Delete 'use.py'

use.py

@@ -1,45 +0,0 @@
-import time
-import torch
-import torchvision
-from torch import nn
-
-list1=[]
-list_data = "2945807302157368193036426212997220033224538323640562241549909514547527720405608656907029313758584719540613589525481454212472019860395476200753292612652064279287757447621682752174888515904584744529078454748554565275582823574162998649840329792320732021527380675691933505646185089414885945266985722969732915061599825966637476"
-# print(len(list_data))
-for i in range(1,len(list_data)):
-    list1.append(int(list_data[i]))
-# print(list1)
-list1=torch.tensor(list1, dtype=torch.float32)
-# print(list1)
-# print(list1.shape)
-
-
-
-
-class Model(nn.Module):
-    def __init__(self):
-        super(Model, self).__init__()
-        self.net = nn.Sequential(
-            nn.Linear(321, 159),
-            nn.ReLU(),
-            nn.Linear(159,81),
-            nn.ReLU(),
-            nn.Linear(81, 3),
-        )
-
-    def forward(self, input):
-        return self.net(input)
-
-
-model = Model()	# 导入网络结构
-model.load_state_dict(torch.load('model.pth', map_location='cpu')) # 导入网络的参数
-
-# print(model)
-
-
-with torch.no_grad():
-    output = model(list1)
-
-result = output.argmax().item()
-print('这是等级.。。。。。。。。。。。。。。。{}'.format(result))
-