Adding sparse Lp regularization operator to Caffe2 #38574
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This pull request was exported from Phabricator. Differential Revision: D21003029 |
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Summary: Pull Request resolved: pytorch#38574 Adding sparse L1 and L2 regularization operator to Caffe2. This doesn't work using run_on_loss, only run_after_optimize. Applying it to run_after_optimize rather than run_on_loss was easier to implement, particularly for the L1 norm which is preferable in some cases and is non-differentiable at zero. Test Plan: Wrote and ran unit tests in operator_test:sparse_lp_regularizer_test. Differential Revision: D21003029 fbshipit-source-id: 2a82b76e8c349fbb7bd2692ecb39bd7d395cbeec
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This pull request has been merged in 7f1a96d. |
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Summary:
Adding sparse L1 and L2 regularization operator to Caffe2. This doesn't work using run_on_loss, only run_after_optimize. Applying it to run_after_optimize rather than run_on_loss was easier to implement, particularly for the L1 norm which is preferable in some cases and is non-differentiable at zero.
This diff has been copied from D6735673 and modified.
Test Plan:
Wrote and ran unit tests:
buck test mode/dev //caffe2/caffe2/python/operator_test:sparse_lp_regularizer_test
Differential Revision: D21003029