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