Deep learning framework by BAIR

Created by
Yangqing Jia
Lead Developer
Evan Shelhamer

Batch Norm Layer


message BatchNormParameter {
  // If false, normalization is performed over the current mini-batch
  // and global statistics are accumulated (but not yet used) by a moving
  // average.
  // If true, those accumulated mean and variance values are used for the
  // normalization.
  // By default, it is set to false when the network is in the training
  // phase and true when the network is in the testing phase.
  optional bool use_global_stats = 1;
  // What fraction of the moving average remains each iteration?
  // Smaller values make the moving average decay faster, giving more
  // weight to the recent values.
  // Each iteration updates the moving average @f$S_{t-1}@f$ with the
  // current mean @f$ Y_t @f$ by
  // @f$ S_t = (1-\beta)Y_t + \beta \cdot S_{t-1} @f$, where @f$ \beta @f$
  // is the moving_average_fraction parameter.
  optional float moving_average_fraction = 2 [default = .999];
  // Small value to add to the variance estimate so that we don't divide by
  // zero.
  optional float eps = 3 [default = 1e-5];