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ContrastiveModel(nn.Module):\n    def __init__(self):\n        super().__init__(); self.encoder=ImageEncoder()\n        self.projector=nn.Sequential(nn.Linear(48,48),nn.ReLU(),nn.Linear(48,24))\n    def forward(self,x):\n        h=self.encoder(x); z=self.projector(h)\n        return h,z\n\ndef info_nce(z1,z2,tau=.2):\n    z=F.normalize(torch.cat([z1,z2]),dim=1); n=len(z1)\n    logits=z@z.T/tau\n    logits=logits.masked_fill(torch.eye(2*n,dtype=torch.bool),-1e9)\n    positive=(torch.arange(2*n)+n)%(2*n)\n    return F.cross_entropy(logits,positive)\n\ndef train_simclr(initial,x,train,destroy_color,epochs=40):\n    # Equal initialization, minibatch ordering, optimizer, steps, and RNG streams.\n    torch.manual_seed(SEED+1)\n    model=copy.deepcopy(initial); opt=torch.optim.Adam(model.parameters(),lr=.002)\n    history=[]; before=next(model.encoder.parameters()).detach().clone()\n    for epoch in range(epochs):\n        model.train(); losses=[]\n        for ids in torch.as_tensor(train)[torch.randperm(len(train))].split(60):\n            batch=x[ids]; a,b=views(batch,destroy_color),views(batch,destroy_color)\n            loss=info_nce(model(a)[1],model(b)[1])\n            opt.zero_grad(); loss.backward(); opt.step(); losses.append(float(loss.detach()))\n        history.append(float(np.mean(losses)))\n    delta=float((next(model.encoder.parameters()).detach()-before).norm())\n    assert delta>0\n    return model,history,delta\n```","sha256":"82501e784ec1c1a42f128baa406d84dc7b8a04583b7be1c4579de0bc12cabed6","chapter":{"id":"course","title":"h 与 z：目标作用在哪一层？ · 实现代码"},"unit":{"id":"notebook-blocks/7a1ffe32994142c180210d4ed48e445f","title":"h 与 z：目标作用在哪一层？ · 实现代码"},"page":{"id":"reading-opening","title":"h 与 z：目标作用在哪一层？ · 实现代码","number":1},"sectionTitle":"h 与 z：目标作用在哪一层？ · 实现代码","currentQuestion":"h 与 z：目标作用在哪一层？ · 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