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authorPrzemysław Dolata <snowball91b@gmail.com>2018-02-06 07:46:25 (GMT)
committerGitHub <noreply@github.com>2018-02-06 07:46:25 (GMT)
commit87e151281d853afdb281e2249620cf839bb932d1 (patch)
tree22f1f1b0c04c13aff554b91fa23fe95ea4f7b048
parentcc521a0801143c242f5da0e95737070c02ce15ab (diff)
parent0cb449e8c766499b29e5314120068ee9c8ebd71e (diff)
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Merge pull request #5598 from ZoroDerVonCodier/patch-1
Update euclidean_loss_layer.hpp with corrected reference in comment
-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.hpp
index 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.
*