* 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.
*