!25 update release note and lenet5_dp_model_train.py

Merge pull request !25 from zheng-huanhuan/dp_pynative

RELEASE.md

+# Release 0.3.0-alpha
+
+## Major Features and Improvements
+
+### Differential Privacy Model Training
+
+Differential Privacy is coming! By using Differential-Privacy-Optimizers, one can still train a model as usual, while the trained model preserved the privacy of training dataset, satisfying the definition of
+differential privacy with proper budget.
+* Optimizers with Differential Privacy([PR23](https://gitee.com/mindspore/mindarmour/pulls/23), [PR24](https://gitee.com/mindspore/mindarmour/pulls/24))
+    * Some common optimizers now have a differential privacy version (SGD/
+    Adam). We are adding more.
+    * Automatically and adaptively add Gaussian Noise during training to achieve Differential Privacy.
+    * Automatically stop training when Differential Privacy Budget exceeds.
+* Differential Privacy Monitor([PR22](https://gitee.com/mindspore/mindarmour/pulls/22))
+    * Calculate overall budget consumed during training, indicating the ultimate protect effect.
+## Bug fixes
+## Contributors
+Thanks goes to these wonderful people: 
+Liu Liu, Huanhuan Zheng, Zhidan Liu, Xiulang Jin
+Contributions of any kind are welcome!
+
+# Release 0.2.0-alpha
+## Major Features and Improvements
+- Add a white-box attack method: M-DI2-FGSM([PR14](https://gitee.com/mindspore/mindarmour/pulls/14)).
+- Add three neuron coverage metrics: KMNCov, NBCov, SNACov([PR12](https://gitee.com/mindspore/mindarmour/pulls/12)).
+- Add a coverage-guided fuzzing test framework for deep neural networks([PR13](https://gitee.com/mindspore/mindarmour/pulls/13)).
+- Update the MNIST Lenet5 examples.
+- Remove some duplicate code.
+
+## Bug fixes
+## Contributors
+Thanks goes to these wonderful people:
+Liu Liu, Huanhuan Zheng, Zhidan Liu, Xiulang Jin
+Contributions of any kind are welcome!
+
 # Release 0.1.0-alpha
 
 Initial release of MindArmour.
@@ -8,4 +43,4 @@ Initial release of MindArmour.
 - Include 13 white-box and 7 black-box attack methods.
 - Provide 5 detection algorithms to detect attacking in multiple way.
 - Provide adversarial training to enhance model security.
-- Provide 6 evaluation metrics for attack methods and 9 evaluation metrics for defense methods.
\ No newline at end of file
+- Provide 6 evaluation metrics for attack methods and 9 evaluation metrics for defense methods.

example/mnist_demo/lenet5_dp_model_train.py

@@ -37,7 +37,6 @@ from mindarmour.utils.logger import LogUtil
 from lenet5_net import LeNet5
 from lenet5_config import mnist_cfg as cfg
 
-
 LOGGER = LogUtil.get_instance()
 TAG = 'Lenet5_train'
 
@@ -121,9 +120,9 @@ if __name__ == "__main__":
     gaussian_mech.set_mechanisms('Gaussian',
                                  norm_bound=args.l2_norm_bound,
                                  initial_noise_multiplier=args.initial_noise_multiplier)
-    net_opt = gaussian_mech.create('Momentum')(params=network.trainable_params(),
-                                               learning_rate=cfg.lr,
-                                               momentum=cfg.momentum)
+    net_opt = gaussian_mech.create('SGD')(params=network.trainable_params(),
+                                          learning_rate=cfg.lr,
+                                          momentum=cfg.momentum)
     micro_size = int(cfg.batch_size // args.micro_batches)
     rdp_monitor = PrivacyMonitorFactory.create('rdp',
                                                num_samples=60000,

setup.py

@@ -18,7 +18,7 @@ from setuptools import setup
 from setuptools.command.egg_info import egg_info
 from setuptools.command.build_py import build_py
 
-version = '0.2.0'
+version = '0.3.0'
 cur_dir = os.path.dirname(os.path.realpath(__file__))
 pkg_dir = os.path.join(cur_dir, 'build')