def test_keras_backward(num_classes):
bounds = (0, 255)
channels = num_classes
model = Sequential()
with warnings.catch_warnings():
warnings.filterwarnings("ignore", category=DeprecationWarning)
model.add(
GlobalAveragePooling2D(data_format="channels_last",
input_shape=(5, 5, channels)))
model = KerasModel(model, bounds=bounds, predicts="logits")
test_image = np.random.rand(5, 5, channels).astype(np.float32)
test_grad_pre = np.random.rand(num_classes).astype(np.float32)
test_grad = model.backward_one(test_grad_pre, test_image)
assert test_grad.shape == test_image.shape
manual_grad = np.repeat(np.repeat((test_grad_pre / 25.0).reshape(
(1, 1, -1)),
5,
axis=0),
5,
axis=1)
np.testing.assert_almost_equal(test_grad, manual_grad)
