!5 update lenet and alexnet

Merge pull request !5 from wukesong/update-lenet-alexnet

tutorials/source_zh_cn/quick_start/quick_start.md

@@ -228,8 +228,6 @@ def fc_with_initialize(input_channels, out_channels):
 神经网络的各层需要预先在`__init__()`方法中定义，然后通过定义`construct()`方法来完成神经网络的前向构造。按照LeNet的网络结构，定义网络各层如下：
 
 ```python
-import mindspore.ops.operations as P
-
 class LeNet5(nn.Cell):
     """
     Lenet network structure
@@ -245,7 +243,7 @@ class LeNet5(nn.Cell):
         self.fc3 = fc_with_initialize(84, 10)
         self.relu = nn.ReLU()
         self.max_pool2d = nn.MaxPool2d(kernel_size=2, stride=2)
-        self.reshape = P.Reshape()
+        self.flatten = nn.Flatten()
 
     #use the preceding operators to construct networks
     def construct(self, x):
@@ -255,7 +253,7 @@ class LeNet5(nn.Cell):
         x = self.conv2(x)
         x = self.relu(x)
         x = self.max_pool2d(x)
-        x = self.reshape(x, (self.batch_size, -1))
+        x = self.flatten(x)
         x = self.fc1(x)
         x = self.relu(x)
         x = self.fc2(x)

tutorials/tutorial_code/lenet.py

@@ -24,7 +24,6 @@ from mindspore import context
 from mindspore.train.serialization import load_checkpoint, load_param_into_net
 from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor
 from mindspore.train import Model
-import mindspore.ops.operations as P
 from mindspore.common.initializer import TruncatedNormal
 import mindspore.dataset.transforms.vision.c_transforms as CV
 import mindspore.dataset.transforms.c_transforms as C
@@ -150,7 +149,7 @@ class LeNet5(nn.Cell):
         self.fc3 = fc_with_initialize(84, 10)
         self.relu = nn.ReLU()
         self.max_pool2d = nn.MaxPool2d(kernel_size=2, stride=2)
-        self.reshape = P.Reshape()
+        self.flatten = nn.Flatten()
 
     # use the preceding operators to construct networks
     def construct(self, x):
@@ -160,7 +159,7 @@ class LeNet5(nn.Cell):
         x = self.conv2(x)
         x = self.relu(x)
         x = self.max_pool2d(x)
-        x = self.reshape(x, (self.batch_size, -1))
+        x = self.flatten(x)
         x = self.fc1(x)
         x = self.relu(x)
         x = self.fc2(x)