individual tests for DataTransforms

    self.assertEqual(new_train.y.shape, train.y.shape)

    self.assertEqual(new_train.X.shape[-1], fp_size)

  def test_data_transformers(self):

  def __init__(self, d):

    '''

       init to load the MNIST data for DataTransforms Tests

      '''

    mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)

    # extracting validation set of MNIST for testing the DataTransforms

    valid = dc.data.NumpyDataset(mnist.validation.images,

    data = (valid.X)[0]

    # reshaping the vector to image

    data = np.reshape(data, (28, 28))

    self.d = data

    dt = DataTransforms(data)

  def blurring():

    # Check Blurring

    dt = DataTransforms(self.d)

    blurred = dt.gaussian_blur(sigma=1.5)

    check_blur = scipy.ndimage.gaussian_filter(data, 1.5)

    assert np.allclose(check_blur, blurred)

  def rotation():

    # Check rotation

    dt = DataTransforms(self.d)

    angles = [0, 5, 10, 90]

    for ang in angles:

      rotate = dt.rotate(ang)

    check_rotate = scipy.ndimage.rotate(data, 270)

    assert np.allclose(rotate, check_rotate)

  def flipping():

    # Check flip

    dt = DataTransforms(self.d)

    flip_lr = dt.flip(direction="lr")

    flip_ud = dt.flip(direction="ud")

    check_lr = np.fliplr(data)

    check_ud = np.flipud(data)

    assert np.allclose(flip_lr, check_lr)

    assert np.allclose(flip_ud, check_ud)

    assert np.allclose(flip_lr, check_lr)

  def scaling():

    # Check Scales

    dt = DataTransforms(self.d)

    h = 150

    w = 150

    scale = scipy.misc.imresize(data, (h, w))