title: nolearn for multi-label classification
body: <p>I tried to use DBN function imported from nolearn package, and here is my code:</p>
  
  <pre><code>from nolearn.dbn import DBN
  import numpy as np
  from sklearn import cross_validation
  
  fileName = 'data.csv'
  fileName_1 = 'label.csv'
  
  data = np.genfromtxt(fileName, dtype=float, delimiter = ',')
  label = np.genfromtxt(fileName_1, dtype=int, delimiter = ',')
  
  clf = DBN(
      [data, 300, 10],
      learn_rates=0.3,
      learn_rate_decays=0.9,
      epochs=10,
      verbose=1,
      )
  
  clf.fit(data,label)
  score = cross_validation.cross_val_score(clf, data, label,scoring='f1', cv=10)
  print score
  </code></pre>
  
  <p>Since my data has the shape(1231, 229) and label with the shape(1231,13), the label sets looks like ([0 0 1 0 1 0 1 0 0 0 1 1 0] ...,[....]), when I ran my code, I got the this error message: bad input shape (1231,13). I wonder two problem might happened here:</p>
  
  <ol>
  <li>DBN does not support multi-label classification</li>
  <li>my label is not suitable to be used in DBN fit function.</li>
  </ol>