Fix RNN scoping situation Summary: There is a long lasting problem of scoping which was introduced in original python wrappers early in H1. Basically each RNNCell implemented has to manually scope outputs of each of the operators. If somebody forgets, then there could be weird bugs with layers etc. Approach is the following. User has to explicitly specify current scope when using apply_over_sequence function and others if the function is going to be called several times (like for stacking layers). This way we use Caffe2 native scoping approach instead of inventing one extra API people have to use (i.e. passing scope name as an argument to the RNNCell constructor). Closes https://github.com/caffe2/caffe2/pull/1681 Differential Revision: D6777536 Pulled By: salexspb fbshipit-source-id: 73d860b8d4857589e04bdea5a6fcd3080d68427c
Caffe2 is a lightweight, modular, and scalable deep learning framework. Building on the original Caffe, Caffe2 is designed with expression, speed, and modularity in mind.
Please use Github issues (https://github.com/caffe2/caffe2/issues) to ask questions, report bugs, and request new features.
Please participate in our survey (https://www.surveymonkey.com/r/caffe2). We will send you information about new releases and special developer events/webinars.
Caffe2 is released under the Apache 2.0 license. See the NOTICE file for details.