When comparing TensorFlow vs Infer.NET, the Slant community recommends TensorFlow for most people. In the question“What are the best artificial intelligence frameworks?” TensorFlow is ranked 2nd while Infer.NET is ranked 4th. 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 Supports multiple inference algorithms
Infer.NET supports expectation propagation (including belief propagation as a special case), variational message passing (also known as variational Bayes), max product (for discrete models), and block Gibbs sampling.
You can use Infer.NET to solve many different kinds of machine learning problems, from standard problems like classification, recommendation or clustering through to customised solutions to domain-specific problems.
Pro Cross platform
Can be used on Windows (.NET), OSX or Linux (using mono)
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 Closed source
Con No commercial license
Infer.NET is free for academic use. However, at this time, commercial use of Infer.NET is limited to Microsoft. No other commercial licenses are available.