When comparing TensorFlow vs Keras, the Slant community recommends TensorFlow for most people. In the question“What are the best artificial intelligence frameworks?” TensorFlow is ranked 2nd while Keras is ranked 3rd. The most important reason people chose TensorFlow is:
TensorFlow is developed and maintained by Google. It's the engine behind a lot of features found in Google applications, such as: * recognizing spoken words * translating from one language to another * improving Internet search results Making it a crucial component in a lot of Google applications. As such, continued support and development is ensured in the long-term, considering how important it is to the current maintainers.
Ranked in these QuestionsQuestion Ranking
Pro Ensured continued support
TensorFlow is developed and maintained by Google. It's the engine behind a lot of features found in Google applications, such as:
- recognizing spoken words
- translating from one language to another
- improving Internet search results
Making it a crucial component in a lot of Google applications. As such, continued support and development is ensured in the long-term, considering how important it is to the current maintainers.
Pro Python has a lot of powerful scientific libraries available
Other than having an easy syntax, using Python also gives developers a wide range of some of the most powerful libraries for scientific calculations (NumPy, SciPy, Pandas) without having to switch languages.
Pro Easily spin up sessions without restarting the program
TensorFlow can run with multiple GPUs. This makes it easy to spin up sessions and run the code on different machines without having to stop or restart the program.
Pro Written in Python, which is regarded as a really pleasant language to read and develop in
TensorFlow is written in Python, with the parts that are crucial for performance implemented in C++. But all of the high-level abstractions and development is done in Python.
Pro Visualization suite available
Google has made a powerful suite of visualizations available for both network topology and performance.
Pro Great debugging potential
You can introduce and retrieve the results of discretionary data on any edge of the graph. You can also combine this with TensorBoard (suite of visualization tools) to get pretty and easy to understand graph visualizations, making debugging even simpler.
Pro Uses Tensorflow or Theano as backend
Pro Simple to use
Pro Can be used to write really short pieces of code
Keras enforces minimalism as much as possible. Because of this, it's possible to write a small Neural Network in just a couple of lines of code.
Pro Really straightforward for someone who is familiar with deep learning
Pro Quite modular
When using Keras you don't have to pull every part of the framework on your project. For example, you can only use training algorithms and not layer implementations. So it works more like a collection of libraries.
Con Not fully open source
For now, Google has only open sourced parts of the AI engine, namely some algorithms that run atop it. The advanced hardware infrastructure that drives this engine is not "open source".
Con Distributed learning not well integrated
poor support for multiple GPUs