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- 2022. 6. 7. · Keras and TensorFlow are both neural network machine learning systems. But while TensorFlow is an end-to-end open-source library for machine learning, Keras is an interface or layer of abstraction that operates on top of TensorFlow (or another open-source library backend). When you use Keras, you’re really using the TensorFlow library.
- PyTorch is a deep learning framework with a pythonic and object oriented approach. PyTorch has more debugging and testing options than TensorFlow. TensorFlow is a low-level deep learning library that provides workflows to high-level APIs such as Keras - albeit with less computational power. TensorFlow is currently more widely used than PyTorch.
- Keras is a high-level API which is running on top of TensorFlow, CNTK, and Theano whereas TensorFlow is a framework that offers both high and low-level APIs. Keras is perfect for quick implementations while Tensorflow is ideal for Deep learning research, complex networks. Keras uses API debug tool such as TFDBG on the other hand, in, Tensorflow ...
- 2 answers. In my opinion, you cannot compare OpenCV ML module with TensorFlow (on one hand the ML module contains some classical ML algorithms, on the other hand Tensorflow is one of the state of the art DNN library heavily maintained by Google and other people). Use Tensorflow (or one of the many DNN libraries that exist) if you want to use DNN.
- 2019. 4. 3. · 3| Community. TensorFlow is adopted by many researchers of various fields like academics, business organisations, etc. It has a much bigger community than PyTorch which implies that it is easier to find for resources or solutions in TensorFlow. There is a vast amount of tutorials, codes, as well as support in TensorFlow and PyTorch, being the ...