text: import numpy
  
  import os
  import urllib
  import gzip
  import cPickle as pickle
  
  def mnist_generator(data, batch_size, n_labelled, limit=None):
      images, targets = data
  
      rng_state = numpy.random.get_state()
      numpy.random.shuffle(images)
      numpy.random.set_state(rng_state)
      numpy.random.shuffle(targets)
      if limit is not None:
          print "WARNING ONLY FIRST {} MNIST DIGITS".format(limit)
          images = images.astype('float32')[:limit]
          targets = targets.astype('int32')[:limit]
      if n_labelled is not None:
          labelled = numpy.zeros(len(images), dtype='int32')
          labelled[:n_labelled] = 1
  
      def get_epoch():
          rng_state = numpy.random.get_state()
          numpy.random.shuffle(images)
          numpy.random.set_state(rng_state)
          numpy.random.shuffle(targets)
  
          if n_labelled is not None:
              numpy.random.set_state(rng_state)
              numpy.random.shuffle(labelled)
  
          image_batches = images.reshape(-1, batch_size, 784)
          target_batches = targets.reshape(-1, batch_size)
  
          if n_labelled is not None:
              labelled_batches = labelled.reshape(-1, batch_size)
  
              for i in xrange(len(image_batches)):
                  yield (numpy.copy(image_batches[i]), numpy.copy(target_batches[i]), numpy.copy(labelled))
  
          else:
  
              for i in xrange(len(image_batches)):
                  yield (numpy.copy(image_batches[i]), numpy.copy(target_batches[i]))
  
      return get_epoch
  
  def load(batch_size, test_batch_size, n_labelled=None):
      filepath = '/tmp/mnist.pkl.gz'
      url = 'http://www.iro.umontreal.ca/~lisa/deep/data/mnist/mnist.pkl.gz'
  
      if not os.path.isfile(filepath):
          print "Couldn't find MNIST dataset in /tmp, downloading..."
          urllib.urlretrieve(url, filepath)
  
      with gzip.open('/tmp/mnist.pkl.gz', 'rb') as f:
          train_data, dev_data, test_data = pickle.load(f)
  
      return (
          mnist_generator(train_data, batch_size, n_labelled), 
          mnist_generator(dev_data, test_batch_size, n_labelled), 
          mnist_generator(test_data, test_batch_size, n_labelled)
      )