code: def load_mnist(training_num=50000):
      
      data_path = os.path.join(os.path.dirname(os.path.realpath('__file__')), 'mnist.npz')
      if not os.path.isfile(data_path):
          from six.moves import urllib
          origin = (
              'https://github.com/sxjscience/mxnet/raw/master/example/bayesian-methods/mnist.npz'
          )
          print('Downloading data from %s to %s' % (origin, data_path))
          ctx = ssl._create_unverified_context()
          with urllib.request.urlopen(origin, context=ctx) as u, open(data_path, 'wb') as f:
              f.write(u.read())
          print('Done!')
      dat = numpy.load(data_path)
      X = (dat['X'][:training_num] / 126.0).astype('float32')
      Y = dat['Y'][:training_num]
      X_test = (dat['X_test'] / 126.0).astype('float32')
      Y_test = dat['Y_test']
      Y = Y.reshape((Y.shape[0],))
      Y_test = Y_test.reshape((Y_test.shape[0],))
      return X, Y, X_test, Y_test
docs: Load mnist dataset