def test_theano_model(num_classes):
bounds = (0, 255)
channels = num_classes
def mean_brightness_net(images):
logits = T.mean(images, axis=(2, 3))
return logits
images = T.tensor4('images')
logits = mean_brightness_net(images)
model = TheanoModel(images, logits, num_classes=num_classes, bounds=bounds)
test_images = np.random.rand(2, channels, 5, 5).astype(np.float32)
test_label = 7
assert model.forward(test_images).shape \
== (2, num_classes)
test_logits = model.forward_one(test_images[0])
assert test_logits.shape == (num_classes, )
test_gradient = model.gradient_one(test_images[0], test_label)
assert test_gradient.shape == test_images[0].shape
np.testing.assert_almost_equal(
model.forward_and_gradient_one(test_images[0], test_label)[0],
test_logits)
np.testing.assert_almost_equal(
model.forward_and_gradient_one(test_images[0], test_label)[1],
test_gradient)
assert model.num_classes() == num_classes
def test_theano_gradient(num_classes):
bounds = (0, 255)
channels = num_classes
def mean_brightness_net(images):
logits = T.mean(images, axis=(2, 3))
return logits
images = T.tensor4("images")
logits = mean_brightness_net(images)
preprocessing = (
np.arange(num_classes)[:, None, None],
np.random.uniform(size=(channels, 5, 5)) + 1,
)
model = TheanoModel(
images,
logits,
num_classes=num_classes,
preprocessing=preprocessing,
bounds=bounds,
)
# theano and lasagne calculate the cross-entropy from the probbilities
# rather than combining softmax and cross-entropy calculation; they
# therefore have lower numerical accuracy
epsilon = 1e-3
np.random.seed(23)
test_image = np.random.rand(channels, 5, 5).astype(np.float32)
test_label = 7
_, g1 = model.forward_and_gradient_one(test_image, test_label)
l1 = model._loss_fn(test_image[None] - epsilon / 2 * g1, [test_label])
l2 = model._loss_fn(test_image[None] + epsilon / 2 * g1, [test_label])
assert 1e5 * (l2 - l1) > 1
# make sure that gradient is numerically correct
np.testing.assert_array_almost_equal(
1e5 * (l2 - l1), 1e5 * epsilon * np.linalg.norm(g1) ** 2, decimal=1
)