The coefficientageThe coefficientsex_femaleThe coefficientforsex_maleis-8.762584065506853The coefficientforbmiis0.3807106266997645The coefficientforchildren_0is-0.06605803000190659The coefficientforchildren_1is-0.946643170369065The coefficientforchildren_2is0.2108032984623088The coefficientforchildren_3is0.8800441822437507The...
类AdversarialLoss中没有对logits_fake进行修改,所以返回刚才的训练程序中。 forepochinrange(1, epochs +1):foridx, (lr, hr)inenumerate(traindata_loader): lrs = lr.to(device) hrs = hr.to(device)# update the discriminatornetD.zero_grad() logits_fake = netD(netG(lrs).detach()) logits_real...
set_train(True)fori, (images, labels)inenumerate(data_loader): loss = train_step(images, ...
eval_losses.append(eval_loss/len(test_loader)) eval_acces.append(eval_acc/len(test_loader)) print('epoch:{},Train Loss:{:.4f},Train Acc:{:.4f},Test Loss:{:.4f},Test Acc:{:.4f}' .format(epoch,train_loss/len(train_loader),train_acc/len(train_loader), eval_loss/len(test_loader...
train_file,is_training=True,config=config,cached_features_file=os.path.join(config.train_path,"cache_" + config.train_file.replace("json","data"))) train_features,train_dataset = train_Dataset.features,train_Dataset.dataset train_loader = torch.utils.data.DataLoader(train_dataset, batch_size...
("Start Training ...")# 训练循环forepochinrange(num_epoch):step=1train_loss=train_acc=0fordataintqdm(train_loader):# Load all data into GPUdata=[i.to(device)foriindata]# 将批次中的所有数据加载到GPU上# data是列表,共5个元素,分别对于input_ids, token_type_ids, attention_mask, start_...
值得注意的是,模型和数据都需要先 load 进 GPU 中,DataParallel 的 module 才能对其进行处理,否则会报错: # 这里要 model.cuda() model = nn.DataParallel(model.cuda(), device_ids=gpus, output_device=gpus[0]) for epoch in range(100): for batch_idx, (data, target) in enumerate(train_loader):...
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=...) model = ... model = nn.DataParallel(model.to(device), device_ids=gpus, output_device=gpus[0]) optimizer = optim.SGD(model.parameters()) for epoch in range(100): for batch_idx, (data, target) in enumerate(...
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=...) model = ... model = nn.DataParallel(model.to(device), device_ids=gpus, output_device=gpus[0]) optimizer = optim.SGD(model.parameters()) for epoch in range(100): for batch_idx, (data, target) in enumerate(...