当使用enumerate(dataloader)时,每次迭代都会从dataloader中获取一个元素(通常是一个批次的数据),并将其与当前迭代的索引一起赋值给idx和batch_x。 idx是当前的迭代索引(从0开始),而batch_x是当前批次的数据。 描述for idx, batch_x in enumerate(dataloader):这行代码的整体工作流程: 这行代码启动了一个循环,该...
for i, data in enumerate(train_loader): #从dataloader里面一步步取数据 optimizer.zero_grad() # 用 optimizer 将 model 参数的 gradient 清零,防止上一步训练出来的gradient影响这一步的训练 train_pred = model(data[0].cuda()) # 利用 model 得到预测的概率分步 这样实际上是在使用 model 的 forward ...
criterion = nn.CrossEntropyLoss() total_iter = 0 epochs = 0 print_freq = 50 while total_iter < max_iter: running_loss = 0.0 for i, data in enumerate(trainloader, 0): inputs, labels = data inputs, labels = Variable(inputs.cuda()), Variable(labels.cuda()) optimizer.zero_grad() ...
239 241 for i, dataloader in enumerate(dataloaders): @@ -245,24 +247,25 @@ def load_accelerator_state( 245 247 if isinstance(dataloader.dataset, IterableDatasetShard): 246 248 sampler = dataloader.get_sampler() 247 249 if isinstance(sampler, SeedableRandomSampler): 248 - sampler =...
# Train for 2 epochsforepochinrange(2):running_loss=0.0fori,datainenumerate(hub_loader):images=data['images']labels=data['labels']# zero the parameter gradientsoptimizer.zero_grad()# forward + backward + optimizeoutputs=net(images)loss=criterion(outputs,labels.reshape(-1))loss.backward()optim...
epoch_loss = 0 epoch_acc = 0 for i, batch in enumerate(dataloader): # 标签形状为 (batch_size, 1) label = batch["label"] text = batch["text"] # tokenized_text 包括 input_ids, token_type_ids, attention_mask tokenized_text = tokenizer(text, max_length=100, add_special_tokens=True...
我看到有人使用torch.utils.data.DataLoader来完成这项任务，所以我更改了代码，改为使用DataLoader。
(device),attention_mask=data[2].squeeze(dim=0).to(device))result.append(evaluate(data,output))result_file="result.csv"withopen(result_file,'w')asf:f.write("ID,Answer\n")fori,test_questioninenumerate(test_questions):# Replace commas in answers with empty strings (since csv is separated ...
(input_batch, target_batch) in enumerate(data_loader): if i < num_batches: input_batch, target_batch = input_batch.to(device), target_batch.to(device) with torch.no_grad(): logits = model(input_batch)[:, -1, :] # Logits of last output token predicted_labels = torch.argmax(...
3 print('Reading Data: ' + observation) 1. 2. 3. 采样数据 将部分的数据读取以备使用。 规律性采样: 1 n = 3 2 with open("Colors.txt", 'r') as open_file: 3 for j, observation in enumerate(open_file): 4 if j % n==0: ...