Is there a canonical way to keep state LSTM stateful, etc. when servicing Tensorflow?
Using the Tensorflow API directly is straightforward - but I'm not sure how best to perform a constant LSTM state between calls after exporting the model for service.
Are there any examples that accomplish the above? The patterns within the repo are very simple.
From Martin Wick on the TF mailing list:
" . , , . , , , . , , ( - , ), , TensorFlow ( ), , / ."
Source: https://habr.com/ru/post/1016904/More articles:Changing Python IP address - pythonNo LLC on Windows? - windowsHeroku CI with rails - ruby-on-railsChanging Selenium Python IP address - pythonAngular Material 2 - Handling Multiple Validation Error Messages - angularparse integer без добавления char в C - cTypeError: t.fx undefined when trying to add jQuery-UI to an Angular 4 application - jqueryWhy are my React nested components not showing up? - reactjsJsx file naming convention - node.jstrying to distinguish between different types of rvalues - literals and non-literals - c ++All Articles