for batch_idx, (data, target) in enumerate(train_loader): data = data.view(-1, 28*28) logits = forward(data) loss = criteon(logits, target) optimizer.zero_grad() loss.backward() # print(w1.grad.norm(), w2.grad.norm()) optimizer.step() if batch_idx % 100 == 0: print('Train...
total_loss/len(train_loader),epoch_time,compution_time))returntotal_loss/len(train_loader)deftest():model.eval()withtorch.no_grad():test_loss=0correct=0fordata,targetintest_loader:ifuse_cuda:data=data
=model(data)loss=F.nll_loss(output,target)optimizer.zero_grad()loss.backward()optimizer.step()compution_time+=time.time()-b_sp# optimizer.step()epoch_time=time.time()-e_spprint('Train Epoch:{}\tLoss:{:.6f}\tepoch time:{:.6f}s\tepoch compution time:{:.6f}s'.format(epoch,total_...
首先,我们需要准备数据。在 PyTorch 中,我们可以使用torch.utils.data.Dataset和torch.utils.data.DataLoader来加载数据集。例如,假设我们有一个图像分类数据集,代码如下: importtorchfromtorchvisionimportdatasets,transformsfromtorch.utils.dataimportDataLoader# 定义数据变换transform=transforms.Compose([transforms.Resize((2...
batch_idx作用 batch_idx * len(data) 这里的batch_idx 就是数组分组之后的组号,len(data)就是每组的数据量,这个式子表示的就是总共已经训练的数据总数 做显示,根据这个已经训练好的得到百分占比,损失函数计算 https://www.cnblogs.com/lishikai/p/12366561.html平均准确度和平均损失的计算...
defdata_generator(data,targets,batch_size):idx=np.arange(len(data))np.random.shuffle(idx)batches=[idx[range(batch_size*i,min(len(data),batch_size*(i+1)))]foriinrange(len(data)/batch_size+1)]whileTrue:foriinbatches:xx,yy=np.array(map(gen_matrix,data[i])),np.array(map(gen_target...
idx= start +self.offset_per_class[c] batch.append(idx) self.offset_per_class[c]+= 1assertlen(batch) ==self.batch_size#random.shuffle(batch)yieldbatch batch=[] i+= 1def__len__(self):returnlen(self.data_source) //self.batch_size#单卡版本deftest_1(): ...
tasks = [] for idx, input_file in enumerate(resource_input_files): command = f"/bin/bash -c \"cat {input_file.file_path}\"" tasks.append(batchmodels.TaskAddParameter( id=f'Task{idx}', command_line=command, resource_files=[input_file] ) ) batch_service_client.task.add_collection(...
batch size影响的是单个epoch前向传播的总时间。当GPU资源有限的时候，forward函数的运行时间正比于batch ...
class LabelSmoothingCrossEntropy(nn.Layer): def __init__(self, smoothing=0.1): super().__init__() self.smoothing = smoothing def forward(self, pred, target): confidence = 1. - self.smoothing log_probs = F.log_softmax(pred, axis=-1) idx = paddle.stack([paddle.arange(log_probs....