Merge pull request BVLC#5 from BVLC/master

Sync: 2015/12/20

examples/finetune_flickr_style/readme.md

@@ -22,10 +22,10 @@ Because we are predicting 20 classes instead of a 1,000, we do need to change th
 Therefore, we change the name of the last layer from `fc8` to `fc8_flickr` in our prototxt.
 Since there is no layer named that in the `bvlc_reference_caffenet`, that layer will begin training with random weights.
 
-We will also decrease the overall learning rate `base_lr` in the solver prototxt, but boost the `blobs_lr` on the newly introduced layer.
+We will also decrease the overall learning rate `base_lr` in the solver prototxt, but boost the `lr_mult` on the newly introduced layer.
 The idea is to have the rest of the model change very slowly with new data, but let the new layer learn fast.
 Additionally, we set `stepsize` in the solver to a lower value than if we were training from scratch, since we're virtually far along in training and therefore want the learning rate to go down faster.
-Note that we could also entirely prevent fine-tuning of all layers other than `fc8_flickr` by setting their `blobs_lr` to 0.
+Note that we could also entirely prevent fine-tuning of all layers other than `fc8_flickr` by setting their `lr_mult` to 0.
 
 ## Procedure