# 1. layer

### Batch Normalization

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. * 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.