Delete 'main.py'

main.py

@@ -1,63 +0,0 @@
-import json,torch,config
-import torch.nn as nn
-from date_build import Chem_Dataset
-from chem_trans import Chem_trans
-from torch.utils.data import Dataset, DataLoader
-from date_build import chem_collate_fn
-from date_build import Chem_Dataset
-import matplotlib.pyplot as plt
-import os
-
-def train(model, train_loader, optimizer, epoch,criterion,L1=True,lambda_l1=1e-5):
-    x=[]
-    y=[]
-    model.train()
-    for i in range(epoch):
-        total_loss = 0.0
-        for batch_idx, (input_,edge_index,target,enc) in enumerate(train_loader):
-
-
-            model = model.to(config.device)
-            input_ = input_.to(config.device)
-            enc=enc.to(config.device)
-            optimizer.zero_grad()
-            target = target.to(config.device)
-
-            output = model(input_,edge_index.to(config.device),enc)
-
-            print(output)
-            print(target)
-
-            loss = criterion(output,target.float())
-
-            if L1:
-                # 计算 L1 正则化项
-                l1_norm = sum(p.abs().sum() for p in model.parameters())
-                # 将 L1 正则化项添加到损失中
-                loss += lambda_l1 * l1_norm
-            # 反向传播和优化
-            loss.backward()
-            optimizer.step()
-
-            # 累计损失
-            total_loss =total_loss+ loss.item()
-            torch.cuda.empty_cache()
-            print(f'epoch: {i}, loss: {loss.item()}')
-        if i % 50 == 0:
-            plt.figure()
-            plt.plot(x, y, label='Line 1')
-            plt.title('Simple Plot')
-            plt.xlabel('X epoch')
-            plt.ylabel('Y Loss')
-            torch.save(model,f'D:\models\model3.1({i}).pt')
-            plt.savefig(f'training_progress/model3.1({i}).png')
-        x.append(i)
-        y.append(total_loss/len(train_loader))
-
-model=Chem_trans().to(config.device)
-
-
-criterion = nn.L1Loss().to(config.device)
-optimizer = torch.optim.SGD(model.parameters(), lr=0.000002,)
-if __name__ == '__main__':
-    train(model,Chem_Dataset,optimizer,epoch=800,criterion=criterion,L1=False,lambda_l1=1e-5)