diff --git a/tutorials/source_zh_cn/advanced_use/mixed_precision.md b/tutorials/source_zh_cn/advanced_use/mixed_precision.md
index 7fdc240fbc66ca74f04b44afc4e5374e6899377f..89e9fb0930906c701ecfa1e2c68a88a5a8717c6c 100644
--- a/tutorials/source_zh_cn/advanced_use/mixed_precision.md
+++ b/tutorials/source_zh_cn/advanced_use/mixed_precision.md
@@ -45,9 +45,20 @@ MindSpore混合精度典型的计算流程如下图所示：
 代码样例如下：
 
 ```python
+import numpy as np
+import mindspore.nn as nn
+import mindspore.common.dtype as mstype
+from mindspore import Tensor, context
+from mindspore.ops import operations as P
+from mindspore.nn import WithLossCell
+from mindspore.nn import Momentum
+from mindspore.nn.loss import MSELoss
 # The interface of Auto_mixed precision
 from mindspore.train import amp
 
+context.set_context(mode=context.GRAPH_MODE)
+context.set_context(device_target="Ascend")
+
 # Define network
 class LeNet5(nn.Cell):
     def __init__(self):
@@ -58,7 +69,7 @@ class LeNet5(nn.Cell):
         self.fc2 = nn.Dense(120, 84)
         self.fc3 = nn.Dense(84, 10)
         self.relu = nn.ReLU()
-        self.max_pool2d = nn.MaxPool2d(kernel_size=2)
+        self.max_pool2d = nn.MaxPool2d(kernel_size=2, stride=2)
         self.flatten = P.Flatten()
 
     def construct(self, x):
@@ -103,6 +114,19 @@ MindSpore还支持手动混合精度。假定在网络中只有一个Dense Layer
 代码样例如下：
 
 ```python
+import numpy as np
+import mindspore.nn as nn
+import mindspore.common.dtype as mstype
+from mindspore import Tensor, context
+from mindspore.ops import operations as P
+from mindspore.nn import WithLossCell, TrainOneStepWithLossScaleCell
+from mindspore.nn import Momentum
+from mindspore.nn.loss import MSELoss
+
+
+context.set_context(mode=context.GRAPH_MODE)
+context.set_context(device_target="Ascend")
+
 # Define network
 class LeNet5(nn.Cell):
     def __init__(self):
@@ -111,9 +135,9 @@ 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).to_float(mstype.float32)
+        self.fc3 = nn.Dense(84, 10)
         self.relu = nn.ReLU()
-        self.max_pool2d = nn.MaxPool2d(kernel_size=2)
+        self.max_pool2d = nn.MaxPool2d(kernel_size=2, stride=2)
         self.flatten = P.Flatten()
 
     def construct(self, x):
@@ -128,6 +152,7 @@ class LeNet5(nn.Cell):
 # Initialize network and set mixing precision
 net = LeNet5()
 net.to_float(mstype.float16)
+net.fc3.to_float(mstype.float32)
 
 # Define training data, label and sens
 predict = Tensor(np.ones([1, 1, 32, 32]).astype(np.float32) * 0.01)
@@ -140,6 +165,7 @@ loss = MSELoss()
 optimizer = Momentum(params=net.trainable_params(), learning_rate=0.1, momentum=0.9)
 net_with_loss = WithLossCell(net, loss)
 train_network = TrainOneStepWithLossScaleCell(net_with_loss, optimizer)
+train_network.set_train()
 
 # Run training
 output = train_network(predict, label, scaling_sens)