text: import numpy as np
  import scipy.misc
  import time
  
  def make_generator(path, n_files, batch_size):
      epoch_count = [1]
      def get_epoch():
          images = np.zeros((batch_size, 3, 64, 64), dtype='int32')
          files = range(n_files)
          random_state = np.random.RandomState(epoch_count[0])
          random_state.shuffle(files)
          epoch_count[0] += 1
          for n, i in enumerate(files):
              image = scipy.misc.imread("{}/{}.png".format(path, str(i+1).zfill(len(str(n_files)))))
              images[n % batch_size] = image.transpose(2,0,1)
              if n > 0 and n % batch_size == 0:
                  yield (images,)
      return get_epoch
  
  def load(batch_size, data_dir='/home/ishaan/data/imagenet64'):
      return (
          make_generator(data_dir+'/train_64x64', 1281149, batch_size),
          make_generator(data_dir+'/valid_64x64', 49999, batch_size)
      )
  
  if __name__ == '__main__':
      train_gen, valid_gen = load(64)
      t0 = time.time()
      for i, batch in enumerate(train_gen(), start=1):
          print("{}\t{}".format(str(time.time() - t0), batch[0][0,0,0,0]))
          if i == 1000:
              break
          t0 = time.time()