初始化手写数字识别

train/VGG16Net.py

@@ -0,0 +1,54 @@
+from torch import nn
+from torch.utils.data import DataLoader
+
+# 定义 VGG16 网络结构
+class VGG16(nn.Module):
+  def __init__(self):
+      super(VGG16, self).__init__()
+
+      self.conv1 = nn.Sequential(
+         #32*1*28*28
+          nn.Conv2d(1, 16, kernel_size=3, padding=1),
+          #16*28*28
+          nn.ReLU(),
+          nn.Conv2d(16, 16, kernel_size=3, padding=1),
+          #16*28*28
+          nn.ReLU()
+      )
+
+      self.conv2 = nn.Sequential(
+          nn.Conv2d(16, 32, kernel_size=3, padding=1),
+          nn.ReLU(),
+          nn.Conv2d(32, 32, kernel_size=3, padding=1),
+          nn.ReLU()
+      )
+
+      self.conv3 = nn.Sequential(
+          nn.Conv2d(32, 64, kernel_size=3, padding=1),
+          nn.ReLU(),
+          nn.Conv2d(64, 64, kernel_size=3, padding=1),
+          nn.ReLU(),
+          nn.Conv2d(64, 64, kernel_size=3, padding=1),
+          nn.ReLU()
+      )
+
+      self.conv4 = nn.Sequential(
+          nn.Conv2d(64, 128, kernel_size=3, padding=1),
+          nn.ReLU(),
+          nn.Conv2d(128, 128, kernel_size=3, padding=1),
+          nn.ReLU()
+      )
+      
+      self.fc = nn.Linear(128*28*28, 100)
+      self.fc1 = nn.Linear(100, 10)
+
+  def forward(self, x):
+      x = self.conv1(x)
+      x = self.conv2(x)
+      x = self.conv3(x)
+      x = self.conv4(x)
+      x = nn.Flatten()(x)
+      x = self.fc(x)
+      x = self.fc1(x)
+      return x
+