def test_tensorflow_model_non_diff(num_classes):
bounds = (0, 255)
channels = num_classes
def mean_brightness_net(images):
logits = tf.reduce_mean(images, axis=(1, 2))
return logits
g = tf.Graph()
with g.as_default():
images = tf.placeholder(tf.float32, (None, 5, 5, channels))
images_nd = tf.cast(images > 0, tf.float32)
logits = mean_brightness_net(images_nd)
with tf.Session(graph=g):
model = TensorFlowModel(
images,
logits,
bounds=bounds)
assert model.session is not None
test_images = np.random.rand(5, 5, channels).astype(np.float32)
test_label = 7
test_gradient = model.gradient(test_images, test_label)
assert (test_gradient == 0).all()
def test_tensorflow_model(num_classes):
bounds = (0, 255)
channels = num_classes
def mean_brightness_net(images):
logits = tf.reduce_mean(images, axis=(1, 2))
return logits
g = tf.Graph()
with g.as_default():
images = tf.placeholder(tf.float32, (None, 5, 5, channels))
logits = mean_brightness_net(images)
with tf.Session(graph=g):
model = TensorFlowModel(
images,
logits,
bounds=bounds)
assert model.session is not None
test_images = np.random.rand(2, 5, 5, channels).astype(np.float32)
test_label = 7
assert model.batch_predictions(test_images).shape \
== (2, num_classes)
test_logits = model.predictions(test_images[0])
assert test_logits.shape == (num_classes,)
test_gradient = model.gradient(test_images[0], test_label)
assert test_gradient.shape == test_images[0].shape
np.testing.assert_almost_equal(
model.predictions_and_gradient(test_images[0], test_label)[0],
test_logits)
np.testing.assert_almost_equal(
model.predictions_and_gradient(test_images[0], test_label)[1],
test_gradient)
assert model.num_classes() == num_classes