DL4J MLPMnistSingleLayerExample with ABCL and JSS

dl4j-mnist.lisp

(in-package :asdf)

(defsystem :dl4j :defsystem-depends-on (:abcl-asdf)
	   :components ((:mvn "org.deeplearning4j/deeplearning4j-core" :version "0.8.0")
			(:mvn "org.nd4j/nd4j-native" :version "0.8.0")))

(asdf:load-system :dl4j)

(in-package :jss)

(defparameter *mnist-train* (new 'MnistDataSetIterator 128 +true+ 123))
(defparameter *mnist-test* (new 'MnistDataSetIterator 128 +false+ 123))

(format t "Build model....~%")
(defparameter *conf*
  (let* ((conf (new 'NeuralNetConfiguration$Builder))
	 (denselayer (new 'DenseLayer$Builder))
	 (output (new 'OutputLayer$Builder)))
  ;;; make dense layer
    (#"nIn" denselayer (* 28 28))
    (#"nOut" denselayer 1000)
    (#"activation" denselayer (#"valueOf" 'org.nd4j.linalg.activations.Activation "RELU"))
    (#"weightInit" denselayer (#"valueOf" 'org.deeplearning4j.nn.weights.WeightInit "XAVIER"))
  ;;; make output layer
    (#"nIn" output 1000)
    (#"nOut" output 10)
    (#"activation" output (#"valueOf" 'org.nd4j.linalg.activations.Activation "SOFTMAX"))
    (#"weightInit" output (#"valueOf" 'org.deeplearning4j.nn.weights.WeightInit "XAVIER"))
  ;; DL4J documentation is wrong
    (#"lossFunction" output
		     (#"valueOf" 'LossFunctions$LossFunction "NEGATIVELOGLIKELIHOOD"))
  ;;; make conf
    (#"seed" conf 123)
    (#"optimizationAlgo" conf (#"valueOf" 'org.deeplearning4j.nn.api.OptimizationAlgorithm "STOCHASTIC_GRADIENT_DESCENT"))
    (#"iterations" conf 1)
    (#"learningRate" conf 0.006)
    (#"updater" conf (#"valueOf" 'org.deeplearning4j.nn.conf.Updater "NESTEROVS"))
    (#"momentum" conf 0.9)
    (#"regularization" conf +true+)
    (#"l2" conf 1e-4)
    (let ((listbuilder (#"list" conf)))
      (#"layer" listbuilder 0 (#"build" denselayer))
      (#"layer" listbuilder 1 (#"build" output))
      (#"pretrain" listbuilder +false+)
      (#"build" listbuilder))))

(defparameter *model* (new 'MultiLayerNetwork *conf*))
(#"init" *model*)

;; not sure why this doesn't work
;; (#"setListeners" *model* (new 'ScoreIterationListener 1))

(format t "Train model....~%")
(dotimes (epoch 15)
  (#"fit" *model* *mnist-train*)
  (format t "End of training epoch ~a, current model score: ~a~%"
	  (1+ epoch)
	  (#"score" *model*)))

(format t "Evaluate model....~%")
(defparameter *eval* (new 'Evaluation 10))

(loop while (#"hasNext" *mnist-test*)
   do (let* ((next (#"next" *mnist-test*))
	     (output (#"output" *model* (#"getFeatureMatrix" next))))
	(#"eval" *eval* (#"getLabels" next) output)))

(format t "~a~%****************Example finished********************" (#"stats" *eval*))