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diff --git a/contrib/torch/nn/SpatialBatchNormalization.lua b/contrib/torch/nn/SpatialBatchNormalization.lua
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+--[[
+   This file implements Batch Normalization as described in the paper:
+   "Batch Normalization: Accelerating Deep Network Training
+                         by Reducing Internal Covariate Shift"
+                by Sergey Ioffe, Christian Szegedy
+
+   This implementation is useful for inputs coming from convolution layers.
+   For non-convolutional layers, see BatchNormalization.lua
+
+   The operation implemented is:
+   y =     ( x - mean(x) )
+        -------------------- * gamma + beta
+        standard-deviation(x)
+   where gamma and beta are learnable parameters.
+
+   The learning of gamma and beta is optional.
+
+   Usage:
+   with    learnable parameters: nn.SpatialBatchNormalization(N [,eps] [,momentum])
+                                 where N = dimensionality of input
+   without learnable parameters: nn.SpatialBatchNormalization(N [,eps] [,momentum], false)
+
+   eps is a small value added to the variance to avoid divide-by-zero.
+       Defaults to 1e-5
+
+   In training time, this layer keeps a running estimate of it's computed mean and std.
+   The running sum is kept with a default momentum of 0.1 (unless over-ridden)
+   In test time, this running mean/std is used to normalize.
+]]--
+local BN, parent = torch.class('nn.SpatialBatchNormalization', 'nn.BatchNormalization')
+
+BN.__version = 2
+
+-- expected dimension of input
+BN.nDim = 4