def test_mnist(self):
(x_train, y_train), (x_test, y_test) = mnist.load_data()
y_train = 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[:200], y_train[:200])
output_shape = mnist_test.output_shape
mnist_test.test(x_test[:200])
mnist_test.evaluate(x_train[:100], y_train[:100])
# create model instance for binary classification
mnist_test = Cifar10CNN()
mnist_test.max_epochs = 1
mnist_test.task = 'binary_classification'
mnist_test.train(x_train[:200], y_train[:200])
prediction = mnist_test.test(x_test[:200])
mnist_test.save('mnist_test')
mnist_reloaded = load_folder("mnist_test")
prediction_loaded = mnist_reloaded.test(x_test[:200])
mnist_reloaded.jacobian_old(x_test[:2])
# Cifar10_CNN is deterministic
np.testing.assert_array_equal(prediction, prediction_loaded)
def test_mnist(self):
(x_train, y_train), (x_test, y_test) = mnist.load_data()
y_train = 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[:200], y_train[:200])
output_shape = mnist_test.output_shape
mnist_test.test(x_test[:200])
mnist_test.evaluate(x_train[:100], y_train[:100])
# create model instance for binary classification
mnist_test = Cifar10CNN()
mnist_test.max_epochs = 1
mnist_test.task = 'binary_classification'
mnist_test.train(x_train[200:400], y_train[200:400].astype(bool))
prediction = mnist_test.test(x_test[200:400])
print("12345: ", mnist_test.input_std)
mnist_test.save('mnist_test')
print("12345: ", mnist_test.input_std)
mnist_reloaded = load_folder("mnist_test")
prediction_loaded = mnist_reloaded.test(x_test[200:400])
mnist_reloaded.jacobian_old(x_test[:2])
eval_result = mnist_reloaded.evaluate(x_test[200:400],
y_train[200:400].astype(bool))
# Cifar10_CNN without dropout is deterministic
np.testing.assert_array_equal(prediction, prediction_loaded)
# test verbose metrics
mnist_reloaded.metrics = ['accuracy']
mnist_reloaded.compile()
print("12345: ", mnist_test.input_std)
mnist_test.save('mnist_test_accuracy')
mnist_reloaded_again = load_folder("mnist_test_accuracy")
eval_result_again = mnist_reloaded_again.evaluate(
x_test[200:400], y_train[200:400])
# assert saving again wont affect the model
self.assertAlmostEqual(eval_result_again['loss'],
eval_result['loss'],
places=3)
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_mnist(self):
# create model instance
mnist_test = Cifar10CNN()
mnist_test.max_epochs = 1
mnist_test.callbacks = ErrorOnNaN()
mnist_test.train(x_train, y_train)
output_shape = mnist_test.output_shape
pred = mnist_test.test(x_test)
test_num = y_test.shape[0]
assert (np.sum(np.argmax(pred, axis=1) == y_test)
) / test_num > 0.9 # assert accurancy
mnist_test.evaluate(x_test, utils.to_categorical(y_test, 10))
# create model instance for binary classification
mnist_test = Cifar10CNN()
mnist_test.max_epochs = 2
mnist_test.task = 'binary_classification'
mnist_test.train(x_train, y_train.astype(bool))
prediction = mnist_test.test(x_test)
assert (np.sum(np.argmax(prediction, axis=1) == y_test)
) / test_num > 0.9 # assert accuracy
mnist_test.save('mnist_test')
mnist_reloaded = load_folder("mnist_test")
prediction_loaded = mnist_reloaded.test(x_test)
eval_result = mnist_reloaded.evaluate(x_test,
utils.to_categorical(y_test, 10))
# Cifar10_CNN without dropout is deterministic
np.testing.assert_array_equal(prediction, prediction_loaded)
# test verbose metrics
mnist_reloaded.metrics = ['accuracy']
mnist_reloaded.compile()
mnist_test.save('mnist_test_accuracy')
mnist_reloaded_again = load_folder("mnist_test_accuracy")
# test with astype boolean deliberately
eval_result_again = mnist_reloaded_again.evaluate(
x_test,
utils.to_categorical(y_test, 10).astype(bool))
# assert saving again wont affect the model
self.assertAlmostEqual(eval_result_again['loss'],
eval_result['loss'],
places=3)
