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fusion_model.pth, a single linear layer) used on top of concatenated features = [MLP_mid(762) || BERT_CLS(768)] → logits(2).fusion_model.pth，用于对 拼接后的特征 [MLP_mid(762) || BERT_CLS(768)] 进行预测输出（2 类 logits）。ℹ️ The fusion head does not compute features itself. You must generate the two feature blocks with the same preprocessing pipeline used in training (StandardScaler→UMAP→StandardScaler for tabular; BERT-Base-Chinese tokenizer/encoder for text).
ℹ️ 融合头 不负责 自行提取特征。请使用训练时一致的预处理与编码流程生成两块特征（结构化端：标准化→UMAP→再标准化；文本端：BERT-Base-Chinese 的分词与编码）。
fusion_model.pth Only) / 调用代码（仅依赖 fusion_model.pth）tab_mid.npy: shape (N, 762) — tabular mid features from your trained MLP branchbert_cls.npy: shape (N, 768) — CLS vectors from your BERT branchtab_mid.npy：形状 (N, 762)，来自已训练 MLP 分支的中间层输出bert_cls.npy：形状 (N, 768)，来自 BERT 分支的 CLS 向量import torch
import torch.nn as nn
import numpy as np
from transformers import BertTokenizer, BertForSequenceClassification
class IPOClassifier(nn.Module):
def __init__(self, num_original, num_categorical, mid_dim, num_classes):
super().__init__()
self.numeric_branch = nn.Sequential(
nn.Linear(30, 128), nn.BatchNorm1d(128), nn.ReLU(), nn.Linear(128, 256)
)
self.missing_branch = nn.Linear(num_original, 64)
self.final_branch = nn.Sequential(nn.Linear(320, 256), nn.BatchNorm1d(256), nn.ReLU())
self.categorical_layer = nn.Sequential(nn.Linear(num_categorical, 128), nn.ReLU())
self.mid_layer = nn.Sequential(nn.Linear(384, mid_dim), nn.LeakyReLU())
self.classifier = nn.Linear(mid_dim, num_classes, bias=False)
def forward(self, main_numeric, missing_indicator, categorical_input):
num_out = self.numeric_branch(main_numeric)
missing_out = self.missing_branch(missing_indicator)
final_combined = self.final_branch(torch.cat([num_out, missing_out], dim=1))
cat_out = self.categorical_layer(categorical_input)
mid = self.mid_layer(torch.cat([final_combined, cat_out], dim=1))
logits = self.classifier(mid)
return logits, mid
class CombinedModel(nn.Module):
def __init__(self, extra_net, bert_fs_model, mid_dim, num_classes):
super().__init__()
self.extra_net = extra_net
self.bert_encoder = bert_fs_model.bert
d1 = mid_dim
d2 = bert_fs_model.config.hidden_size
self.fusion_classifier = nn.Linear(d1 + d2, num_classes)
def forward(self, main_numeric, missing_indicator, categorical, input_ids, attention_mask):
_, extra_mid = self.extra_net(main_numeric, missing_indicator, categorical)
bert_outputs = self.bert_encoder(input_ids=input_ids, attention_mask=attention_mask)
bert_cls = bert_outputs.last_hidden_state[:, 0, :]
logits = self.fusion_classifier(torch.cat([extra_mid, bert_cls], dim=1))
return logits
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
mid_dim = 762
num_classes = 2
num_original = 53
num_categorical = 72
dummy_extra = IPOClassifier(num_original, num_categorical, mid_dim, num_classes)
dummy_bert = BertForSequenceClassification.from_pretrained("bert", num_labels=num_classes)
fusion_model = CombinedModel(dummy_extra, dummy_bert, mid_dim, num_classes)
fusion_model.load_state_dict(torch.load("fusion_model.pth", map_location=device))
fusion_model.to(device).eval()
print("✅ 模型加载成功，可直接推理")
(1, 762) and (1, 768) arrays.(1, 762) 与 (1, 768) 的数组。示例参考来源（论文中提及）：上交所、国泰安（CSMAR/GTJA 等数据库） 及公开公司文件。
Note: Ensure you have proper data licenses and comply with exchange/database terms.