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40 lines
1.2 KiB
40 lines
1.2 KiB
import torch
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from torch import nn
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DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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list1=[]
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list_data = "2945807302157368193036426212997220033224538323640562241549909514547527720405608656907029313758584719540613589525481454212472019860395476200753292612652064279287757447621682752174888515904584744529078454748554565275582823574162998649840329792320732021527380675691933505646185089414885945266985722969732915061599825966637476"
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# print(len(list_data))
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for i in range(1,len(list_data)):
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list1.append(int(list_data[i]))
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# print(list1)
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list1=torch.tensor(list1, dtype=torch.float32).to(DEVICE)
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# print(list1)
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# print(list1.shape)
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class Model(nn.Module):
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def __init__(self):
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super(Model, self).__init__()
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self.net = nn.Sequential(
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nn.Linear(321, 200),
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nn.ReLU(),
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nn.Linear(200,100),
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nn.ReLU(),
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nn.Linear(100, 3),
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)
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def forward(self, input):
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return self.net(input)
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model = torch.load("Modle_0_GPU.pth").to(DEVICE)
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model.eval()
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with torch.no_grad():
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output = model(list1)
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result = output.argmax().item()
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print('这是等级{}'.format(result))
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