net.eval()# 评估模式,会关闭 dropoutacc_sum += (net(X.to(device)).argmax(dim=1) == y.to(device)).float().sum().cpu().item() net.train()# 改回训练模式else:# 如果是自定义的模型if'is_training'innet.__code__.co_varnames: acc_sum += (net(X, is_training=False).argmax(dim...
argmax(dim=1) == y).sum().cpu().item() n += y.shape[0] batch_count += 1 test_acc = evaluate_accuracy(test_iter, net) print('epoch %d, loss %.4f, train acc %.3f, test acc %.3f, time %.1f sec' % (epoch + 1, train_l_sum / n, train_acc_sum / n, test_acc, ...
test_correct =0test_total =0test_running_loss =0withtorch.no_grad():forx, yintestloader: y_pred = model(x) loss = loss_func(y_pred, y) y_pred = torch.argmax(y_pred, dim =1) test_correct += (y_pred == y).sum().item() test_total += y.size(0) test_running_loss +=...
argmax(y_pred, dim=1).flatten() y = y.flatten() target_mask = (y >= 0) & (y < self.num_classes) y = y[target_mask] y_pred = y_pred[target_mask] indices = self.num_classes * y + y_pred m = torch.bincount(indices, minlength=self.num_classes ** 2).reshape(self.num_...
pred = torch.argmax(out, dim=1) correct = pred.eq(y).sum() acc = float(correct) / len(y)#print lossifi % echoStep ==0:print"Step %d/%d/%d : Loss %.4f , Acc %.4f "%(i,batchLen*epoch,epochIdx+1,float(loss),acc)#evaluateifi % evalStep ==0andevalData !=None: ...
pred = out.argmax(dim=1) # Use the class with highest probability. test_correct = pred[data.test_mask] == data.y[data.test_mask] # Check against ground-truth labels. test_acc = int(test_correct.sum()) / int(data.test_mask.sum()) # Derive ratio of correct predictions. ...
1. 2. 3. 在GPU上转换图像 大多数变换支持PIL图像顶部的张量(要可视化变换的效果,请参阅此链接)。使用张量图像,如果cuda可用,我们可以在GPU上运行变换! import torch.nn as nn transforms = torch.nn.Sequential( T.RandomCrop(224), ...
argmax(dim = 1)==y).sum().item() n += y_pred.shape[0] train_ls.append(train_ls_sum) train_acc.append(train_acc_sum/n) test_ls_sum,test_acc_sum,n = 0,0,0 for x,y in test_iter: y_pred = net(x) l = loss(y_pred,y) test_ls_sum +=l.item() test_acc_sum += ...
argmax(torch.softmax(y_pred, dim=1), dim=1) train_acc += (y_pred_class == y).sum().item()/len(y_pred) # 调整指标以得到每个批次的平均损失和准确率 train_loss = train_loss / len(dataloader) train_acc = train_acc / len(dataloader) return train_loss, train_acc def test_step(...
基本上都是用to函数来将计算放到gpu上。或者也可以在创建对象的时候指定设备,再看例子。 X = torch.rand(1, 28, 28, device=device) logits = model(X) pred_probab = nn.Softmax(dim=1)(logits) y_pred = pred_probab.argmax(1) print(f"Predicted class: {y_pred}") 来源:维度攻城狮...