update: filter model V3.3

filter/RunningLogs.txt

@@ -13,3 +13,4 @@ Note
 0133: V4.2-test3
 0138: V4.3-test3
 0155: V5-test3 # V4 的效果也不是特别好
+0229: V3.3-test3 # 重新回到V3迭代

filter/modelV1_5.py

@@ -1,47 +0,0 @@
-import torch
-import torch.nn as nn
-
-class VideoClassifierV1_5(nn.Module):
-    def __init__(self, embedding_dim=1024, hidden_dim=256, output_dim=3):
-        super().__init__()
-        self.num_channels = 4
-        self.channel_names = ['title', 'description', 'tags', 'author_info']
-        
-        # 通道权重参数（可学习）
-        self.channel_weights = nn.Parameter(torch.ones(self.num_channels))
-        
-        # 全连接层
-        self.fc1 = nn.Linear(embedding_dim * self.num_channels, hidden_dim)
-        self.fc2 = nn.Linear(hidden_dim, output_dim)
-        self.log_softmax = nn.LogSoftmax(dim=1)
-
-    def forward(self, input_texts, sentence_transformer):
-        # 各通道特征提取
-        channel_features = []
-        for _, name in enumerate(self.channel_names):
-            # 获取当前通道的批量文本
-            batch_texts = input_texts[name]
-            
-            # 使用SentenceTransformer生成嵌入
-            embeddings = torch.tensor(
-                sentence_transformer.encode(batch_texts, task="classification")
-            )
-            channel_features.append(embeddings)
-        
-        # 将通道特征堆叠并加权
-        channel_features = torch.stack(channel_features, dim=1)  # [batch_size, num_channels, embedding_dim]
-        channel_weights = torch.softmax(self.channel_weights, dim=0)
-        weighted_features = channel_features * channel_weights.unsqueeze(0).unsqueeze(-1)
-        
-        # 拼接所有通道特征
-        combined_features = weighted_features.view(weighted_features.size(0), -1)  # [batch_size, num_channels * embedding_dim]
-        
-        # 全连接层
-        x = torch.relu(self.fc1(combined_features))
-        output = self.fc2(x)
-        output = self.log_softmax(output)
-        return output
-
-    def get_channel_weights(self):
-        """获取各通道的权重（用于解释性分析）"""
-        return torch.softmax(self.channel_weights, dim=0).detach().cpu().numpy()

filter/modelV3_3.py

@@ -1,28 +1,26 @@
 import torch
 import torch.nn as nn
 
-class VideoClassifierV5(nn.Module):
-    def __init__(self, embedding_dim=1024, hidden_dim=640, output_dim=3):
+class VideoClassifierV3_3(nn.Module):
+    def __init__(self, embedding_dim=1024, hidden_dim=512, output_dim=3):
         super().__init__()
         self.num_channels = 4
         self.channel_names = ['title', 'description', 'tags', 'author_info']
         
-        # 改进1：带温度系数的通道权重（比原始固定权重更灵活）
+        # 带温度系数的通道权重（比原始固定权重更灵活）
         self.channel_weights = nn.Parameter(torch.ones(self.num_channels))
-        self.temperature = 1.4  # 可调节的平滑系数
+        self.temperature = 1.7  # 可调节的平滑系数
         
-        # 改进2：更稳健的全连接结构
+        # 改进后的非线性层
         self.fc = nn.Sequential(
             nn.Linear(embedding_dim * self.num_channels, hidden_dim*2),
             nn.BatchNorm1d(hidden_dim*2),
             nn.Dropout(0.1),
             nn.ReLU(),
-            nn.Linear(hidden_dim*2, hidden_dim),
-            nn.LayerNorm(hidden_dim),
-            nn.Linear(hidden_dim, output_dim)
+            nn.Linear(hidden_dim*2, output_dim)
         )
         
-        # 改进3：输出层初始化
+        # 输出层初始化
         nn.init.xavier_uniform_(self.fc[-1].weight)
         nn.init.zeros_(self.fc[-1].bias)
 
@@ -56,7 +54,3 @@ class VideoClassifierV5(nn.Module):
     def get_channel_weights(self):
         """获取各通道权重（带温度调节）"""
         return torch.softmax(self.channel_weights / self.temperature, dim=0).detach().cpu().numpy()
-
-    def set_temperature(self, temperature):
-        """设置温度值"""
-        self.temperature = temperature

filter/train.py

@@ -3,7 +3,7 @@ os.environ["PYTORCH_ENABLE_MPS_FALLBACK"]="1"
 from torch.utils.data import DataLoader
 import torch.optim as optim
 from dataset import MultiChannelDataset
-from modelV5 import VideoClassifierV5
+from modelV3_3 import VideoClassifierV3_3
 from sentence_transformers import SentenceTransformer
 import torch.nn as nn
 from sklearn.metrics import f1_score, recall_score, precision_score, accuracy_score, classification_report
@@ -39,8 +39,8 @@ test_loader = DataLoader(test_dataset, batch_size=24, shuffle=False)
 
 # 初始化模型和SentenceTransformer
 sentence_transformer = SentenceTransformer("Thaweewat/jina-embedding-v3-m2v-1024")
-model = VideoClassifierV5()
-checkpoint_name = './filter/checkpoints/best_model_V5.pt'
+model = VideoClassifierV3_3()
+checkpoint_name = './filter/checkpoints/best_model_V3.3.pt'
 
 # 模型保存路径
 os.makedirs('./filter/checkpoints', exist_ok=True)
@@ -84,19 +84,12 @@ step = 0
 eval_interval = 50
 num_epochs = 8
 
-total_steps = num_epochs * len(train_loader)  # 总训练步数
-T_max = 1.4  # 初始温度
-T_min = 0.15  # 最终温度
-
 for epoch in range(num_epochs):
     model.train()
     epoch_loss = 0
     
     # 训练阶段
     for batch_idx, batch in enumerate(train_loader):
-        temperature = T_max - (T_max - T_min) * (step / total_steps)
-        model.set_temperature(temperature)
-
         optimizer.zero_grad()
         
         # 传入文本字典和sentence_transformer