From ddff6e193fddf3602887448ed6b406c1eabe534b Mon Sep 17 00:00:00 2001
From: p9kh64cfp <1047063963@qq.com>
Date: Tue, 31 Dec 2024 11:21:00 +0800
Subject: [PATCH] ADD file via upload

---
 model_alexnet.py | 40 ++++++++++++++++++++++++++++++++++++++++
 1 file changed, 40 insertions(+)
 create mode 100644 model_alexnet.py

diff --git a/model_alexnet.py b/model_alexnet.py
new file mode 100644
index 0000000..7f1ec69
--- /dev/null
+++ b/model_alexnet.py
@@ -0,0 +1,40 @@
+import torch.nn as nn
+import torch
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+class ImprovedAlexNet(nn.Module):
+    def __init__(self, num_classes=1000):
+        super(ImprovedAlexNet, self).__init__()
+        self.features = nn.Sequential(  # 卷积层提取图像特征
+            nn.Conv2d(3, 48, kernel_size=11, stride=4, padding=2),  # input[3, 224, 224]  output[48, 55, 55]
+            nn.ReLU(inplace=True),  # 直接修改覆盖原值，节省运算内存
+            nn.MaxPool2d(kernel_size=3, stride=2),  # output[48, 27, 27]
+            nn.Conv2d(48, 128, kernel_size=5, padding=2),  # output[128, 27, 27]
+            nn.ReLU(inplace=True),
+            nn.MaxPool2d(kernel_size=3, stride=2),  # output[128, 13, 13]
+            nn.Conv2d(128, 192, kernel_size=3, padding=1),  # output[192, 13, 13]
+            nn.ReLU(inplace=True),
+            nn.Conv2d(192, 192, kernel_size=3, padding=1),  # output[192, 13, 13]
+            nn.ReLU(inplace=True),
+            nn.Conv2d(192, 128, kernel_size=3, padding=1),  # output[128, 13, 13]
+            nn.ReLU(inplace=True),
+            nn.MaxPool2d(kernel_size=3, stride=2),  # output[128, 6, 6]
+        )
+        self.classifier = nn.Sequential(  # 全连接层对图像分类
+            nn.Dropout(p=0.5),  # Dropout 随机失活神经元，默认比例为0.5
+            nn.Linear(128 * 6 * 6, 2048),
+            nn.ReLU(inplace=True),
+            nn.Dropout(p=0.5),
+            nn.Linear(2048, 2048),
+            nn.ReLU(inplace=True),
+            nn.Linear(2048, num_classes),
+        )
+
+    def forward(self, x):
+        x = self.features(x)#进行卷积操作
+        x = torch.flatten(x, start_dim=1)#展平
+        x = self.classifier(x)#输出
+        return x
+