Firsh Push at 20241207

experiments/pytorch_Model.py

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+import torch
+import torch.nn as nn
+import torch.optim as optim
+import torchvision.transforms as transforms
+import timm
+
+
+class ModifiedXception(nn.Module):
+    def __init__(self):
+        super(ModifiedXception, self).__init__()
+        
+        # 加載 Xception 預訓練模型，去掉最後一層 (fc 層)
+        self.base_model = timm.create_model('xception', pretrained=True)
+        self.base_model.fc = nn.Identity()  # 移除原來的 fully connected 層
+        
+        # 新增全局平均池化層、隱藏層和輸出層
+        self.global_avg_pool = nn.AdaptiveAvgPool2d(1)  # 全局平均池化
+        self.hidden_layer = nn.Linear(2048, 1370)  # 隱藏層，輸入大小取決於 Xception 的輸出大小
+        self.output_layer = nn.Linear(1370, 2)  # 輸出層，依據分類數目設定
+        
+        # 激活函數與 dropout
+        self.relu = nn.ReLU()
+        self.dropout = nn.Dropout(0.6)
+
+    def forward(self, x):
+        x = self.base_model(x)               # Xception 主體
+        x = self.global_avg_pool(x)           # 全局平均池化
+        x = self.relu(self.hidden_layer(x))   # 隱藏層 + ReLU
+        x = self.dropout(x)                   # Dropout
+        x = self.output_layer(x)              # 輸出層
+        return x