def test_color_images(self):
# create model instance
mnist_test = Cifar10CNN()
mnist_test.max_epochs = 1
mnist_test.callbacks = ErrorOnNaN()
mnist_test.train(x_train_color, y_train)
pred = mnist_test.test(x_test_color)
test_num = y_test.shape[0]
assert (np.sum(np.argmax(pred, axis=1) == y_test)) / test_num > 0.9 # assert accuracy
# create model instance for binary classification
mnist_test = Galaxy10CNN()
mnist_test.max_epochs = 1
mnist_test.mc_num = 3
mnist_test.train(x_train[:200], y_train[:200])
prediction = mnist_test.test(x_test[:200])
mnist_test.save('cifar10_test')
mnist_reloaded = load_folder("cifar10_test")
prediction_loaded = mnist_reloaded.test(x_test[:200])
mnist_reloaded.jacobian(x_test[:2], mean_output=True, mc_num=2)
# mnist_reloaded.hessian_diag(x_test[:10], mean_output=True, mc_num=2)
# Cifar10_CNN is deterministic
np.testing.assert_array_equal(prediction, prediction_loaded)
def test_color_images(self):
# test colored 8bit images
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train = np.random.randint(0, 255, size=(1000, 28, 28, 3))
x_test = np.random.randint(0, 255, size=(100, 28, 28, 3))
y_train = y_train[:1000]
y_train = np_utils.to_categorical(y_train, 10)
# To convert to desirable type
x_train = x_train.astype(np.float32)
x_test = x_test.astype(np.float32)
y_train = y_train.astype(np.float32)
# create model instance
mnist_test = Cifar10CNN()
mnist_test.max_epochs = 1
mnist_test.callbacks = ErrorOnNaN()
mnist_test.train(x_train, y_train[:1000])
mnist_test.test(x_test[:1000])
# create model instance for binary classification
mnist_test = Galaxy10CNN()
mnist_test.max_epochs = 1
mnist_test.train(x_train[:1000], y_train[:1000])
prediction = mnist_test.test(x_test[:1000])
mnist_test.save('cifar10_test')
mnist_reloaded = load_folder("cifar10_test")
prediction_loaded = mnist_reloaded.test(x_test[:1000])
mnist_reloaded.jacobian(x_test[:10], mean_output=True, mc_num=2)
# Cifar10_CNN is deterministic
np.testing.assert_array_equal(prediction, prediction_loaded)
def test_nomodel(self):
nomodel = Galaxy10CNN()
self.assertRaises(AttributeError, nomodel.summary)
self.assertRaises(AttributeError, nomodel.save)
self.assertRaises(AttributeError, nomodel.get_weights)
self.assertRaises(AttributeError, nomodel.test, np.zeros(100))