()
return (shuffle(training_data), validation_data, test_data)
Loads the dataset.

  Looks for the dataset at /tmp/mnist.pkl.gz and downloads it if it is not there
  already.

  Note: The training data is shuffled.

  Returns:
    ((train_x, train_y), (valid_x, valid_y), (test_x, test_y)).
    Shapes:
      train_x: num_training_examples x image_size
      train_y: num_training_examples x num_classes
      valid_x: num_validation_examples x image_size
      valid_y: num_validation_examples x num_classes
      test_x: num_test_examples x image_size
      test_y: num_test_examples x num_classes
Loads the dataset.
Loads the dataset .
def load():
  """Loads the dataset.

  Looks for the dataset at /tmp/mnist.pkl.gz and downloads it if it is not there
  already.

  Note: The training data is shuffled.

  Returns:
    ((train_x, train_y), (valid_x, valid_y), (test_x, test_y)).
    Shapes:
      train_x: num_training_examples x image_size
      train_y: num_training_examples x num_classes
      valid_x: num_validation_examples x image_size
      valid_y: num_validation_examples x num_classes
      test_x: num_test_examples x image_size
      test_y: num_test_examples x num_classes
  """
  filepath = _maybe_download()
  with gzip.open(os.path.join(filepath), 'rb') as f:
    training_data, validation_data, test_data = pickle.load(f)
  training_data = (training_data[0], [to_one_hot(x) for x in training_data[1]])
  validation_data = (validation_data[0],
                     [to_one_hot(x) for x in validation_data[1]])
  test_data = (test_data[0], [to_one_hot(x) for x in test_data[1]])

  def shuffle(data):
    zipped = zip(*data)
    random.shuffle(zipped)
    return zip(*zipped)

  return (shuffle(training_data), validation_data, test_data)
def load ( ) : filepath = _maybe_download ( ) with gzip . open ( os . path . join ( filepath ) , 'rb' ) as f : training_data , validation_data , test_data = pickle . load ( f ) training_data = ( training_data [ 0 ] , [ to_one_hot ( x ) for x in training_data [ 1 ] ] ) validation_data = ( validation_data [ 0 ] , [ to_one_hot ( x ) for x in validation_data [ 1 ] ] ) test_data = ( test_data [ 0 ] , [ to_one_hot ( x ) for x in test_data [ 1 ] ] ) def shuffle ( data ) : zipped = zip ( * data ) random . shuffle ( zipped ) return zip ( * zipped ) return ( shuffle ( training_data ) , validation_data , test_data )