anchor.diff:                   *list-of-percents-format*
                    (list (mapcar #'->percent errors)))))))
   
  -(defmethod log-test-error ((trainer mnist-logging-trainer) learner)
  -  (let ((*print-level* nil))
  -    (when (zerop (n-inputs trainer))
  -      (describe learner))
  -    (describe trainer)))
  -
   (defmethod log-test-error ((trainer mnist-rbm-trainer) (rbm mnist-rbm))
     (call-next-method)
     (map nil #'xxx (clouds rbm))
  -  (log-dbn-mean-field-classification-accuracy
  -   rbm
  -   (make-sampler (subseq *training-images* 0 10000)
  -                 :max-n 10000
  -                 :omit-label-p nil)
  -   "TRAINING RECONSTRUCTION")
  -  (log-msg "DBN TEST RMSE: ~{~,5F~^, ~} (~D)~%"
  +  (log-dbn-classification-accuracy rbm (make-training-sampler)
  +                                   "TRAINING RECONSTRUCTION")
  +  (log-msg "DBN TEST RMSE: ~{~,5F~^, ~}~%"
              (map 'list
                   #'get-error
  -                (dbn-mean-field-errors* (make-sampler *test-images*
  -                                                      :max-n
  -                                                      (length *test-images*))
  -                                        (dbn rbm) :rbm rbm))
  -           (n-inputs trainer))
  -  (log-dbn-mean-field-classification-accuracy
  -   rbm
  -   (make-sampler *test-images* :max-n (length *test-images*) :omit-label-p t)
  -   "TEST"))
  +                (dbn-mean-field-errors* (make-test-sampler) (dbn rbm) :rbm rbm)))
  +  (log-dbn-classification-accuracy rbm (make-test-sampler :omit-label-p t)
  +                                   "TEST"))
   
   (defmethod negative-phase :around (batch trainer (rbm mnist-rbm))
     (call-next-method)
  
positive.code: (defmethod log-test-error (trainer (bpn mnist-bpn))
    (call-next-method)
    (multiple-value-bind (e ce)
        (bpn-error (make-sampler *test-images* :max-n (length *test-images*)
                                 :omit-label-p t) bpn)
      (log-msg "TEST CROSS ENTROPY ERROR: ~,5F (~D)~%"
               ce (n-inputs trainer))
      (log-msg "TEST CLASSIFICATION ACCURACY: ~? (~D)~%"
  
negative.code:                                          (floor (n-inputs trainer)
                                                  (length *training-images*)))
                                   *mnist-dir*)
                  dbm)
    (log-dbm-classification-accuracy dbm (make-training-sampler)
                                     "TRAINING RECONSTRUCTION")
    (log-dbm-classification-accuracy dbm (make-training-sampler :omit-label-p t)
                                     "TRAINING")
    ;; This is too time consuming.
    #+nil
    (log-msg "DBM TEST RMSE: ~{~,5F~^, ~}~%"
             (map 'list
                  #'get-error
                  (mnist-dbm-mean-field-errors (make-test-sampler) dbm)))
    (log-dbm-classification-accuracy dbm (make-test-sampler :omit-label-p t)
                                     "TEST"))
  
  (defmethod positive-phase :around (batch trainer (dbm mnist-dbm))
    (call-next-method)
  