keras VS Tensorflow

Keras and TensorFlow are two leading players in deep learning frameworks, and both platforms stand as integral tools for developing and deploying machine learning models. Both frameworks share a common objective of empowering researchers and developers in the dynamic landscape of artificial intelligence.

However, Keras and TensorFlow differ in key features and functionalities despite their shared goal. We have differentiated both solutions based on their pros and cons, API architecture, debugging, and more.

Keras vs TensorFlow: Overview

Keras is a high-level API that belongs to the TensorFlow platform. It offers a highly approachable and productive platform for solving problems related to machine learning. In addition, it covers every step of the machine learning workflow, from data processing to deployment.

On the other hand, TensorFlow was developed by Google and released to the public in 2015; it is an open-source library used for numerical computation and large-scale machine learning. Some key features of TensorFlow include pre-built algorithms, application integration, and more.

Between Keras and TensorFlow, Keras is often preferred for its simple API architecture, while TensorFlow's extensive deployment options make it a favourable choice for production pipelines.

Keras vs TensorFlow: Pros & Cons

• Keras has a simple architecture that is readable and concise. On the contrary, TensorFlow is not very easy to use.
• TensorFlow is used for high-performance models and extensive datasets as it requires faster execution. On the other hand, Keras is used for smaller datasets as it is slower.
• Keras supports a simple network, so debugging is not always needed. However, with TensorFlow, debugging can be a complex process.
• The overall speed in Keras is slow as it uses Theano or TensorFlow in the backend for processing. But, with TensorFlow, you will experience fast performance as the profiler is used in backend processing.

Keras vs TensorFlow: In Terms of Features

• Visualization Tools: Based on the visualization feature, tools offered by Keras are not as advanced and extensive as TensorFlow. Therefore, TensorBoard, a popular visualization tool by TensorFlow, will simplify the debugging process, making it a favourable option for users.
• Model Deployment: On the basis of deployment, Keras has a simpler deployment model, but TensorFlow and a few TensorFlow alternatives support more deployment options, including TensorFlow Serving, TensorFlow Lite, and TensorFlow.js. Therefore, TensorFlow will be ideal for a deep learning framework with multiple deployment capabilities.
• Language Support: Keras only supports Python, but TensorFlow has multi-language capabilities, including Python, C++, and Java.
• Distributed Training: TensorFlow offers comprehensive support for distributed training. However, Keras might require more manual configurations for complex setups.

Keras vs TensorFlow: Ease of Use

Based on ease of use, Keras and Keras alternatives are considered more user-friendly since it has a simple, readable, concise architecture, whereas TensorFlow is not very easy to use.

Keras vs TensorFlow: Datasets

Keras is apt for smaller datasets as it is slower. However, TensorFlow is used for high-performance models and massive datasets that require faster execution.

Keras vs TensorFlow: Speed

The performance is sluggish and slower in Keras, while TensorFlow and TensorFlow alternatives provide a high-paced, fast performance ideal for large dataset models.

Keras vs TensorFlow: Learning Curve

TensorFlow comes with a steep learning curve; on the contrary, Keras is simple to learn because of its easy interface, which makes it a favourable choice among beginners.

Keras vs TensorFlow: Debugging

Debugging in TensorFlow is a complex process, but Keras and some of the Keras alternatives have a simple architecture, so it requires less debugging.

Which is Better, Keras or TensorFlow?

Keras outshines TensorFlow in various important areas, making it a favourable choice for most users. First and foremost, Keras has a simple architecture, unlike TensorFlow, so, it becomes simple for the users to understand the basic functioning of Keras. In addition, Keras does not require frequent debugging because of its intuitive interface and highly dynamic architecture.

However, TensorFlow shines under the speed parameter. TensorFlow offers high performance, which is needed for working on large datasets.