update code and readme

model.py

@@ -84,3 +84,57 @@ class ConvNet(nn.Module):
             m.weight.data.normal_(0, 0.01)
             m.bias.data.zero_()
 
+
+class ConvNet2(nn.Module):
+    def __init__(self, num_classes):
+        super(ConvNet2, self).__init__()
+        self.features = nn.Sequential(
+            nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1),
+            nn.LeakyReLU(inplace=True),
+            nn.MaxPool2d(kernel_size=2, stride=2),
+            nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1),
+            nn.Conv2d(128, 128, kernel_size=3, stride=1, padding=1),
+            nn.LeakyReLU(inplace=True),
+            nn.MaxPool2d(kernel_size=2, stride=2),
+            nn.Conv2d(128, 256, kernel_size=3, stride=1, padding=1),
+            nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1),
+            nn.LeakyReLU(inplace=True),
+            nn.MaxPool2d(kernel_size=2, stride=2),
+            nn.Conv2d(256, 512, kernel_size=3, stride=1, padding=1),
+            nn.LeakyReLU(inplace=True),
+            nn.MaxPool2d(kernel_size=2, stride=2),
+        )
+        self.classifier = nn.Sequential(
+            nn.Linear(512 * 4 * 4, 4096),
+            # nn.Dropout(),
+            nn.ReLU(inplace=True),
+            nn.Linear(4096, num_classes),
+        )
+        self.weight_init()
+
+    def forward(self, x):
+        x = self.features(x)
+        x = x.view(x.size(0), -1)
+        x = self.classifier(x)
+        return x
+
+    def weight_init(self):
+        for layer in self.features:
+            self._layer_init(layer)
+        for layer in self.classifier:
+            self._layer_init(layer)
+
+    def _layer_init(self, m):
+        # 使用isinstance来判断m属于什么类型
+        if isinstance(m, nn.Conv2d):
+            n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
+            m.weight.data.normal_(0, np.sqrt(2. / n))
+        elif isinstance(m, nn.BatchNorm2d):
+            # m中的weight，bias其实都是Variable，为了能学习参数以及后向传播
+            m.weight.data.fill_(1)
+            m.bias.data.zero_()
+        elif isinstance(m, nn.Linear):
+            # n = m.weight.size(1)
+            m.weight.data.normal_(0, 0.01)
+            m.bias.data.zero_()
+            # init.xavier_normal_(m.weight)