def train(model,optimizer,loss_function,train_data,val_data,test_data,epochs): #输入:网络架构,优化器,损失函数,训练集,验证集,测试集,轮次 best_acc,best_epoch =0,0 #输出验证集中准确率最高的轮次和准确率 train_list,val_List = [],[] # 创建列表保存每
with torch.no_grad(): y_pred = model(X_train) acc_train = (y_pred.argmax(dim=1) == y_train).float().mean().item() y_pred = model(X_test) acc_test = (y_pred.argmax(dim=1) == y_test).float().mean().item() print(epoch, acc_train, acc_test) ### 训练结果 ### ...
deftrain(epoch):print('\nEpoch: %d'%epoch)# scheduler.step()model.train()train_acc=0.0forbatch_idx,(img,label)inenumerate(trainloader):# 迭代器,一次迭代 batch_size 个数据进去 image=img.to(device)label=label.to(device)optimizer.zero_grad()out=model(image)loss=criterion(out,label)loss.bac...
best_dev_acc{:.6f} %,train_loss:{:.6f},dev_loss:{:.6f}".format(epoch, epochs, step, len(train_loader), two_pro_train_acc * 100, two_pro_dev_acc * 100,dev_best_acc*100,two_pro_loss,two_pro_dev_loss))
train_l_sum=0.0#训练损失值 train_acc_sum=0.0#训练精度 n, start=0, time.time() pbar=tqdm(enumerate(train_iter), total=nb) #tqmd可以更直观地观察训练集加载的过程 fori, (imgs, targets)inpbar: imgs=imgs.to(device) targets=targets.to(device) ...
return acc, loss def epoch_train(self, epoch): """模型训练""" loss_sum = [] accuracies = [] self.model.train() for step, (inputs, labels) in enumerate(tqdm(self.train_loader)): inputs = (self.device) labels = (self.device).long() ...
每个进程绑定一张加速卡--nproc_per_node指定需要创建的进程数量train.py 是对应的包含 DDP 分布式训练...
no_grad(): y_pred = model(X_train) acc_train = (y_pred.argmax(dim=1) == y_train).float().mean().item() y_pred = model(X_test) acc_test = (y_pred.argmax(dim=1) == y_test).float().mean().item() print(epoch, acc_train, acc_test) ### 训练结果 ### 10%|██...
acc = sum_num / val_sampler.total_size我们详细看看每个epoch是训练时和单GPU训练的差异(上面的train_one_epoch) def train_one_epoch(model, optimizer, data_loader, device, epoch): model.train() loss_function = torch.nn.CrossEntropyLoss() mean_l...
acc = sum_num / val_sampler.total_size 我们详细看看每个epoch是训练时和单GPU训练的差异(上面的train_one_epoch) deftrain_one_epoch(model, optimizer, data_loader, device, epoch): model.train() loss_function = torch.nn.CrossEntropyLoss() ...