anchor.diff:  /5+L1:
   
   (alexandria:mean '(99.04 99.28 99.10 98.92 99.01 99.14 99.16)) => 99.09286
  +
  +/6+L1:
  +2015-02-14 21:19:29: training at n-instances: 180000000
  +2015-02-14 21:19:29: train train+ bpn PREDICTION acc.: 99.76% (60000)
  +2015-02-14 21:19:29: train train+ bpn PREDICTION xent: 7.842d-5 (60000)
  +2015-02-14 21:19:29: test at n-instances: 180000000
  +2015-02-14 21:19:31: pred. test bpn PREDICTION acc.: 99.18% (10000)
  +2015-02-14 21:19:31: pred. test bpn PREDICTION xent: 3.908d-4 (10000)
  +
   
   2014-09-20 12:02:26: n-inputs: 60000000
   2014-09-20 12:02:26: cuda mats: 70307, copies: h->d: 800008, d->h: 2085009
  
positive.code:   (log-msg "test at n-instances: ~S~%" (n-instances optimizer))
    (log-padded
     (let ((bpn (bpn learner)))
       (monitor-bpn-results (make-sampler (test optimizer)) bpn
                            (make-mnist-label-monitors
                             bpn :attributes '(:event "pred." :dataset "test")))))
    (log-mat-room)
    (log-msg "---------------------------------------------------~%"))
  
  
negative.code:                                             :key #'image-label))
                                 :pass-fold t))
  
  (repeatably ()
    (with-example-log ()
      (let ((*default-mat-ctype* :float)
            (*cuda-enabled* t))
        (train-mnist/6 :training (training-images) :test (test-images)))))
  
  (with-example-log ()
    ;; This only works with doubles for now, because boltzmann
  