diff --git a/examples/privacy/sup_privacy/sup_privacy.py b/examples/privacy/sup_privacy/sup_privacy.py
index 3c0b744..0fb28b7 100644
--- a/examples/privacy/sup_privacy/sup_privacy.py
+++ b/examples/privacy/sup_privacy/sup_privacy.py
@@ -148,4 +148,4 @@ if __name__ == "__main__":
     masklayers_lenet5.append(MaskLayerDes("fc3.weight", 6, True, False, 50))
 
     # do suppreess privacy train, with stronger privacy protection and better performance than Differential Privacy
-    mnist_suppress_train(10, 3, 0.05, 60000, 1000, 0.95, 0.0, masklayers=masklayers_lenet5)  # used
+    mnist_suppress_train(10, 3, 0.10, 60000, 1000, 0.95, 0.0, masklayers=masklayers_lenet5)  # used
diff --git a/mindarmour/privacy/sup_privacy/mask_monitor/masker.py b/mindarmour/privacy/sup_privacy/mask_monitor/masker.py
index 89c5045..fd54ac2 100644
--- a/mindarmour/privacy/sup_privacy/mask_monitor/masker.py
+++ b/mindarmour/privacy/sup_privacy/mask_monitor/masker.py
@@ -83,16 +83,15 @@ class SuppressMasker(Callback):
         cur_step_in_epoch = (cb_params.cur_step_num - 1) % cb_params.batch_num + 1
 
         if self._suppress_ctrl is not None and self._model.network_end is not None:
-            self._suppress_ctrl.update_status(cb_params.cur_epoch_num, cur_step, cur_step_in_epoch)
-
             if not self._suppress_ctrl.mask_initialized:
                 raise ValueError("Not initialize network!")
+            if cur_step_in_epoch % 100 == 1:
+                self._suppress_ctrl.calc_theoretical_sparse_for_conv()
+                _, _, _ = self._suppress_ctrl.calc_actual_sparse_for_conv(
+                    self._suppress_ctrl.networks)
+            self._suppress_ctrl.update_status(cb_params.cur_epoch_num, cur_step, cur_step_in_epoch)
             if self._suppress_ctrl.to_do_mask:
                 self._suppress_ctrl.update_mask(self._suppress_ctrl.networks, cur_step)
                 LOGGER.info(TAG, "suppress update")
             elif not self._suppress_ctrl.to_do_mask and self._suppress_ctrl.mask_started:
                 self._suppress_ctrl.reset_zeros()
-            if cur_step_in_epoch % 100 == 1:
-                self._suppress_ctrl.calc_theoretical_sparse_for_conv()
-                _, _, _ = self._suppress_ctrl.calc_actual_sparse_for_conv(
-                    self._suppress_ctrl.networks)
diff --git a/mindarmour/privacy/sup_privacy/sup_ctrl/conctrl.py b/mindarmour/privacy/sup_privacy/sup_ctrl/conctrl.py
index e77b48e..339bfdd 100644
--- a/mindarmour/privacy/sup_privacy/sup_ctrl/conctrl.py
+++ b/mindarmour/privacy/sup_privacy/sup_ctrl/conctrl.py
@@ -40,15 +40,16 @@ class SuppressPrivacyFactory:
         """
         Args:
             networks (Cell): The training network.
+                This networks parameter should be same as 'network' parameter of SuppressModel().
             mask_layers (list): Description of the training network layers that need to be suppressed.
             policy (str): Training policy for suppress privacy training. Default: "local_train", means local training.
             end_epoch (int): The last epoch in suppress operations, 0<start_epoch<=end_epoch<=100. Default: 10.
-                This end_epoch parameter should be the same as 'epoch' parameter of mindspore.train.model.train().
+                This end_epoch parameter should be same as 'epoch' parameter of mindspore.train.model.train().
             batch_num (int): The num of batch in an epoch, should be equal to num_samples/batch_size. Default: 20.
             start_epoch (int): The first epoch in suppress operations, 0<start_epoch<=end_epoch<=100. Default: 3.
             mask_times (int): The num of suppress operations. Default: 1000.
             lr (Union[float, int]): Learning rate, should be unchanged during training. 0<lr<=0.50. Default: 0.05.
-                This lr parameter should be the same as 'learning_rate' parameter of mindspore.nn.SGD().
+                This lr parameter should be same as 'learning_rate' parameter of mindspore.nn.SGD().
             sparse_end (float): The sparsity to reach, 0.0<=sparse_start<sparse_end<1.0. Default: 0.90.
             sparse_start (Union[float, int]): The sparsity to start, 0.0<=sparse_start<sparse_end<1.0. Default: 0.0.
 
@@ -164,7 +165,7 @@ class SuppressCtrl(Cell):
             msg = "mask_interval should be greater than 10, smaller than 20, but got {}".format(self.mask_step_interval)
             msg += "\n Precision of trained model may be poor !!! "
             msg += "\n please modify epoch_start, epoch_end and batch_num !"
-            msg += "\n        mask_interval = (epoch_end-epoch_start+1)*batch_num, batch_num = samples/batch_size"
+            msg += "\n mask_interval = (epoch_end-epoch_start+1)*batch_num/mask_times, batch_num = samples/batch_size"
             LOGGER.info(TAG, msg)
 
         if self.sparse_end >= 1.00 or self.sparse_end <= 0:
@@ -256,10 +257,11 @@ class SuppressCtrl(Cell):
                 msg = "can't match this mask layer: {} ".format(one_mask_layer.layer_name)
                 LOGGER.error(TAG, msg)
                 raise ValueError(msg)
-        msg = "this lr parameter should be the same as 'learning_rate' parameter of mindspore.nn.SGD()\n"
-        msg += "this end_epoch parameter should be the same as 'epoch' parameter of mindspore.train.model.train()\n"
-        msg += "sup_privacy only support SGD optimizer"
-        LOGGER.info(TAG, msg)
+        msg = "\nThis networks parameter should be same as 'network' parameter of SuppressModel()"
+        msg += "\nThis lr parameter should be same as 'learning_rate' parameter of mindspore.nn.SGD()\n"
+        msg += "\nThis end_epoch parameter should be same as 'epoch' parameter of mindspore.train.model.train()\n"
+        msg += "\nsup_privacy only support SGD optimizer"
+        LOGGER.warn(TAG, msg)
 
     def update_status(self, cur_epoch, cur_step, cur_step_in_epoch):
         """
@@ -289,21 +291,26 @@ class SuppressCtrl(Cell):
             self.to_do_mask = False
             self.mask_started = False
 
-    def update_mask(self, networks, cur_step):
+    def update_mask(self, networks, cur_step, target_sparse=0.0):
         """
         Update add mask arrays and multiply mask arrays of network layers.
 
         Args:
             networks (Cell): The training network.
             cur_step (int): Current epoch of the whole training process.
+            target_sparse(float): The sparsity to reach. Default: 0.0.
         """
         if self.sparse_end <= 0.0:
             return
 
-        self.cur_sparse = self.sparse_end +\
-                          (self.sparse_start - self.sparse_end)*\
-                          math.pow((1.0 - (cur_step + 0.0 - self.mask_start_step) / self.mask_all_steps), 3)
-        self.cur_sparse = min(self.cur_sparse, self.sparse_end)
+        last_sparse = self.cur_sparse
+        if target_sparse > 0.0:
+            self.cur_sparse = target_sparse
+        else:
+            self.cur_sparse = self.sparse_end + \
+                              (self.sparse_start - self.sparse_end) * \
+                              math.pow((1.0 - (cur_step + 0.0 - self.mask_start_step) / self.mask_all_steps), 3)
+        self.cur_sparse = max(self.sparse_start, max(last_sparse, min(self.cur_sparse, self.sparse_end)))
         m = 0
         for layer in networks.get_parameters(expand=True):
             grad_idx = self.grad_idx_map[m]
@@ -450,7 +457,7 @@ class SuppressCtrl(Cell):
     def update_mask_layer_approximity(self, weight_array_flat, weight_array_flat_abs, actual_stop_pos, layer_index):
         """
         Update add mask arrays and multiply mask arrays of one single layer with many parameter.
-            disable clipping loweer, clipping, adding noise operation
+        Disable clipping lower, clipping, adding noise operation
 
         Args:
             weight_array_flat (numpy.ndarray): The weight array of layer's parameters.
@@ -477,6 +484,8 @@ class SuppressCtrl(Cell):
         if last_sparse_pos <= 0:
             init_batch_suppress = True
         for i in range(0, part_num):
+            if split_k_num <= 0:
+                break
             array_row_mul_mask = mul_mask_array_flat[i * part_size : (i + 1) * part_size]
             array_row_flat_abs = weight_array_flat_abs[i * part_size : (i + 1) * part_size]
             if not init_batch_suppress:
@@ -488,7 +497,6 @@ class SuppressCtrl(Cell):
                 del array_row_flat_abs_masked
                 del set_abs
                 del list2
-                gc.collect()
             else:
                 val_array_align = array_row_flat_abs
 
@@ -497,7 +505,6 @@ class SuppressCtrl(Cell):
                 del array_row_flat_abs
                 del array_row_mul_mask
                 del val_array_align
-                gc.collect()
                 continue
 
             partition = np.partition(val_array_align, real_split_k_num - 1)
@@ -509,7 +516,6 @@ class SuppressCtrl(Cell):
             del array_row_mul_mask
             del val_array_align
             del partition
-            gc.collect()
 
         if real_part_num > 0:
             sparse_thd = sparse_thd / real_part_num
@@ -570,9 +576,9 @@ class SuppressCtrl(Cell):
             if array_mul_mask_flat_conv2[i] <= 0.0:
                 sparse = sparse + 1.0
                 sparse_value_2 += 1.0
-        sparse = sparse/full
-        sparse_value_1 = sparse_value_1/full_conv1
-        sparse_value_2 = sparse_value_2/full_conv2
+        sparse = sparse / full
+        sparse_value_1 = sparse_value_1 / full_conv1
+        sparse_value_2 = sparse_value_2 / full_conv2
         msg = "conv sparse mask={}, sparse_1={}, sparse_2={}".format(sparse, sparse_value_1, sparse_value_2)
         LOGGER.info(TAG, msg)
         return sparse, sparse_value_1, sparse_value_2
@@ -591,16 +597,24 @@ class SuppressCtrl(Cell):
         full_conv1 = 0.0
         full_conv2 = 0.0
 
+        conv1_matched = False
+        conv2_matched = False
+
         array_cur_conv1 = np.ones(np.shape([1]), dtype=np.float32)
         array_cur_conv2 = np.ones(np.shape([1]), dtype=np.float32)
         for layer in networks.get_parameters(expand=True):
-            if "networks.conv1.weight" in layer.name or "networks.layers.0.weight" in layer.name:  # lenet5/res50 vgg16
+            if not conv1_matched and \
+                    ("networks.conv1.weight" in layer.name or "networks.layers.0.weight" in layer.name):
+                # lenet5/res50 vgg16
                 array_cur_conv1 = layer.data.asnumpy()
-                print("calc_actual_sparse, match conv1")
-            if "networks.conv2.weight" in layer.name or "networks.layers.3.weight" in layer.name \
-                   or "networks.layer1.0.conv1.weight" in layer.name: # res50
+                print("calc_actual_sparse, match conv1: {}".format(layer.name))
+                conv1_matched = True
+            if not conv2_matched and \
+                    ("networks.conv2.weight" in layer.name or "networks.layers.3.weight" in layer.name \
+                     or "networks.layer1.0.conv1.weight" in layer.name):  # res50
                 array_cur_conv2 = layer.data.asnumpy()
-                print("calc_actual_sparse, match conv2")
+                print("calc_actual_sparse, match conv2: {}".format(layer.name))
+                conv2_matched = True
 
         array_mul_mask_flat_conv1 = array_cur_conv1.flatten()
         array_mul_mask_flat_conv2 = array_cur_conv2.flatten()
@@ -675,10 +689,18 @@ class SuppressCtrl(Cell):
         return sparse
 
     def print_paras(self):
+        """
+        Show parameters info
+        """
         msg = "paras: start_epoch:{}, end_epoch:{}, batch_num:{}, interval:{}, lr:{}, sparse_end:{}, sparse_start:{}" \
             .format(self.mask_start_epoch, self.mask_end_epoch, self.batch_num, self.mask_step_interval,
                     self.lr, self.sparse_end, self.sparse_start)
         LOGGER.info(TAG, msg)
+        msg = "\nThis networks parameter should be same as 'network' parameter of SuppressModel()"
+        msg = "\nThis lr parameter should be same as 'learning_rate' parameter of mindspore.nn.SGD()"
+        msg += "\nThis end_epoch parameter should be same as 'epoch' parameter of mindspore.train.model.train()"
+        msg += "\nsup_privacy only support SGD optimizer"
+        LOGGER.info(TAG, msg)
 
 def get_one_mask_layer(mask_layers, layer_name):
     """
@@ -713,11 +735,14 @@ class MaskLayerDes:
             grad layers (print in PYNATIVE_MODE).
         is_add_noise (bool): If True, the weight of this layer can add noise.
             If False, the weight of this layer can not add noise.
+            If parameter num is greater than 100000, is_add_noise has not effect.
         is_lower_clip (bool): If true, the weights of this layer would be clipped to greater than an lower bound value.
             If False, the weights of this layer won't be clipped.
+            If parameter num is greater than 100000, is_lower_clip has not effect.
         min_num (int): The number of weights left that not be suppressed.
             If min_num is smaller than (parameter num*SupperssCtrl.sparse_end), min_num has not effect.
         upper_bound (Union[float, int]): max abs value of weight in this layer, default: 1.20.
+            If parameter num is greater than 100000, upper_bound has not effect.
     """
     def __init__(self, layer_name, grad_idx, is_add_noise, is_lower_clip, min_num, upper_bound=1.20):
         self.layer_name = check_param_type('layer_name', layer_name, str)
@@ -753,7 +778,7 @@ class GradMaskInCell(Cell):
         self.is_add_noise = is_add_noise
         self.is_lower_clip = is_lower_clip
         self.min_num = min_num
-        self.upper_bound = check_value_positive('upper_bound', upper_bound)
+        self.upper_bound = max(0.10, check_value_positive('upper_bound', upper_bound))
 
         self.para_num = array.size
         self.is_approximity = False