text: # coding: utf-8
  try:
      import urllib.request
  except ImportError:
      raise ImportError('You should use Python 3.x')
  import os.path
  import gzip
  import pickle
  import os
  import numpy as np
  
  
  url_base = 'http://yann.lecun.com/exdb/mnist/'
  key_file = {
      'train_img':'train-images-idx3-ubyte.gz',
      'train_label':'train-labels-idx1-ubyte.gz',
      'test_img':'t10k-images-idx3-ubyte.gz',
      'test_label':'t10k-labels-idx1-ubyte.gz'
  }
  
  dataset_dir = os.path.dirname(os.path.abspath(__file__))
  save_file = dataset_dir + "/mnist.pkl"
  
  train_num = 60000
  test_num = 10000
  img_dim = (1, 28, 28)
  img_size = 784
  
  
  def _download(file_name):
      file_path = dataset_dir + "/" + file_name
      
      if os.path.exists(file_path):
          return
  
      print("Downloading " + file_name + " ... ")
      urllib.request.urlretrieve(url_base + file_name, file_path)
      print("Done")
      
  def download_mnist():
      for v in key_file.values():
         _download(v)
          
  def _load_label(file_name):
      file_path = dataset_dir + "/" + file_name
      
      print("Converting " + file_name + " to NumPy Array ...")
      with gzip.open(file_path, 'rb') as f:
              labels = np.frombuffer(f.read(), np.uint8, offset=8)
      print("Done")
      
      return labels
  
  def _load_img(file_name):
      file_path = dataset_dir + "/" + file_name
      
      print("Converting " + file_name + " to NumPy Array ...")    
      with gzip.open(file_path, 'rb') as f:
              data = np.frombuffer(f.read(), np.uint8, offset=16)
      data = data.reshape(-1, img_size)
      print("Done")
      
      return data
      
  def _convert_numpy():
      dataset = {}
      dataset['train_img'] =  _load_img(key_file['train_img'])
      dataset['train_label'] = _load_label(key_file['train_label'])    
      dataset['test_img'] = _load_img(key_file['test_img'])
      dataset['test_label'] = _load_label(key_file['test_label'])
      
      return dataset
  
  def init_mnist():
      download_mnist()
      dataset = _convert_numpy()
      print("Creating pickle file ...")
      with open(save_file, 'wb') as f:
          pickle.dump(dataset, f, -1)
      print("Done!")
  
  def _change_ont_hot_label(X):
      T = np.zeros((X.size, 10))
      for idx, row in enumerate(T):
          row[X[idx]] = 1
          
      return T
      
  
  def load_mnist(normalize=True, flatten=True, one_hot_label=False):
      """MNIST 데이터셋 읽기
      
      Parameters
      ----------
      normalize : 이미지의 픽셀 값을 0.0~1.0 사이의 값으로 정규화할지 정한다.
      one_hot_label : 
          one_hot_label이 True면、레이블을 원-핫(one-hot) 배열로 돌려준다.
          one-hot 배열은 예를 들어 [0,0,1,0,0,0,0,0,0,0]처럼 한 원소만 1인 배열이다.
      flatten : 입력 이미지를 1차원 배열로 만들지를 정한다. 
      
      Returns
      -------
      (훈련 이미지, 훈련 레이블), (시험 이미지, 시험 레이블)
      """
      if not os.path.exists(save_file):
          init_mnist()
          
      with open(save_file, 'rb') as f:
          dataset = pickle.load(f)
      
      if normalize:
          for key in ('train_img', 'test_img'):
              dataset[key] = dataset[key].astype(np.float32)
              dataset[key] /= 255.0
              
      if one_hot_label:
          dataset['train_label'] = _change_ont_hot_label(dataset['train_label'])
          dataset['test_label'] = _change_ont_hot_label(dataset['test_label'])    
      
      if not flatten:
           for key in ('train_img', 'test_img'):
              dataset[key] = dataset[key].reshape(-1, 1, 28, 28)
  
      return (dataset['train_img'], dataset['train_label']), (dataset['test_img'], dataset['test_label']) 
  
  
  if __name__ == '__main__':
      init_mnist()
  
  