diff --git a/example/mnist_demo/mnist_train.py b/example/mnist_demo/mnist_train.py
index 7e595b4e57c19b31f8ee844638490f7fffe211ba..d44fe481fc4bf9a878b8199734d8b5bf6139ec7f 100644
--- a/example/mnist_demo/mnist_train.py
+++ b/example/mnist_demo/mnist_train.py
@@ -27,6 +27,8 @@ from mindarmour.utils.logger import LogUtil
 sys.path.append("..")
 from data_processing import generate_mnist_dataset
 LOGGER = LogUtil.get_instance()
+LOGGER.set_level('INFO')
+
 TAG = "Lenet5_train"
 
 
diff --git a/mindarmour/diff_privacy/__init__.py b/mindarmour/diff_privacy/__init__.py
index 8d8594adf3515ccc33869933f44461da18e949a0..a54ce80b771d621d6b61b942688dcf1e1d0f8ece 100644
--- a/mindarmour/diff_privacy/__init__.py
+++ b/mindarmour/diff_privacy/__init__.py
@@ -7,6 +7,8 @@ from .mechanisms.mechanisms import AdaClippingWithGaussianRandom
 from .mechanisms.mechanisms import NoiseMechanismsFactory
 from .mechanisms.mechanisms import ClipMechanismsFactory
 from .monitor.monitor import PrivacyMonitorFactory
+from .monitor.monitor import RDPMonitor
+from .monitor.monitor import ZCDPMonitor
 from .optimizer.optimizer import DPOptimizerClassFactory
 from .train.model import DPModel
 
@@ -16,5 +18,7 @@ __all__ = ['NoiseGaussianRandom',
            'NoiseMechanismsFactory',
            'ClipMechanismsFactory',
            'PrivacyMonitorFactory',
+           'RDPMonitor',
+           'ZCDPMonitor',
            'DPOptimizerClassFactory',
            'DPModel']
diff --git a/mindarmour/diff_privacy/monitor/monitor.py b/mindarmour/diff_privacy/monitor/monitor.py
index 229fcd8f55c8f45a286cbdbcf5469e9f1349e260..db05168c3121ba5f55f31003ebdf03d85a77d043 100644
--- a/mindarmour/diff_privacy/monitor/monitor.py
+++ b/mindarmour/diff_privacy/monitor/monitor.py
@@ -40,7 +40,12 @@ class PrivacyMonitorFactory:
 
         Args:
             policy (str): Monitor policy, 'rdp' and 'zcdp' are supported
-                by now.
+                by now. If policy is 'rdp', the monitor will compute the
+                privacy budget of DP training based on Renyi differential
+                privacy theory; If policy is 'zcdp', the monitor will compute
+                the privacy budget of DP training based on zero-concentrated
+                differential privacy theory. It's worth noting that 'zcdp'
+                is not suitable for subsampling nosie mechanism.
             args (Union[int, float, numpy.ndarray, list, str]): Parameters
                 used for creating a privacy monitor.
             kwargs (Union[int, float, numpy.ndarray, list, str]): Keyword