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nets/lenet.py

+# -*- coding: utf-8 -*-
+# @Time    : 2021/7/5 16:29
+# @Author  : Mat
+# @File    : lenet.py
+# @Software: PyCharm
+import math
+
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+# ************************LeNet5*********************
+class LeNet(nn.Module):
+    def __init__(self, num_class=10):
+        super(LeNet, self).__init__()
+        self.conv1 = nn.Conv2d(3, 6, 5)
+        self.conv2 = nn.Conv2d(6, 16, 5)
+        self.fc1 = nn.Linear(16 * 5 * 5, 120)
+        self.fc2 = nn.Linear(120, 84)
+        self.fc3 = nn.Linear(84, num_class)
+
+    def forward(self, x):
+        x = F.max_pool2d(F.relu(self.conv1(x)), (2, 2))
+        x = F.max_pool2d(F.relu(self.conv2(x)), 2)
+        x = x.view(x.size()[0], -1)
+        x = F.relu(self.fc1(x))
+        x = F.relu(self.fc2(x))
+        x = self.fc3(x)
+        return x
+
+    def _initialize_weights(self):
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
+                m.weight.data.normal_(0, math.sqrt(2. / n))
+                if m.bias is not None:
+                    m.bias.data.zero_()
+            elif isinstance(m, nn.Linear):
+                m.weight.data.normal_(0, 0.01)
+                m.bias.data.zero_()
+
+
+if __name__ == '__main__':
+    net = LeNet()
+    print(net)