PyTorch vs Keras

Updated March 28, 2023

Difference Between PyTorch vs Keras

The PyTorch is a deep learning type framework that is low level based API that concentrate on array expressions. The PyTorch framework supports the python programming language and the framework is much faster and flexible than other python programming language supported framework. The Keras is other learning framework that is based on python programming language that uses the neural networks and execute on TensorFlow. The Keras framework more focused on research, development type applications and can be easily extends to add new features in the framework so that it can be used widely for the applications.

Head to Head Difference Between PyTorch vs Keras

Below are the top 7 differences between PyTorch vs Keras

Key differences Between PyTorch vs Keras

Below are the key differences mentioned:

1. One of the major difference between both the frameworks is size of the dataset in the framework. The Keras uses the small size dataset as the size of the network is small and simple in this framework the PyTorch framework contains the large size network that use the large size dataset in the framework. The use of the dataset is in the research and development for the application.
2. The other difference both the frameworks is performance of the framework. The PyTorch framework has high performance and the processing speed is much more compared to other framework. The PyTorch framework is more suitable for the application that requires fat processing speed and high performance. The Keras framework uses for those applications which does not focused on performance and processing speed.
3. The other key difference is the debugging capabilities of the framework. The PyTorch framework supports the debugging feature in its framework as the size of network is very large this feature is important for this framework. The Keras framework contains simple network that does not require debugging feature and the framework supports the applications that has simple architecture.
4. The main difference between PyTorch framework and Keras framework is flexibility of the framework. The Keras is high-level type framework which bundles up the learning layers and the features provided by the framework is limited when it is compared to PyTorch framework. In PyTorch framework the custom layers can be added to provide the extensibility in the framework. The new features can be added in this framework and all functions can be properly used in PyTorch framework.
5. The community support for the PyTorch is more when it is compared to Keras framework. The documentation for the PyTorch is more easy to read and understand compare to Keras framework. The Keras is more suitable for the beginners as the size of network is small and easy to understand in Keras framework. The code readability is easy and simple in Keras framework.
6. One of the other important difference between Keras and PyTorch framework is support for cross platform and portability. The PyTorch framework does not supports the portability feature and the features is limited for PyTorch framework. The Keras is better option when there is need of portability as the framework supports the cross platform that means the Keras framework can be run on top of the TenserFlow framework.
7. The abstraction feature is provided in Keras framework. It means he complex information and details are hidden for the user and the framework can be easily used for the beginners. The PyTorch is little complex and does not support this features in its framework. The complete information is required to know for the framework before its can be used for the application. The feature of customization is supported in PyTorch framework that means new custom layers can be added as per the user requirement in the framework.

PyToch vs Keras Comparison Table

Below are the primary comparison between PyTorch vs Keras:

Conclusion

The deep learning based frameworks i.e. PyTorch and Keras supports python programming language in their frameworks. Both the frameworks are widely used for the research and development applications and on the basis of user requirement the frameworks can be selected and used for the application.

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