update resnet in cifar

chapter_computer-vision/kaggle-gluon-cifar10.md

@@ -184,30 +184,29 @@ test_data = gdata.DataLoader(test_ds.transform_first(transform_test),
 
 ## 定义模型
 
-我们这里使用了ResNet-18模型，并使用混合式编程来提升执行效率。
+我们在这里定义ResNet-18模型，并使用混合式编程来提升执行效率。
 
 ```{.python .input  n=6}
 class Residual(nn.HybridBlock):
-    def __init__(self, channels, same_shape=True, **kwargs):
+    def __init__(self, num_channels, use_1x1conv=False, strides=1, **kwargs):
         super(Residual, self).__init__(**kwargs)
-        self.same_shape = same_shape
-        with self.name_scope():
-            strides = 1 if same_shape else 2
-            self.conv1 = nn.Conv2D(channels, kernel_size=3, padding=1,
+        self.conv1 = nn.Conv2D(num_channels, kernel_size=3, padding=1,
+                               strides=strides)
+        self.conv2 = nn.Conv2D(num_channels, kernel_size=3, padding=1)
+        if use_1x1conv:
+            self.conv3 = nn.Conv2D(num_channels, kernel_size=1,
                                    strides=strides)
-            self.bn1 = nn.BatchNorm()
-            self.conv2 = nn.Conv2D(channels, kernel_size=3, padding=1)
-            self.bn2 = nn.BatchNorm()
-            if not same_shape:
-                self.conv3 = nn.Conv2D(channels, kernel_size=1,
-                                       strides=strides)
+        else:
+            self.conv3 = None
+        self.bn1 = nn.BatchNorm()
+        self.bn2 = nn.BatchNorm()
 
-    def hybrid_forward(self, F, x):
-        out = F.relu(self.bn1(self.conv1(x)))
-        out = self.bn2(self.conv2(out))
-        if not self.same_shape:
-            x = self.conv3(x)
-        return F.relu(out + x)
+    def hybrid_forward(self, F, X):
+        Y = F.relu(self.bn1(self.conv1(X)))
+        Y = self.bn2(self.conv2(Y))
+        if self.conv3:
+            X = self.conv3(X)
+        return F.relu(Y + X)
 
 
 class ResNet(nn.HybridBlock):
@@ -223,15 +222,15 @@ class ResNet(nn.HybridBlock):
             net.add(nn.Activation(activation='relu'))
             # 模块 2。
             for _ in range(3):
-                net.add(Residual(channels=32))
+                net.add(Residual(num_channels=32))
             # 模块 3。
-            net.add(Residual(channels=64, same_shape=False))
+            net.add(Residual(num_channels=64, use_1x1conv=True, strides=2))
             for _ in range(2):
-                net.add(Residual(channels=64))
+                net.add(Residual(num_channels=64))
             # 模块 4。
-            net.add(Residual(channels=128, same_shape=False))
+            net.add(Residual(num_channels=128, use_1x1conv=True, strides=2))
             for _ in range(2):
-                net.add(Residual(channels=128))
+                net.add(Residual(num_channels=128))
             # 模块 5。
             net.add(nn.AvgPool2D(pool_size=8))
             net.add(nn.Flatten())