Both Tensorflow and Keras are famous machine learning modules used in the field of data science. In this article, we will look at the advantages, disadvantages and the difference between these libraries.
TensorFlow is an open-source platform for machine learning and a symbolic math library that is used for machine learning applications.
Advantages of TensorFlow:
- Tensor flow has a better graph representation for a given data rather than any other top platform out there.
- Tensor flow has the advantage that it does support and uses many backend software like GUI and ASIC.
- When it comes to community support tensor flow has the best.
- Tensor flow also helps in debugging the sub-part of the graphs.
- Tensor flow has shown a better performance when compared with other platforms.
Disadvantages of TensorFlow:
- Tensor flow not specifically designed for the Windows operating systems but it is designed for other OS like Linux but tensor flow can be installed in windows with the help of a python package installer(pip).
- The speed of the tensor flow is less when it is compared to other platforms of the same type.
- For a better understanding of tensor flow, the user must have the fundamentals of calculus.
- Tensor flow does not support OpenCL.
It is an Open Source Neural Network library that runs on top of Theano or Tensorflow. It is designed to be fast and easy for the user to use. It is a useful library to construct any deep learning algorithm of whatever choice we want.
Advantages of Keras:
- Keras is the best platform out there to work on neural network models.
- The API that Keras has a user-friendly where a beginner can easily understand.
- Keras has the advantage that it can choose any libraries which support it for its backend support.
- Keras provides various pre-trained models which help the user in further improving the models the user is designing.
- When it comes to community support Keras has the best like stack overflow.
Disadvantages of Keras:
- The major drawback of Keras is it is a low-level application programming interface.
- Few of the pre-trained models that the Keras has been not much supportive when it comes to designing of some models.
- The errors given by the Keras library were not much helpful for the user.
Difference between TensorFlow and Keras:
|1.||Tensorhigh-performanceFlow is written in C++, CUDA, Python.||Keras is written in Python.|
|2.||TensorFlow is used for large datasets and high performance models.||Keras is usually used for small datasets.|
|3.||TensorFlow is a framework that offers both high and low-level APIs.||Keras is a high-Level API.|
|4.||TensorFlow is used for high-performance models.||Keras is used for low-performance models.|
|5.||In TensorFlow performing debugging leads to complexities.||In Keras framework, there is only minimal requirement for debugging the simple networks.|
|6.||TensorFlow has a complex architecture and not easy to use.||Keras has a simple architecture and easy to use.|
|7.||TensorFlow was developed by the Google Brain team.||Keras was developed by François Chollet while he was working on the part of the research effort of project ONEIROS.|
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