Tensorflow losing has nothing to do with Google getting bored -- it's vice versa.
Tensorflow is a symbolic framework, which is less intuitive to work with for most people than the Pytorch. Not to mention the errors Tensorflow generates are more annoying to debug (again more an issue with the fact that it's symbolic than any lack of effort on part of Google)
Google tried to fix it by introducing an eager mode in Tensorflow but by then it was too late.
> Google tried to fix it by introducing an eager mode in Tensorflow but by then it was too late.
And the fix was "new major version with a fundamentally different programming paradigm"
But it turns out when your users are irritated with your product, and you tell them to change to a fundamentally different programming paradigm, the new programming paradigm they change to might not be yours.
Intuitive has nothing to do with it. Developers will tend to prefer things that make their lives easier. Debuggability is a huge part of that. Tf 1.0 having a static execution graph was a major pain. No wonder people switched to PyTorch and didn’t look back.
Tensorflow is a symbolic framework, which is less intuitive to work with for most people than the Pytorch. Not to mention the errors Tensorflow generates are more annoying to debug (again more an issue with the fact that it's symbolic than any lack of effort on part of Google)
Google tried to fix it by introducing an eager mode in Tensorflow but by then it was too late.