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实现代码","sourceSlides":[],"knowledgeBlock":null,"formulas":[],"previousBlock":"b-131dc9db2b5b2ce1fc46c74e","nextBlock":"b-5f34be7201adb3cb15ffc528","prerequisites":[{"id":"b-9097a7be33f211c20bcbff34","url":"#b-9097a7be33f211c20bcbff34","role":"section-title"},{"id":"b-c272599ee658d1c297507858","url":"#b-c272599ee658d1c297507858","role":"problem-context"}],"relatedBlocks":[]},{"id":"b-5f34be7201adb3cb15ffc528","type":"pre","scope":"document","url":"https://codingai-lec03.pages.dev/course/notebook-blocks/9ac44bfb569543debd1b305a066b77bb.html#b-5f34be7201adb3cb15ffc528","content":"```\n%%writefile -a lab11_segmentation.py\ndef train_model(model,x,target,epochs,lr):\n    opt=torch.optim.Adam(model.parameters(),lr=lr);history=[]\n    for epoch in range(epochs):\n        model.train();losses=[]\n        for ids in torch.randperm(len(x)).split(32):\n            images=x[ids];labels=target[ids]\n            # One geometric decision applies to both image and mask.\n            if torch.rand(())<.5:images=images.flip(-1);labels=labels.flip(-1)\n            logits=model(images)\n            assert logits.shape==(len(ids),3,48,48) and labels.dtype==torch.long\n            loss=F.cross_entropy(logits,labels,ignore_index=255)\n            opt.zero_grad();loss.backward();opt.step();losses.append(float(loss.detach()))\n        history.append(float(np.mean(losses)))\n        if epoch==0 or (epoch+1)%5==0:print(type(model).__name__,epoch+1,history[-1],flush=True)\n    return history\n\n@torch.no_grad()\ndef predict(model,x,mode='bilinear'):\n    model.eval();return torch.cat([model(b,mode=mode).argmax(1) for b in x.split(32)])\n\ndef plot_predictions(images,truth,pred,path,title,indices=None):\n    if indices is None:indices=[0,1,2,3]\n    palette=np.array([[.04,.04,.05],[.95,.22,.15],[.10,.45,1],[.55,.55,.55]])\n    fig,axes=plt.subplots(len(indices),4,figsize=(10,2.6*len(indices)),squeeze=False)\n    for row,i in enumerate(indices):\n        mask=truth[i].numpy().copy();mask[mask==255]=3\n        error=(pred[i]!=truth[i])&(truth[i]!=255)\n        data=[images[i].permute(1,2,0).numpy(),palette[mask],palette[pred[i].numpy()],error.numpy()]\n        for ax,d,name in zip(axes[row],data,['Input RGB','Target / gray=ignore','Model prediction','Error on valid pixels']):\n            ax.imshow(d,cmap='magma',interpolation='nearest');ax.axis('off');ax.set_title(f'{name} #{i}',fontsize=9)\n    fig.suptitle(title);fig.tight_layout();fig.savefig(path,dpi=140);plt.close(fig)\n\n\n```","sha256":"9280d9f051fba81f9736060d652c337abb09125136855ddeff5476d73d4ad9c5","chapter":{"id":"course","title":"参数更新和三个单条件比较 · 实现代码"},"unit":{"id":"notebook-blocks/9ac44bfb569543debd1b305a066b77bb","title":"参数更新和三个单条件比较 · 实现代码"},"page":{"id":"reading-opening","title":"参数更新和三个单条件比较 · 实现代码","number":1},"sectionTitle":"参数更新和三个单条件比较 · 实现代码","currentQuestion":"参数更新和三个单条件比较 · 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