anchor.diff:       for max-iterations in max-iterations-s
        for randomize-weights in randomize-weights-s
        for annealing in annealings
  -     for taf = (format nil "~a-~a.~a" tafno id extension)
  +     for taf = (join-paths path (format nil "~a-~a.~a" tafno id extension))
        collect
          (lambda ()
            (let ((result (train-n-test :training-file training-file
  
positive.code: 
  (defun try-models (&key
                       (path (home-based "common-lisp/dc-ann"))
                       (training-file
                        (home-based "circle-training-data.csv"))
                       (test-file
                        (home-based "circle-training-data.csv"))
                       (trained-ann-file
                        (home-based "circle-ann-frozen.dat"))
                       ids
                       topologies
                       learning-rates
  
negative.code:              (push result *model-results*))
             nil))
       into job-queue
       finally (return (thread-pool-start :try-models
                                          (min thread-count (length ids))
                                          job-queue
                                          (lambda (f) (funcall f))
                                          (lambda () (format t "All done!~%"))))))
  
  ;; (try-models :ids '(1 2 3 4)
  ;;             :topologies '((2 20 10 5 1)
  ;;                           (2 100 1)
  ;;                           (2 32 8 2 1)
  ;;                           (2 30 1))
  ;;             :learning-rates '(0.1 0.1 0.1 0.1)
  ;;             :momenti '(0.3 0.3 0.3 0.3)
  ;;             :transfer-functions '(#'bound-sigmoid 
  ;;                                   #'bound-sigmoid
  ;;                                   #'bound-sigmoid
  ;;                                   #'bound-sigmoid)
  ;;             :transfer-derivatives '(#'bound-sigmoid-derivative
  ;;                                     #'bound-sigmoid-derivative
  ;;                                     #'bound-sigmoid-derivative
  ;;                                     #'bound-sigmoid-derivative)
  ;;             :target-mses '(0.05 0.05 0.05 0.05)
  ;;             :max-iterations-s '(1000000 1000000 1000000 1000000)
  ;;             :randomize-weights-s '(:min -0.5 :max 0.5)
  ;;             :annealings '(nil nil nil nil))
  