If you want to use enumerate with tqdm, you can use it this way: for i,data in enumerate(tqdm(train_dataloader)): images, labels = data images, labels = images.to(device), labels.to(device) ... Share Improve this answer Follow answered Mar 2, 2023 at 13:42 Hamzah Al-Qadasi ...
关于数据集,源码中是367天的股票数据,其损失函数使用的是:nn.MSELoss(),用来计算两个特征的差值的平方和的均值。 for i, (batch_x, batch_y, batch_x_mark, batch_y_mark) in enumerate(train_loader): iter_count += 1 model_optim.zero_grad() batch_x = batch_x.float().to(self.device) # ...
for i, (inputs, labels) in enumerate(train_loader): # 遍历训练集的每个批次 inputs = inputs.to(device) # 将输入数据移动到指定设备 labels = labels.to(device) # 将标签数据移动到指定设备 optimizer.zero_grad() # 将优化器中的梯度清零 outputs = model(inputs) # 将输入数据喂入模型,得到输出...
def train(model, dataloader, optimizer, criterion, device): model.train() 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...
model.train() 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
train(train_loader, model, criterion, optimizer, epoch, cur_lr) File "train.py", line 134, in train for i, (input, target) in enumerate(train_loader): File "/root/anaconda3/envs/caffe-tf/lib/python3.6/site-packages/torch/utils/data/dataloader.py", line 322, innext ...
forepochinrange(2):running_loss=0.0fori,datainenumerate(deeplake_loader):images,labels=data['images'],data['labels']# zero the parameter gradientsoptimizer.zero_grad()# forward + backward + optimizeoutputs=net(images)loss=criterion(outputs,labels.reshape(-1))loss.backward()optimizer.step()# ...
计算loss时的代码如下,和上面说的策略一致,但是很奇怪在有监督的train_supervised上也是相同的代码。 for batch_idx, (inputs, labels, descriptions) in enumerate(loader): with autocast(): optimizer.zero_grad() class_tokens = clip.tokenize(descriptions, context_length=77, truncate=True) inputs, class...
In [ ] min_loss=10000 step = 0 for epoch in range(epoch_num): for i, (data, label) in enumerate(train_loader): network.train() data = data.to(device) label = label.to(device) optimizer.zero_grad() outputs= network(data) train_loss = criterion(outputs,label) train_loss.backward(...
The coefficientageThe coefficientsex_femaleThe coefficientforsex_maleis-8.762584065506853The coefficientforbmiis0.3807106266997645The coefficientforchildren_0is-0.06605803000190659The coefficientforchildren_1is-0.946643170369065The coefficientforchildren_2is0.2108032984623088The coefficientforchildren_3is0.8800441822437507The...