Should 'gamma' and 'beta' be skipped in L2 regularization?

General Discussion
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#1

Hi all,
I was going through Matterport mask-rcnn code. They are skipping ‘gamma’ and ‘beta’ of Batch norm from L2 regularization.

        # Add L2 Regularization
        # Skip gamma and beta weights of batch normalization layers.
        reg_losses = [
            keras.regularizers.l2(self.config.WEIGHT_DECAY)(w) / tf.cast(tf.size(w), tf.float32)
            for w in self.keras_model.trainable_weights
            if 'gamma' not in w.name and 'beta' not in w.name]

Any reason for this?