TensorFlow vs Keras – Which is Better?
Keras and TensorFlow both are Python libraries. These libraries play an important role in the field of Data Science. These are a collection of built-in functions and help you in your overall programming execution. It helps you to build a special kind of application. They simplify your tasks. The library enables you to write code in fewer lines of code. Keras and TensorFlow are such libraries that help you in the field of Data Science. These both are the most popular libraries when it comes to Deep Learning. These libraries focus on fast implementation. It enables you to complete your tasks in less time. Let us learn about TensorFlow vs Keras.
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TensorFlow is an open-source Python library. It enables you to perform dataflow tasks over a wide range of tasks. It is a symbolic math library and mostly useful in Machine Learning. TensorFlow uses symbolic math for dataflow and differential programming. Its APIs are easy-to-use. It has a comprehensive system of functions and resources that help you to deal with high-level APIs.
This library provides you with tons of concepts that will lead you to work with Machine Learning models. The most famous application of TensorFlow is its implementation in Neural Network, analyzing handwriting, and face recognition. The logic in TensorFlow is unique. It relies on both a machine’s CPU as well as GPU.
Pros of using TensorFlow
1. Platform independent: TensorFlow enables you to implement your ML model anywhere. It does not care about the platform you are using. You can use TensorFlow on any language or any platform.
2. Enhances the creation of complex technology: TensorFlow provides you flexible features to deal with complex technologies. It has controllable features like Keras functional API and Sub Classing API that helps you to create complex technology.
Keras is an open-source library built in Python. It runs on the top of Theano and TensorFlow and is a high-level API. It does not deal with low-level computations. Keras is built to enable fast implementation in Deep Learning Neural Networks. Using Keras in Deep Learning enables fast and quick prototyping. It runs seamlessly on CPU and GPU. It is easy to debug and offers you more flexibility. Keras wraps its functionality around other Depp Learning and Machine Learning libraries. It can be used to train and build models.
Benefits of using Keras
1. User-friendly: Keras is a user-friendly library that has a readable and easy syntax. It has a simple interface that is flexible. It also provides you clear error messages.
2. Extensibility: It is highly extensible. It enables you to write custom building blocks for new ideas. It is easy to extend.
TensorFlow vs Keras
Although TensorFlow and Keras are related to each other. These have some certain basic differences. These differences will help you to distinguish between them.
1. Functionality: Although Keras has many general functions and features for Machine Learning and Deep Learning. But TensorFlow is more advanced and enhanced. TensorFlow is more active in high-level operations such as threading, debugging, queues, etc.
2. Speed: Keras is slower than TensorFlow. The performance is comparatively slower in Keras. Whereas TensorFlow provides a similar pace which is fast and suitable for high performance.
3. Increase in control: Control is not an important requirement. But some Neural Networks may require it to have a better understanding. It sometimes becomes important when you have to deal with concepts like weights and gradients. TensorFlow is proficient in this. TensorFlow offers this option much more than Keras.
4. Debugging: Keras provides you an opportunity that enables you less frequent need to debug. For simple networks, there is no need for debugging. But when it comes, it is quite difficult to perform debugging.
5. Level of API: Keras is a high-level API. It is capable of running on the top of TensorFlow and Theano. It has an easy and simple syntax and facilitates fast implementation. TensorFlow provides both low and high-level API. It focuses on direct work with array expressions.
6. Popularity: Keras is much more popular than TensorFlow. It has gained more popularity in recent years. It has gained enormous growth due on the way to Deep learning. Also, Keras has easy syntax, which leads to an increase in its popularity.
7. Architecture: Keras has a simple architecture. It is more readable and concise than TensorFlow. TensorFlow, on the other hand, does not have any simple architecture as such. It is not easy to work with it. Although it provides Keras as a library that makes works easier.
8. Dataset: As Keras is comparatively small, it deals with small datasets. It is not able to handle complex datasets. On the other hand, TensorFlow allows you to work with complex and large datasets. It is due to the fact that TensorFlow offers high performances that require fast executions.
Keras and TensorFlow both work with Deep Learning and Machine Learning. Keras is a Python library that is flexible and extensible. It runs on the top of Theano and TensorFlow. Both are an open-source Python library. TensorFlow offers you high-performance factors. Both libraries are similar. There are not many differences. There are a few points which help you to distinguish between TensorFlow vs Keras.