no_grad(): for data, target in test_loader: output = model(data) pred = output.argmax(dim=1, keepdim=True) correct += pred.eq(target.view_as(pred)).sum().item() print(f'Accuracy: {100. * correct / len(test_loader.dataset)}') Typically from here, one could...
defforward(self,x):x=F.sigmoid((x-self.params[0])**9)returntorch.sum(x,dim=-1,keepdim=...
no_grad(): predictions = [] true_labels = [] for batch in test_dataloader: input_ids, attention_mask, labels = tuple(t.to(device) for t in batch) outputs = model(input_ids, attention_mask=attention_mask) _, predicted_labels = torch.max(outputs.logits, dim=1) predictions.extend(...
并在结果中保留列特征 print("2)a",X.sum(dim=1, keepdim=True)) # dim为1,按照相同的行求和,并在结果中保留行特征 print("1)b",X.sum(dim=0, keepdim=False)) # dim为0,按照相同的列求和,不在结果中保留列特征 print("1)b",X.sum(dim=1, keepdim=False)) # dim为1,按照相同的行求和,...
output = ddp_model(data) pred = output.argmax(dim=1, keepdim=True) correct += pred.eq(target.view_as(pred)).sum().item() print(f'Accuracy: {100. * correct / len(test_loader.dataset)}') 0 comments on commit e1cf44c Please sign in to comment. Footer...
Saved searches Use saved searches to filter your results more quickly Cancel Create saved search Sign in Sign up Reseting focus {{ message }} yemu1138178251 / FFCA-YOLO Public Notifications You must be signed in to change notification settings Fork 3 Star 117 ...
294 + pred = output.argmax(dim=1, keepdim=True) 295 + correct += pred.eq(target.view_as(pred)).sum().item() 296 + print(f'Accuracy: {100. * correct / len(test_loader.dataset)}') 297 + ``` 298 + 299 + With this your PyTorch training loop is now setup to be ran...
no_grad(): for data, target in test_loader: output = model(data) pred = output.argmax(dim=1, keepdim=True) correct += pred.eq(target.view_as(pred)).sum().item() print(f'Accuracy: {100. * correct / len(test_loader.dataset)}') Typically from here, one could ...
no_grad(): for data, target in test_loader: output = model(data) pred = output.argmax(dim=1, keepdim=True) correct += pred.eq(target.view_as(pred)).sum().item() print(f'Accuracy: {100. * correct / len(test_loader.dataset)}') Typically from here, one cou...