diff --git a/tutorials/source_en/advanced_use/mixed_precision.md b/tutorials/source_en/advanced_use/mixed_precision.md
index f73d71cece34f8a3d1920999b06dd47f90d811e3..d7b4fb45329c0558315f63f6617846baf9499487 100644
--- a/tutorials/source_en/advanced_use/mixed_precision.md
+++ b/tutorials/source_en/advanced_use/mixed_precision.md
@@ -80,14 +80,13 @@ label = Tensor(np.zeros([1, 10]).astype(np.float32))
 scaling_sens = Tensor(np.full((1), 1.0), dtype=mstype.float32)
 
 # Define Loss and Optimizer
-net.set_train()
 loss = MSELoss()
 optimizer = Momentum(params=net.trainable_params(), learning_rate=0.1, momentum=0.9)
 net_with_loss = WithLossCell(net, loss)
 train_network = amp.build_train_network(net_with_loss, optimizer, level="O2")
 
 # Run training
-output = train_network(inputs, label, scaling_sens)
+output = train_network(predict, label, scaling_sens)
 ```
 
 
@@ -98,7 +97,7 @@ MindSpore also supports manual mixed precision. It is assumed that only one dens
 The following is the procedure for implementing manual mixed precision:
 1. Define the network. This step is similar to step 2 in the automatic mixed precision. NoteThe fc3 operator in LeNet needs to be manually set to FP32.
 
-2. Configure the mixed precision. Use net.add_flags_recursive(fp16=True) to set all operators of the cell and its sub-cells to FP16.
+2. Configure the mixed precision. Use net.to_float(mstype.float16) to set all operators of the cell and its sub-cells to FP16.
 
 3. Use TrainOneStepWithLossScaleCell to encapsulate the network model and optimizer.
 
@@ -113,7 +112,7 @@ class LeNet5(nn.Cell):
         self.conv2 = nn.Conv2d(6, 16, 5, pad_mode='valid')
         self.fc1 = nn.Dense(16 * 5 * 5, 120)
         self.fc2 = nn.Dense(120, 84)
-        self.fc3 = nn.Dense(84, 10).add_flags_recursive(fp32=True)
+        self.fc3 = nn.Dense(84, 10).to_float(mstype.float32)
         self.relu = nn.ReLU()
         self.max_pool2d = nn.MaxPool2d(kernel_size=2)
         self.flatten = P.Flatten()
@@ -129,7 +128,7 @@ class LeNet5(nn.Cell):
 
 # Initialize network and set mixing precision
 net = LeNet5()
-net.add_flags_recursive(fp16=True)
+net.to_float(mstype.float16)
 
 # Define training data, label and sens
 predict = Tensor(np.ones([1, 1, 32, 32]).astype(np.float32) * 0.01)
@@ -144,5 +143,5 @@ net_with_loss = WithLossCell(net, loss)
 train_network = TrainOneStepWithLossScaleCell(net_with_loss, optimizer)
 
 # Run training
-output = train_network(inputs, label, scaling_sens)
+output = train_network(predict, label, scaling_sens)
 ```
diff --git a/tutorials/source_zh_cn/advanced_use/mixed_precision.md b/tutorials/source_zh_cn/advanced_use/mixed_precision.md
index 5b091cfd3fbf080bfa28192a1a96190eaac62f77..7fdc240fbc66ca74f04b44afc4e5374e6899377f 100644
--- a/tutorials/source_zh_cn/advanced_use/mixed_precision.md
+++ b/tutorials/source_zh_cn/advanced_use/mixed_precision.md
@@ -12,7 +12,7 @@
 
 ## 概述
 
-混合精度训练方法通过混合使用单精度和半精度数据格式来加速深度神经网络训练过程，同时保持了单精度训练所能达到的网络精度。混合精度训练能够加速计算过程，同时减少内存使用和存取，并在特定的硬件上可以训练更大的模型或batch size。
+混合精度训练方法是通过混合使用单精度和半精度数据格式来加速深度神经网络训练的过程，同时保持了单精度训练所能达到的网络精度。混合精度训练能够加速计算过程，同时减少内存使用和存取，并使得在特定的硬件上可以训练更大的模型或batch size。
 
 ## 计算流程
 
@@ -79,14 +79,13 @@ label = Tensor(np.zeros([1, 10]).astype(np.float32))
 scaling_sens = Tensor(np.full((1), 1.0), dtype=mstype.float32)
 
 # Define Loss and Optimizer
-net.set_train()
 loss = MSELoss()
 optimizer = Momentum(params=net.trainable_params(), learning_rate=0.1, momentum=0.9)
 net_with_loss = WithLossCell(net, loss)
 train_network = amp.build_train_network(net_with_loss, optimizer, level="O2")
 
 # Run training
-output = train_network(inputs, label, scaling_sens)
+output = train_network(predict, label, scaling_sens)
 ```
 
 
@@ -97,7 +96,7 @@ MindSpore还支持手动混合精度。假定在网络中只有一个Dense Layer
 以下是一个手动混合精度的实现步骤：
 1. 定义网络: 该步骤与自动混合精度中的步骤2类似；注意：在LeNet中的fc3算子，需要手动配置成FP32；
 
-2. 配置混合精度: LeNet通过net.add_flags_recursive(fp16=True)，把该Cell及其子Cell中所有的算子都配置成FP16；
+2. 配置混合精度: LeNet通过net.to_float(mstype.float16)，把该Cell及其子Cell中所有的算子都配置成FP16；
 
 3. 使用TrainOneStepWithLossScaleCell封装网络模型和优化器。
 
@@ -112,7 +111,7 @@ class LeNet5(nn.Cell):
         self.conv2 = nn.Conv2d(6, 16, 5, pad_mode='valid')
         self.fc1 = nn.Dense(16 * 5 * 5, 120)
         self.fc2 = nn.Dense(120, 84)
-        self.fc3 = nn.Dense(84, 10).add_flags_recursive(fp32=True)
+        self.fc3 = nn.Dense(84, 10).to_float(mstype.float32)
         self.relu = nn.ReLU()
         self.max_pool2d = nn.MaxPool2d(kernel_size=2)
         self.flatten = P.Flatten()
@@ -128,7 +127,7 @@ class LeNet5(nn.Cell):
 
 # Initialize network and set mixing precision
 net = LeNet5()
-net.add_flags_recursive(fp16=True)
+net.to_float(mstype.float16)
 
 # Define training data, label and sens
 predict = Tensor(np.ones([1, 1, 32, 32]).astype(np.float32) * 0.01)
@@ -143,5 +142,5 @@ net_with_loss = WithLossCell(net, loss)
 train_network = TrainOneStepWithLossScaleCell(net_with_loss, optimizer)
 
 # Run training
-output = train_network(inputs, label, scaling_sens)
+output = train_network(predict, label, scaling_sens)
 ```
\ No newline at end of file