shuffle=True)# 定义一个函数来计算top5准确率deftop5_accuracy(output,target,topk=(1,)):withtorch.no_grad():maxk=max(topk)batch_size=target.size(0)_,pred=output.topk(maxk,1,True,True)pred=pred.t()correct=pred.eq(target.view(1,-1).expand_as(pred))res=[]forkintopk:correct_k=correc...
deftop_k_accuracy(output,target,k=5):withtorch.no_grad():batch_size=target.size(0)_,pred=output.topk(k,1,True,True)pred=pred.t()correct=pred.eq(target.view(1,-1).expand_as(pred))returncorrect[:k].reshape(-1).float().sum(0,keepdim=True).mul_(100.0/batch_size)# 测试模型并计...
Pytorch实现Top1准确率和Top5准确率 之前一直不清楚Top1和Top5是什么,其实搞清楚了很简单,就是两种衡量指标,其中,Top1就是普通的Accuracy,Top5比Top1衡量标准更“严格”, 具体来讲,比如一共需要分10类,每次分类器的输出结果都是10个相加为1的概率值,Top1就是这十个值中最大的那个概率值对应的分类恰好正确的...
Pytorch实现Top1准确率和Top5准确率 Pytorch实现Top1准确率和Top5准确率 之前⼀直不清楚Top1和Top5是什么，其实搞清楚了很简单，就是两种衡量指标，其中，Top1就是普通的Accuracy，Top5⽐Top1衡量标准更“严格”，具体来讲，⽐如⼀共需要分10类，每次分类器的输出结果都是10个相加为1的概率值，Top1就是...
[prec1, prec5], class_to = accuracy(output, target, topk=(1, 5)) losses.update(loss.item(), input.size(0)) top1.update(prec1[0], input.size(0)) top5.update(prec5[0], input.size(0)) # compute gradient and do SGD step ...
torch.max(output.data,1) correct+=torch.sum(pred==target) print_loss=loss.data.item() test_loss+=print_loss correct=correct.data.item() acc=correct/total_num avgloss=test_loss/len(test_loader) print('\nValset:Averageloss:{:.4f},Accuracy:{}/{}({:.0f}%)\n'.format( avgloss,...
>preds_correct=get_num_correct(train_preds,train_set.targets)>print('total correct:',preds_correct)>print('accuracy:',preds_correct/len(train_set))total correct:53578accuracy:0.8929666666666667 我们可以看到正确预测的总数,并通过除以训练集中的样本数来打印准确性。
global toppredicted ##当预测率大于原来的保存模型ifcurrentpredicted>toppredicted:toppredicted=currentpredicted torch.save(model.state_dict(),savemodel_name)print(savemodel_name+" saved, currentpredicted:%d %%"%currentpredicted)print('Accuracy on test set: %d %%'%currentpredicted)##开始训练 ...
accuracy = 0 model.eval() with torch.no_grad():for inputs, labels in testloader: inputs, labels = inputs.to(device), labels.to(device) logps = model.forward(inputs) batch_loss = criterion(logps, labels) test_loss += batch_loss.item()ps = torch.exp(logps) top_p, top_class ...
All checkpoints are trained to 90 epochs with SGD optimizer with lr0=0.001 and weight_decay=5e-5 at image size 224 and all default settings.Runs logged to https://wandb.ai/glenn-jocher/YOLOv5-Classifier-v6-2 Accuracy values are for single-model single-scale on ImageNet-1k dataset.Reproduc...