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人工智能系统实战第三期/实战代码/计算机视觉/CNN/network/AlexNet.py

+import torch
+import torch
+import torch.nn as nn
+
+
+class AlexNet(nn.Module):
+    def __init__(self, num_classes=1000):
+        super(AlexNet, self).__init__()
+        self.features = nn.Sequential(
+            nn.Conv2d(3, 96, kernel_size=11, stride=4, padding=2),  # input[3, 224, 224]  output[96, 55, 55]
+            nn.ReLU(inplace=True),
+            nn.MaxPool2d(kernel_size=3, stride=2),  # output[96, 27, 27]
+
+            nn.Conv2d(96, 256, kernel_size=5, padding=2),  # output[256, 27, 27]
+            nn.ReLU(inplace=True),
+            nn.MaxPool2d(kernel_size=3, stride=2),  # output[256, 13, 13]
+
+            nn.Conv2d(256, 384, kernel_size=3, padding=1),  # output[384, 13, 13]
+            nn.ReLU(inplace=True),
+
+            nn.Conv2d(384, 384, kernel_size=3, padding=1),  # output[384, 13, 13]
+            nn.ReLU(inplace=True),
+
+            nn.Conv2d(384, 256, kernel_size=3, padding=1),  # output[256, 13, 13]
+            nn.ReLU(inplace=True),
+            nn.MaxPool2d(kernel_size=3, stride=2),  # output[256, 6, 6]
+        )
+        self.classifier = nn.Sequential(
+            nn.Dropout(p=0.5),
+            nn.Linear(256 * 6 * 6, 4096),
+            nn.ReLU(inplace=True),
+
+            nn.Dropout(p=0.5),
+            nn.Linear(4096, 1000),
+            nn.ReLU(inplace=True),
+
+            nn.Linear(1000, num_classes),
+        )
+
+    def forward(self, x):
+        x = self.features(x)  # 256 * [6 * 6] 6x6的图片 / 矩阵
+        x = torch.flatten(x, start_dim=1)  # 256 * 6 * 6 数列
+        x = self.classifier(x)
+        return x
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