadvantages of Keano
Some of the pre-trained models don’t provide a lot of support to design models.
Errors given out are not easily understandable.
It is a low-level API.
Keras vs Tensorflow
| Keras | TensorFlow | |
|---|---|---|
| What? | Keras is an open-source neural network library that has the ability to run on Theano and Tensorflow. | TensorFlow is an open-source end-to-end platform to build machine learning applications and was developed by researchers and developers at Google Brain. |
| Architecture | The architecture of Keras is simple and readable. | TensorFlow has a complex architecture. |
| Speed | Keras speed is slow. | TensorFlow is faster than Keras. |
| Languages | Keras is written in Python. | TensorFlow is written in C++, Python and CUDA. |
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