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author committer Dave Brown 2017-05-08 01:25:27 (GMT) GitHub 2017-05-08 01:25:27 (GMT) 0cb449e8c766499b29e5314120068ee9c8ebd71e (patch) f6330a4c5f3b48950b06b64704b064e728b8e259 7d3f8a7ea43fb06cd9804bc90933c7a91cd88ec9 (diff) caffe-0cb449e8c766499b29e5314120068ee9c8ebd71e.zipcaffe-0cb449e8c766499b29e5314120068ee9c8ebd71e.tar.gzcaffe-0cb449e8c766499b29e5314120068ee9c8ebd71e.tar.bz2
Update euclidean_loss_layer.hpp
-rw-r--r--include/caffe/layers/euclidean_loss_layer.hpp2
1 files changed, 1 insertions, 1 deletions
 diff --git a/include/caffe/layers/euclidean_loss_layer.hpp b/include/caffe/layers/euclidean_loss_layer.hppindex f564569..24568c5 100644--- a/include/caffe/layers/euclidean_loss_layer.hpp+++ b/include/caffe/layers/euclidean_loss_layer.hpp@@ -30,7 +30,7 @@ namespace caffe { * This can be used for least-squares regression tasks. An InnerProductLayer * input to a EuclideanLossLayer exactly formulates a linear least squares * regression problem. With non-zero weight decay the problem becomes one of- * ridge regression -- see src/caffe/test/test_sgd_solver.cpp for a concrete+ * ridge regression -- see src/caffe/test/test_gradient_based_solver.cpp for a concrete * example wherein we check that the gradients computed for a Net with exactly * this structure match hand-computed gradient formulas for ridge regression. *