Difference Between TensorFlow vs Keras
Tensorflow is the most renowned library used for profound learning models in development. It has a massive and wonderful culture. Tensorflow is sufficient for the widespread popularity of the commits as well as the number of forks on the TensorFlow Github depository. But it is not so easy to use TensorFlow. On the other hand, Keras is a TensorFlow based High-Level API. It is simpler to use as compared to Tensorflow. How are the two differences, when Keras is installed on the top of Tensorflow? And why should I ever use Tensorflow for deeper learning models, if Keras is more user-friendly? The points below will clarify which one to choose from.
What is Keras?
Keras is a high-level profound learning Python library commonly used to create neural networks to solve complex challenges by data scientists. The higher level API means Keras can serve as a front end and Theano or Tensor-flow can be used as a rear end. When implementing deep neural networks, Keras promotes the research of data scientists. It is highly popular with its broad, easy to understand API. Documentation is very clear to everyone to start. The other thing is the higher level API can be used. This means that it can act as an interface for the TensorFlow, Theano, etc.
What is Tensorflow?
Today, Google’s TensorFlow is the world-famous profound computing library. The products used to improve search engines, translations, subtitling or recommendations by Google use machine learning in all its Products. Google has not only data; it has the largest computer in the world, which means that it was built to scale Tensor Flow. TensorFlow is a Google Brain project library to speed up machine learning and research into deep neural networks. Designed to run with several CPUs and GPUs, it has several wrappers, in several languages such as C++, Python or Java.
Head to Head Comparison between TensorFlow vs Keras (Infographics)
Below are the top 7 differences between TensorFlow vs Keras:
Key Differences Between TensorFlow vs Keras
The key differences between a TensorFlow vs Keras are provided and discussed as follows:
- Keras is a high-level API that runs on TensorFlow. For its simple usability and its syntactic simplicity, it has been promoted, which enables rapid development.
- The performance of Keras is comparatively slow, while Tensorflow delivers a similar pace that is fast and efficient.
- The architecture of Keras is plain. It is easier to read and briefer. On the other hand, TensorFlow is not easy to use, although it provides Keras as a system that facilitates working.
- For keras, the debugging of simple networks is typically much less difficult. Whereas, debugging is very difficult for Tensorflow.
- Keras is usually used as a slower comparison with small datasets. TensorFlow, on the other hand, is used for high-performance models and large data sets requiring rapid implementation.
TensorFlow vs Keras Comparison Table
Let’s discuss the top comparison between TensorFlow vs Keras:
|
TensorFlow |
Keras |
| Tensorflow is a low-level architecture API. | Keras is a High-level architecture API. |
| TensorFlow is not comparatively easy to use. | It is more user friendly and easy to use as compared to TensorFlow. |
| Radio prototyping is not feasible in Tensorflow. | In Keras, Radio prototyping means building simple or complex neural networks can be done within a few minutes. |
| TensorFlow provides more advanced operations as compared to Keras. | Keras provides various general-purpose functionalities for building Deep learning models. |
| The architecture of TensorFlow is complex. | The architecture of Keras is Simple. It is easier to understand. |
| Debugging is difficult in Tensorflow. | Debugging is easier in Keras. |
| TensorFlow is used for high-performance models and large data sets which requires rapid implementation. | Keras has small datasets. |
Among these two systems, there are many variations. Keras is an open-source library for a number of different tasks during machine learning while TensorFlow is an open-source library. TensorFlow provides high and low-level APIs, while Keras only supplies high-level APIs. Tensorflow’s robust execution makes it possible to instantly iterate with intuitive debugging In terms of flexibility. All frameworks, therefore, promote the creation and training of high-level API models. Keras has a Python design that makes it much easier to use than TensorFlow.
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