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self.answer=nn.Sequential(nn.Linear(64+12 if visual else 12,64),nn.ReLU(),nn.Linear(64,len(ANSWERS)))\n    def forward(self,pixels,question_id):\n        q=self.question(question_id)\n        features=torch.cat([self.cnn(pixels),q],-1) if self.visual else q\n        return self.answer(features)\n\nfull=VQA(True);question_only=VQA(False)\ncurve=[]\nfor label,net in [('image+question',full),('question-only',question_only)]:\n    optimizer=torch.optim.Adam(net.parameters(),lr=.002)\n    for epoch in range(28):\n        order=torch.randperm(Ytr.numel());net.train();loss_sum=0.\n        for pair_ids in order.split(72):\n            image_ids=pair_ids//3;qids=pair_ids%3\n            logits=net(Xtr[image_ids],qids);target=Ytr[image_ids,qids]\n            loss=F.cross_entropy(logits,target)\n            optimizer.zero_grad();loss.backward();optimizer.step();loss_sum+=float(loss.detach())*len(pair_ids)\n        if epoch in [0,5,13,27]:print(label,'epoch',epoch+1,'CE',round(loss_sum/Ytr.numel(),4))\n        curve.append({'model':label,'epoch':epoch+1,'train_ce':loss_sum/Ytr.numel()})\n    torch.save(net.state_dict(),OUT/('lab14-'+('visual' if net.visual else 'question-only')+'-weights.pt'))\n```","sha256":"701155870a1e8d1c9076048a018c3eac278a5c5ce5d1530da54c7367d1727177","chapter":{"id":"course","title":"3. 模型、损失与公平比较范围 · 实现代码"},"unit":{"id":"notebook-blocks/7f07825ae8a84326b6a7a768b26c7caf","title":"3. 模型、损失与公平比较范围 · 实现代码"},"page":{"id":"reading-opening","title":"3. 模型、损失与公平比较范围 · 实现代码","number":1},"sectionTitle":"3. 模型、损失与公平比较范围 · 实现代码","currentQuestion":"3. 模型、损失与公平比较范围 · 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