diff --git a/example/mnist_demo/lenet5_config.py b/example/mnist_demo/lenet5_config.py
index 69e8efac63ddc9aff83520660797f6d749a5d8cf..e9ac0480a560d31d14ff6821fab95d28733ec767 100644
--- a/example/mnist_demo/lenet5_config.py
+++ b/example/mnist_demo/lenet5_config.py
@@ -33,7 +33,7 @@ mnist_cfg = edict({
     'dataset_sink_mode': False,  # whether deliver all training data to device one time
     'micro_batches': 16,  # the number of small batches split from an original batch
     'norm_clip': 1.0,  # the clip bound of the gradients of model's training parameters
-    'initial_noise_multiplier': 0.2,  # the initial multiplication coefficient of the noise added to training
+    'initial_noise_multiplier': 1.5,  # the initial multiplication coefficient of the noise added to training
     # parameters' gradients
     'mechanisms': 'AdaGaussian',  # the method of adding noise in gradients while training
     'optimizer': 'Momentum'  # the base optimizer used for Differential privacy training
diff --git a/example/mnist_demo/lenet5_dp.py b/example/mnist_demo/lenet5_dp.py
index c86bf1bcc0523aa288904b4da66e5a9e8410b5b9..bf62fd3155c71a06a14a7d109bfbd7c4d09b0078 100644
--- a/example/mnist_demo/lenet5_dp.py
+++ b/example/mnist_demo/lenet5_dp.py
@@ -87,7 +87,7 @@ def generate_mnist_dataset(data_path, batch_size=32, repeat_size=1,
 
 if __name__ == "__main__":
     # This configure can run both in pynative mode and graph mode
-    context.set_context(mode=context.PYNATIVE_MODE, device_target=cfg.device_target)
+    context.set_context(mode=context.GRAPH_MODE, device_target=cfg.device_target)
     network = LeNet5()
     net_loss = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True, reduction="mean")
     config_ck = CheckpointConfig(save_checkpoint_steps=cfg.save_checkpoint_steps,
diff --git a/mindarmour/diff_privacy/mechanisms/mechanisms.py b/mindarmour/diff_privacy/mechanisms/mechanisms.py
index cdcf3a5a64461b70fe8d5c9b3e16ec615493151b..d58724f986f37569013aa92dd28c44fdbc133aa5 100644
--- a/mindarmour/diff_privacy/mechanisms/mechanisms.py
+++ b/mindarmour/diff_privacy/mechanisms/mechanisms.py
@@ -37,8 +37,8 @@ class MechanismsFactory:
         """
         Args:
             policy(str): Noise generated strategy, could be 'Gaussian' or
-                'AdaGaussian'. Noise would be decayed with 'AdaGaussian' mechanism while
-                be constant with 'Gaussian' mechanism. Default: 'AdaGaussian'.
+                'AdaGaussian'. Noise would be decayed with 'AdaGaussian' mechanism
+                while be constant with 'Gaussian' mechanism.
             args(Union[float, str]): Parameters used for creating noise
                 mechanisms.
             kwargs(Union[float, str]): Parameters used for creating noise
@@ -74,7 +74,7 @@ class GaussianRandom(Mechanisms):
 
     Args:
         norm_bound(float): Clipping bound for the l2 norm of the gradients.
-            Default: 1.0.
+            Default: 0.5.
         initial_noise_multiplier(float): Ratio of the standard deviation of
             Gaussian noise divided by the norm_bound, which will be used to
             calculate privacy spent. Default: 1.5.
@@ -86,14 +86,14 @@ class GaussianRandom(Mechanisms):
 
     Examples:
         >>> gradients = Tensor([0.2, 0.9], mstype.float32)
-        >>> norm_bound = 1.0
-        >>> initial_noise_multiplier = 0.1
+        >>> norm_bound = 0.5
+        >>> initial_noise_multiplier = 1.5
         >>> net = GaussianRandom(norm_bound, initial_noise_multiplier)
         >>> res = net(gradients)
         >>> print(res)
     """
 
-    def __init__(self, norm_bound=1.0, initial_noise_multiplier=1.5, mean=0.0, seed=0):
+    def __init__(self, norm_bound=0.5, initial_noise_multiplier=1.5, mean=0.0, seed=0):
         super(GaussianRandom, self).__init__()
         self._norm_bound = check_value_positive('norm_bound', norm_bound)
         self._norm_bound = Tensor(norm_bound, mstype.float32)
@@ -128,10 +128,10 @@ class AdaGaussianRandom(Mechanisms):
 
     Args:
         norm_bound(float): Clipping bound for the l2 norm of the gradients.
-            Default: 1.5.
+            Default: 1.0.
         initial_noise_multiplier(float): Ratio of the standard deviation of
             Gaussian noise divided by the norm_bound, which will be used to
-            calculate privacy spent. Default: 5.0.
+            calculate privacy spent. Default: 1.5.
         mean(float): Average value of random noise. Default: 0.0
         noise_decay_rate(float): Hyper parameter for controlling the noise decay.
             Default: 6e-4.
@@ -145,7 +145,7 @@ class AdaGaussianRandom(Mechanisms):
     Examples:
         >>> gradients = Tensor([0.2, 0.9], mstype.float32)
         >>> norm_bound = 1.0
-        >>> initial_noise_multiplier = 5.0
+        >>> initial_noise_multiplier = 1.5
         >>> mean = 0.0
         >>> noise_decay_rate = 6e-4
         >>> decay_policy = "Time"
@@ -155,7 +155,7 @@ class AdaGaussianRandom(Mechanisms):
         >>> print(res)
     """
 
-    def __init__(self, norm_bound=1.5, initial_noise_multiplier=5.0, mean=0.0,
+    def __init__(self, norm_bound=1.0, initial_noise_multiplier=1.5, mean=0.0,
                  noise_decay_rate=6e-4, decay_policy='Time', seed=0):
         super(AdaGaussianRandom, self).__init__()
         norm_bound = check_value_positive('norm_bound', norm_bound)