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5.0 (102 Rezensionen)
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import torch,torch.nn as nn,torch.optim as optim,torch.nn.functional as F,randomclass SlowpokeNet(nn.Module): def __init__(self): super().__init__() self.m=nn.Sequential(nn.Linear(2,32),nn.ReLU(),nn.Linear(32,16),nn.ReLU(),nn.Linear(16,2)) def forward(self,x): return self.m(x)model=SlowpokeNet()opt=optim.Adam(model.parameters(),lr=0.01)for _ in range(6): x=torch.randn(8,2) y=(x[:,1]>x[:,0]).long() p=model(x) loss=F.cross_entropy(p,y) opt.zero_grad();loss.backward();opt.step()t=torch.randn(1,2)r=model(t).argmax(-1).item()print(["Slowpoke","Psyduck"][r],"materializes, confused but determined.")
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