{"action":"created","issue":{"url":"https://api.github.com/repos/huanghoujing/AlignedReID-Re-Production-Pytorch/issues/42","repository_url":"https://api.github.com/repos/huanghoujing/AlignedReID-Re-Production-Pytorch","labels_url":"https://api.github.com/repos/huanghoujing/AlignedReID-Re-Production-Pytorch/issues/42/labels{/name}","comments_url":"https://api.github.com/repos/huanghoujing/AlignedReID-Re-Production-Pytorch/issues/42/comments","events_url":"https://api.github.com/repos/huanghoujing/AlignedReID-Re-Production-Pytorch/issues/42/events","html_url":"https://github.com/huanghoujing/AlignedReID-Re-Production-Pytorch/issues/42","id":336088416,"node_id":"MDU6SXNzdWUzMzYwODg0MTY=","number":42,"title":"关于 classification mutual learning","user":{"login":"lawpdas","id":5070053,"node_id":"MDQ6VXNlcjUwNzAwNTM=","avatar_url":"https://avatars3.githubusercontent.com/u/5070053?v=4","gravatar_id":"","url":"https://api.github.com/users/lawpdas","html_url":"https://github.com/lawpdas","followers_url":"https://api.github.com/users/lawpdas/followers","following_url":"https://api.github.com/users/lawpdas/following{/other_user}","gists_url":"https://api.github.com/users/lawpdas/gists{/gist_id}","starred_url":"https://api.github.com/users/lawpdas/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/lawpdas/subscriptions","organizations_url":"https://api.github.com/users/lawpdas/orgs","repos_url":"https://api.github.com/users/lawpdas/repos","events_url":"https://api.github.com/users/lawpdas/events{/privacy}","received_events_url":"https://api.github.com/users/lawpdas/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":3,"created_at":"2018-06-27T06:06:27Z","updated_at":"2018-06-29T04:16:52Z","closed_at":null,"author_association":"NONE","body":"您好，我按照 Deep Mutual Learning复现 classification 的 mutual learning，但是一直没效果。偶然看到您论文里这部分实验，想问一下单独只做  classification 的 mutual learning 有什么要注意的？"},"comment":{"url":"https://api.github.com/repos/huanghoujing/AlignedReID-Re-Production-Pytorch/issues/comments/401242819","html_url":"https://github.com/huanghoujing/AlignedReID-Re-Production-Pytorch/issues/42#issuecomment-401242819","issue_url":"https://api.github.com/repos/huanghoujing/AlignedReID-Re-Production-Pytorch/issues/42","id":401242819,"node_id":"MDEyOklzc3VlQ29tbWVudDQwMTI0MjgxOQ==","user":{"login":"lawpdas","id":5070053,"node_id":"MDQ6VXNlcjUwNzAwNTM=","avatar_url":"https://avatars3.githubusercontent.com/u/5070053?v=4","gravatar_id":"","url":"https://api.github.com/users/lawpdas","html_url":"https://github.com/lawpdas","followers_url":"https://api.github.com/users/lawpdas/followers","following_url":"https://api.github.com/users/lawpdas/following{/other_user}","gists_url":"https://api.github.com/users/lawpdas/gists{/gist_id}","starred_url":"https://api.github.com/users/lawpdas/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/lawpdas/subscriptions","organizations_url":"https://api.github.com/users/lawpdas/orgs","repos_url":"https://api.github.com/users/lawpdas/repos","events_url":"https://api.github.com/users/lawpdas/events{/privacy}","received_events_url":"https://api.github.com/users/lawpdas/received_events","type":"User","site_admin":false},"created_at":"2018-06-29T04:16:52Z","updated_at":"2018-06-29T04:16:52Z","author_association":"NONE","body":"我发现改成 False 后 loss 很难下降，修改 loss weight 也没什么变化。另外我对 pytorch 不太熟，不是很明白您的程序，我这样写有问题吗？\r\n```python\r\n        \"\"\"compute p1 p2\"\"\"\r\n        logits1 = FC1(Feat1(inputs).view(-1,256))\r\n        logits2 = FC2(Feat2(inputs).view(-1,256))\r\n        \r\n        \"\"\"update net1\"\"\"\r\n        l1 = F.cross_entropy(logits1, labels)\r\n        kl1 = F.kl_div(F.log_softmax(logits1, dim=1),F.softmax(logits2.detach(), dim=1),False)/logits1.shape[0]\r\n        loss1 = l1 + kl1\r\n        \r\n        optimizer1.zero_grad()\r\n        loss1.backward()\r\n        optimizer1.step()\r\n        \r\n        \"\"\"compute p1\"\"\"\r\n        logits1 = FC1(Feat1(inputs).view(-1,256))\r\n\r\n        \"\"\"update net2\"\"\"\r\n        l2 = F.cross_entropy(logits2, labels)\r\n        kl2 = F.kl_div(F.log_softmax(logits2, dim=1),F.softmax(logits1.detach(), dim=1),False)/logits2.shape[0]\r\n        loss2 = l2 + kl2\r\n        \r\n        optimizer2.zero_grad()\r\n        loss2.backward()\r\n        optimizer2.step()\r\n```"}}
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