[docs] identify batch norm layer blobs

1 files changed, 12 insertions, 11 deletions

diff --git a/include/caffe/layers/batch_norm_layer.hpp b/include/caffe/layers/batch_norm_layer.hpp
index c38c8410..a26ad1a4 100644
--- a/include/caffe/layers/batch_norm_layer.hpp
+++ b/include/caffe/layers/batch_norm_layer.hpp
@@ -13,18 +13,19 @@ namespace caffe {
  * @brief Normalizes the input to have 0-mean and/or unit (1) variance across
  *        the batch.
  *
- * This layer computes Batch Normalization described in [1].  For
- * each channel in the data (i.e. axis 1), it subtracts the mean and divides
- * by the variance, where both statistics are computed across both spatial
- * dimensions and across the different examples in the batch.
+ * This layer computes Batch Normalization as described in [1]. For each channel
+ * in the data (i.e. axis 1), it subtracts the mean and divides by the variance,
+ * where both statistics are computed across both spatial dimensions and across
+ * the different examples in the batch.
  *
- * By default, during training time, the network is computing global 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 parameter blobs, i.e.,
- * param {lr_mult: 0} three times in the layer definition.
+ * By default, during training time, the network is computing global
+ * 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.
  *
  * Note that the original paper also included a per-channel learned bias and
  * scaling factor. To implement this in Caffe, define a `ScaleLayer` configured