update code and readme

model.py

@@ -84,57 +84,3 @@ 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)

train.py

@@ -97,7 +97,7 @@ if __name__ == "__main__":
     print("测试集数据:", dataset.test_size)
     trainloader, testloader = dataset.get_loader(batch_size)
 
-    net = ConvNet2(num_classes)
+    net = ConvNet(num_classes)
     print('网络结构：\n', net)
     if torch.cuda.is_available():
         net = net.cuda()