Keras v/s Pytorch Introduction to Implementation
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The document compares Keras and PyTorch, highlighting that Keras is a high-level API built on TensorFlow, Theano, and CNTK, making it easy to use but more challenging to debug due to abstraction. In contrast, PyTorch offers a low-level API with more flexibility, allowing for easier debugging and line-by-line execution, although it lacks direct data visualization support. Finally, Keras exports models in safe formats like JSON or H5, while PyTorch saves models in pickle format.
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Keras v/s Pytorch Introduction to Implementation
- 2. Keras vs PyTorch Keras • High level API • Works on top of TensorFlow, Theno and CNTK. • Supported by GOOGLE. • Easy to use PyTorch • Low Level API • Directly works with array expression • Supported by Facebook. • Flexibility to use
- 4. Debudding Keras • Keras, wraps lot of computation in abstractions • Difficult pin down the error. • Less chances of making errors Pytorch • Verbose framework • Execute line by line • Easy to Debug(We can debug like numpy)
- 5. Exporting models • Keras • Exports model to JSON or H5. • Safer • Can take advantage of tensor board for data visualization and model interpretation • PyTorch • Save the model in pickles • No direct data visualization support.
- 6. Performance
- 7. Thank you