anchor.diff:  ;;;;
   ;;;;   "Deep Boltzmann Machines",
   ;;;;   http://www.cs.toronto.edu/~hinton/absps/dbm.pdf
  +;;;;
  +;;;; For dropout:
  +;;;;
  +;;;;   "Improving neural networks by preventing co-adaptation of
  +;;;;   feature detectors"
  +;;;;   http://arxiv.org/pdf/1207.0580.pdf
  +;;;;
  +;;;; Maxout:
  +;;;;
  +;;;;  "Maxout Networks"
  +;;;;  http://arxiv.org/abs/1302.4389
   ;;;;
   ;;;; Download the four files from http://yann.lecun.com/exdb/mnist and
   ;;;; gunzip them. Set *MNIST-DATA-DIR* to point to their directory and
  
positive.code: ;;;; The new, 2000->10 connections in the backprop network are trained
  ;;;; for a few batches and finally all weights are trained together.
  ;;;; Takes less than two days to train on a 2.16GHz Core Duo and
  ;;;; reaches ~98.86% accuracy.
  ;;;;
  ;;;; During DBN training the DBN TEST RMSE is the RMSE of the mean
  ;;;; field reconstruction of the test images while TRAINING RMSE is of
  
negative.code: (defclass mnist-bpn-gd-trainer (mnist-bp-trainer segmented-gd-trainer)
    ())
  
  (defclass mnist-bpn-gd-segment-trainer (batch-gd-trainer)
    ((n-inputs-in-epoch :initarg :n-inputs-in-epoch :reader n-inputs-in-epoch)
     (n-epochs-to-reach-final-momentum
  