output.append(int(Y.argmax(dim=1).item()))return''.join([idx_to_char[i]foriinoutput]) 开发者ID:wdxtub,项目名称:deep-learning-note,代码行数:20,代码来源:utils.py 示例4: calculate_outputs_and_gradients ▲点赞 6▼ # 需要导入模块: import torch [as 别名]# 或者: from torch importargma...
pred = output.argmax(dim=1, keepdim=True) # get the index of the max log-probability correct += pred.eq(target.view_as(pred)).sum().item() test_loss /= len(test_loader.dataset) print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format( test_loss...
outputs, aux_outputs=model(inputs) loss1=criterion(outputs, labels) loss2=criterion(aux_outputs, labels) loss= loss1 + 0.4 *loss2else: outputs=model(inputs) loss=criterion(outputs, labels) pred= torch.argmax(outputs, 1)ifphase =='train': loss.backward() optimizer.step()#计算损失值runni...
super(SoftmaxLayer, self).__init__() def forward(self, X): X_exp = X.exp() # 对每个元素做指数运算 partition = X_exp.sum(dim=1, keepdim=True) # 求列和,即对同行元素求和 n*1 return X_exp / partition # broadcast net = torch.nn.Sequential( FlattenLayer(), torch.nn.Linear(num...
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' in net.__code__.co_varnames: acc_sum += (net(X, is_training=False).argmax(dim=1) == y).float().sum().item() else: acc_sum += ...
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, ...
argmax(dim=1) * (coarse_segm_bbox > 0).long() ) return labels def resample_uv_tensors_to_bbox( u: torch.Tensor, v: torch.Tensor, labels: torch.Tensor, box_xywh_abs: IntTupleBox, ) -> torch.Tensor: """ Resamples U and V coordinate estimates for the given bounding box Args:...
acc_sum += (net(X).argmax(dim=1) == y).float().sum().item() n += y.shape[0]returnacc_sum / n 开发者ID:wdxtub,项目名称:deep-learning-note,代码行数:19,代码来源:utils.py 示例6: train_cnn ▲点赞 6▼ # 需要导入模块: import torch [as 别名]# 或者: from torch importdevice...
pytorch-第五章使⽤迁移学习实现分类任务-torchvison torchvision 主要是由三⼤模块组成, model, transforms, datasets transforms 主要可以进⾏数据增强 datasets 主要下载⼀些常⽤的数据集如mnist数据集 model 主要是将原来的模型进⾏下载 第⼀部分: 数据集的准备⼯作 第⼀步: 使⽤transforms...
outputs = torch.stack(inputs, dim=0) torch.stack((R, spatial_R), dim=1) 和torch.stack的区别是stack会新增一个维度,cat不会 torch.cat 将两个tensor拼接在一起。 也可以多个,不只是两个 和torch.stack的区别是stack会新增一个维度,cat不会 C = torch.cat( (A,B),0 ) #按维数0拼接(竖着拼)...