Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Extending TF Support for RNN & Upsample Layers #314

Open
wants to merge 16 commits into
base: master
Choose a base branch
from

Conversation

Ram81
Copy link
Member

@Ram81 Ram81 commented Feb 24, 2018

@utsavgarg this PR contains initial work for RNN layer support for TF import.
For handling RNN layers the current logic needed to be tweaked. Difference between other layers and RNN layers is that

  • RNN layers are composed of multiple basic ops but they don't have layer name common for all the ops or any other pattern in name for combining ops like we do in conv layers.

  • This PR is based on my observation of each RNN layer ops, it starts with an op named based on TypeofRNNCell+'ZeroState', ex: 'LSTMCellZeroState'. and all the ops following 'rnn' prefix after the ZeroState ops can be used to extract RNN layer parameter.

  • So I use these properties to assign layer type to multiple ops and use them to extract features of layers.

@coveralls
Copy link

coveralls commented Feb 24, 2018

Coverage Status

Coverage decreased (-0.03%) to 94.989% when pulling 986ca7d on Ram81:TF-Import-Support into d700fb0 on Cloud-CV:master.

@Ram81 Ram81 changed the title Extending TF Support for RNN Layers Extending TF Support for RNN & Upsample Layers May 30, 2018
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
Labels
None yet
Projects
None yet
Development

Successfully merging this pull request may close these issues.

2 participants