In the current Demanding world, we see there are 3 top Deep Learning Frameworks. However, still, there is a confusion on which one to use is it either Tensorflow/Keras/Pytorch. So, let’s go little into details of each of these Frameworks in the below factors and see which one suits your needs and who stands at the top.
Introduction –
Tensor Flow:
Tensor Flow is an open-source library for dataflow programming across a range of tasks. It is symbolic math library that is used for Machine Learning applications like neural networks.
Keras:
Keras is an open source neural network library written in Python. It is capable of running on top of Tensor Flow. It is designed to enable fast experimentation with deep neural networks.
PyTorch:
PyTorch is an open-source Machine learning for Python, based on the torch. It is used for applications such as Natural Language Processing and was developed by Facebook’s AI Research group.
Comparison Factors –
All 3 Frameworks are related to each other and also have certain basic differences that distinguish them from one another.
Level of API:
Keras is a high-level API capable of running on top of TensorFlow, CNTK, and Theano. It has gained favour for its ease of use and syntactic simplicity, facilitating fast development.
TensorFlow is a framework that provides both high and low-level APIs.
Pytorch, on the other hand, is a lower-level API focused on direct work with array expressions. It has gained immense interest in the last year, becoming a preferred solution for academic research, and applications of deep learning requiring optimizing custom expressions.
Speed:
Tensor Flow and PyTorch provide high performance with a similar pace and Fast. Whereas Keras is slow in performance comparatively with PyTorch and Tensor Flow.
Architecture:
Keras has a simple architecture that is more readable and concise.
Tensor Flow is not very easy to use even though it provides Keras as a Framework that makes work easier.
PyTorch has a complex architecture, the readability is less when compared to Keras.
Debugging:
In Keras, it is very less frequently need to debug the simple networks.
In Tensor It is quite difficult to perform the debugging.
PyTorch has debugging capabilities when compared to the other 2 frameworks Keras and Tensor Flow.
Dataset:
Keras is used for small datasets as it is comparatively slower.
On the other hand, Tensor Flow and PyTorch are used for high-performance models and large datasets that require fast execution.
Popularity:
With the increasing demand in the field of Data Science, there has been an enormous growth of Deep learning technology in the industry. With this, all three frameworks have gained quite a lot of popularity. Keras tops the list followed by TensorFlow and PyTorch. It has gained immense popularity due to its simplicity than the other 2 Frameworks.
However, there is no absolute to answer which one framework is better, as all the above parameters just show the difference between the 3 frameworks. Ultimately the final choice of deciding the framework depends on these below 3 factors.
- Technical Requirements
- Requirements
- Ease of Use
Final Conclusion –
Keras Is used in the scenarios where
- Rapid Prototyping
- Small Dataset
- Multiple back-end support
Tensor Flow is used in the scenarios where
- Large Dataset
- High Performance
- Functionality
- Object Detection
PyTorch is used in scenarios where
- Flexibility
- Short Training Duration
- Debugging capabilities
