old_contents: from nolearn.dbn import DBN
  from readfacedatabases import *
  
  from sklearn.metrics import classification_report
  
  
  
  parser = argparse.ArgumentParser(description='nolearn test')
  parser.add_argument('--equalize',dest='equalize',action='store_true', default=False,
                      help="if true, the input images are equalized before being fed into the net")
  parser.add_argument('--maxEpochs', type=int, default=1000,
                      help='the maximum number of supervised epochs')
  
  args = parser.parse_args()
  
  
  def KanadeClassifier():
  
    clf = DBN(
        [1200, 1500, 1500, 7],
        learn_rates=0.1,
        learn_rates_pretrain=0.05,
        learn_rate_decays=0.9,
        use_re_lu=True,
        nesterov=True,
        momentum=0.95,
        real_valued_vis=True,
        minibatch_size=20,
        epochs=args.maxEpochs,
        verbose=False
        )
  
    data, labels = readKanade(big, None, equalize=args.equalize)
    data, labels = shuffle(data, labels)
  
    # Split data for training and testing
    kf = cross_validation.KFold(n=len(data), n_folds=5)
    for train, test in kf:
      break
  
    trainData = data[train]
    trainLabels = labels[train]
  
  
    testData = data[test]
    testLabels = labels[test]
  
  
    clf.fit(trainData, trainLabels)
  
    predictedLabels = clf.predicted(testData)
  
    print "Accuracy:", zero_one_score(testLabels, predictedLabels)
    print "Classification report:"
    print classification_report(testLabels, predictedLabels)
  
  if __name__ == '__main__':
    KanadeClassifier()
new_contents: from nolearn.dbn import DBN
  from readfacedatabases import *
  
  from sklearn.metrics import classification_report
  
  import argparse
  
  
  
  parser = argparse.ArgumentParser(description='nolearn test')
  parser.add_argument('--equalize',dest='equalize',action='store_true', default=False,
                      help="if true, the input images are equalized before being fed into the net")
  parser.add_argument('--maxEpochs', type=int, default=1000,
                      help='the maximum number of supervised epochs')
  
  args = parser.parse_args()
  
  
  def KanadeClassifier():
  
    clf = DBN(
        [1200, 1500, 1500, 7],
        learn_rates=0.1,
        learn_rates_pretrain=0.05,
        learn_rate_decays=0.9,
        use_re_lu=True,
        nesterov=True,
        momentum=0.95,
        real_valued_vis=True,
        minibatch_size=20,
        epochs=args.maxEpochs,
        verbose=False
        )
  
    data, labels = readKanade(big, None, equalize=args.equalize)
    data, labels = shuffle(data, labels)
  
    # Split data for training and testing
    kf = cross_validation.KFold(n=len(data), n_folds=5)
    for train, test in kf:
      break
  
    trainData = data[train]
    trainLabels = labels[train]
  
  
    testData = data[test]
    testLabels = labels[test]
  
  
    clf.fit(trainData, trainLabels)
  
    predictedLabels = clf.predicted(testData)
  
    print "Accuracy:", zero_one_score(testLabels, predictedLabels)
    print "Classification report:"
    print classification_report(testLabels, predictedLabels)
  
  if __name__ == '__main__':
    KanadeClassifier()
subject: fix import