anchor.diff:  ;;;; Maxout BPN (see TRAIN-MNIST/4)
   ;;;;
   ;;;; I couldn't quite reproduce the 99.06% claimed in the paper.
  +;;;;
  +;;;;
  +;;;; Max-channel BPN (see TRAIN-MNIST/5)
  +;;;;
  +;;;; 99.16%
   
   (in-package :mgl-example-mnist)
   
  
positive.code:                                    2))
                             :learning-rate (flt learning-rate)
                             :learning-rate-decay (flt learning-rate-decay)
                             :batch-size 100)))
               (when l2-upper-bound
                 (arrange-for-renormalizing-activations
                  bpn trainer l2-upper-bound))
               trainer))
           (make-segmenter (fn)
             (if l2-upper-bound
                 (make-dwim-grouped-segmenter fn)
                 fn)))
      (unless (zerop n-softmax-epochs)
        (log-msg "Starting to train the softmax layer of BPN~%")
        (train (make-sampler training :n-epochs n-softmax-epochs)
  
negative.code: 
  (defun build-maxout-mnist-bpn (&key (n-units-1 1200) (n-units-2 1200)
                                 (group-size 5))
    (build-bpn (:class 'mnist-bpn)
      (inputs (->input :size 784 :dropout (flt 0.2)))
      (f1-activations (add-activations :name 'f1 :inputs '(inputs)
                                       :size n-units-1))
  