parameters: ()
return_statement: return (x_train, y_train), (x_test, y_test)
docstring: Loads CIFAR10 dataset.
  
    Returns:
        Tuple of Numpy arrays: `(x_train, y_train), (x_test, y_test)`.
docstring_summary: Loads CIFAR10 dataset.
docstring_tokens: Loads CIFAR10 dataset .
function: def load_data():
    """Loads CIFAR10 dataset.
  
    Returns:
        Tuple of Numpy arrays: `(x_train, y_train), (x_test, y_test)`.
    """
    dirname = 'cifar-10-batches-py'
    origin = 'http://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz'
    path = get_file(dirname, origin=origin, untar=True)
  
    num_train_samples = 50000
  
    x_train = np.zeros((num_train_samples, 3, 32, 32), dtype='uint8')
    y_train = np.zeros((num_train_samples,), dtype='uint8')
  
    for i in range(1, 6):
      fpath = os.path.join(path, 'data_batch_' + str(i))
      data, labels = load_batch(fpath)
      x_train[(i - 1) * 10000:i * 10000, :, :, :] = data
      y_train[(i - 1) * 10000:i * 10000] = labels
  
    fpath = os.path.join(path, 'test_batch')
    x_test, y_test = load_batch(fpath)
  
    y_train = np.reshape(y_train, (len(y_train), 1))
    y_test = np.reshape(y_test, (len(y_test), 1))
  
    if K.image_data_format() == 'channels_last':
      x_train = x_train.transpose(0, 2, 3, 1)
      x_test = x_test.transpose(0, 2, 3, 1)
  
    return (x_train, y_train), (x_test, y_test)
function_tokens: def load_data ( ) : dirname = 'cifar-10-batches-py' origin = 'http://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz' path = get_file ( dirname , origin = origin , untar = True ) num_train_samples = 50000 x_train = np . zeros ( ( num_train_samples , 3 , 32 , 32 ) , dtype = 'uint8' ) y_train = np . zeros ( ( num_train_samples , ) , dtype = 'uint8' ) for i in range ( 1 , 6 ) : fpath = os . path . join ( path , 'data_batch_' + str ( i ) ) data , labels = load_batch ( fpath ) x_train [ ( i - 1 ) * 10000 : i * 10000 , : , : , : ] = data y_train [ ( i - 1 ) * 10000 : i * 10000 ] = labels fpath = os . path . join ( path , 'test_batch' ) x_test , y_test = load_batch ( fpath ) y_train = np . reshape ( y_train , ( len ( y_train ) , 1 ) ) y_test = np . reshape ( y_test , ( len ( y_test ) , 1 ) ) if K . image_data_format ( ) == 'channels_last' : x_train = x_train . transpose ( 0 , 2 , 3 , 1 ) x_test = x_test . transpose ( 0 , 2 , 3 , 1 ) return ( x_train , y_train ) , ( x_test , y_test )