Keras and PyTorch are two of the most powerful open-source machine learning libraries.
Keras is a python based open-source library used in deep learning (for neural networks).It can run on top of TensorFlow, Microsoft CNTK or Theano. It is very simple to understand and use, and suitable for fast experimentation. Keras models can be run both on CPU as well as GPU.
PyTorch is an open-source machine learning library which was developed by Facebook’s AI Research Group. It can be integrated with Python and C++. It is popular because of its efficient memory usage and the ability to debug neural networks easily.
Let’s see the differences between Keras and PyTorch.
|1.||Keras was released in March 2015.||While PyTorch was released in October 2016.|
|2.||Keras has a high level API.||While PyTorch has a low level API.|
|3.||Keras is comparatively slower in speed.||While PyTorch has a higher speed than Keras, suitable for high performance.|
|4.||Keras has a simple architecture,making it more readable and easy to use.||While PyTorch has very low readablility due to a complex architecture.|
|5.||Keras has a smaller community support.||While PyTorch has a stronger community support.|
|6.||Keras is mostly used for small datasets due to its slow speed.||While PyTorch is preferred for large datasets and high performance.|
|7.||Debugging in Keras is difficult due to presence of computational junk.||While debugging in PyTorch is easier and faster.|
|8.||Keras provides static computation graphs.||While PyTorch provides dynamic computation graphs.|
|9.||Backend for Keras include:TensorFlow, Theano and Microsoft CNTK backend.||While PyTorch has no backend implementation.|
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