print(f"Epoch={epoch} Train Accuracy={train_acc} Train loss={train_loss} Validation accuracy={val_acc} Validation loss={val_loss} Memory used={memory_used} MB") def test_model(model,val_dataloader): model.eval()
train_acc, train_loss = self.epoch_train(epoch) test_acc, test_loss = self.epoch_test(epoch) self.tensorboard.add_scalar("train_acc", train_acc, epoch) self.tensorboard.add_scalar("train_loss", train_loss, epoch) self.tensorboard.add_scalar("test_acc", test_acc, epoch) self.tensorboa...
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...
Val Acc: 90.63%, Time: 0:02:36 * Iter: 300, Train Loss: 0.33, Train Acc: 89.84%, Val Loss: 0.32, Val Acc: 90.52%, Time: 0:03:41 * Iter: 400, Train Loss: 0.4, Train Acc: 88.28%, Val Loss: 0.28, Val Acc: 91.38%, Time: 0:04:49 * Iter: 500, Train Loss: 0.24, Trai...
lr, num_epochs,batch_size=0.05,10,256train_iter, test_iter = d2l.load_data_fashion_mnist(batch_size,resize=96) d2l.train_ch6(net, train_iter, test_iter, num_epochs, lr, d2l.try_gpu()) loss 0.013, train acc 0.997, test acc 0.876 ...
sum_num = evaluate(model=model,data_loader=val_loader,device=device)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):...
train_acc=0 num_correct=0forstep, (x,y)inenumerate(train_loader):#x: [b, 3, 224, 224], y: [b]x, y =x.to(device), y.to(device) model.train() logits=model(x) loss=criteon(logits, y) optimizer.zero_grad() loss.backward() ...
train_curve.append(loss.item()) if (i+1) % log_interval == 0: loss_mean = loss_mean / log_interval print("Training:Epoch[{:0>3}/{:0>3}] Iteration[{:0>3}/{:0>3}] Loss: {:.4f} Acc:{:.2%}".format( epoch, MAX_EPOCH, i+1, len(train_loader), loss_mean, correct /...
acc = correct / len(test_data) print(acc, test_loss) 为了早点响应.train(),应利用.eval() 将网络明确地设置为评估模式。 正如前文所述,计算图通常会在使用网络时生成。通过 with torch.no_grad() 使用 no_grad 上下文管理器,可以防止这种情况发生。