batch norm: hide statistics from solver, simplifying layer definition

batch norm statistics are not learnable parameters subject to solver
updates, so they must be shielded from the solver. `BatchNorm` layer now
masks its statistics for itself by zeroing parameter learning rates
instead of relying on the layer definition.

n.b. declaring `param`s for batch norm layers is no longer allowed.

include/caffe/layers/batch_norm_layer.hpp

@@ -22,10 +22,8 @@ namespace caffe {
  * mean/variance statistics via a running average, which is then used at test
  * time to allow deterministic outputs for each input. You can manually toggle
  * whether the network is accumulating or using the statistics via the
- * use_global_stats option. IMPORTANT: for this feature to work, you MUST set
- * the learning rate to zero for all three blobs, i.e., param {lr_mult: 0} three
- * times in the layer definition. For reference, these three blobs are (0)
- * mean, (1) variance, and (2) the moving average factor.
+ * use_global_stats option. For reference, these statistics are kept in the
+ * layer's three blobs: (0) mean, (1) variance, and (2) moving average factor.
  *
  * Note that the original paper also included a per-channel learned bias and
  * scaling factor. To implement this in Caffe, define a `ScaleLayer` configured

src/caffe/layers/batch_norm_layer.cpp

@@ -34,6 +34,14 @@ void BatchNormLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
                 this->blobs_[i]->mutable_cpu_data());
     }
   }
+  // Mask statistics from optimization by setting local learning rates
+  // for mean, variance, and the bias correction to zero.
+  CHECK_EQ(this->layer_param_.param_size(), 0)
+      << "Cannot configure batch normalization statistics as layer parameters.";
+  for (int i = 0; i < this->blobs_.size(); ++i) {
+    ParamSpec* fixed_param_spec = this->layer_param_.add_param();
+    fixed_param_spec->set_lr_mult(0.);
+  }
 }
 
 template <typename Dtype>