告密者的下场

loss=loss_func(outputs,labels)

_,pred=torch.max(outputs.data,dim=1)

acc=pred.eq(labels.data).cpu().sum()

one=torch.ones_like(labels)

zero=torch.zeros_like(labels)

tn=((labels==zero)*(pred==zero)).sum()

tp=((labels==one)*(pred==one)).sum()

fp=((labels==zero)*(pred==one)).sum()

fn=((labels==one)*(pred==zero)).sum()

test_sum_fn+=fn.item()

test_sum_fp+=fp.item()

test_sum_tn+=tn.item()

test_sum_tp+=tp.item()

sum_loss+=loss.item()

sum_correct+=acc.item()

test_precision=test_sum_tp*1.0/(test_sum_fp+test_sum_tp)

test_recall=test_sum_tp*1.0/(test_sum_fn+test_sum_tp)

test_loss=sum_loss*1.0/len(testDataLoader)

test_correct=sum_correct*1.0/len(testDataLoader)/batch_size

writer.add_scalar(“testloss“,test_loss,global_step=epoch+1)

writer.add_scalar(“testcorrect“,test_correct,gl