for i, x in enumerate(train_loader): x = x.to(cuda0) 1. 2. 3. 在系统上使用多个 GPU 时,您可以使用 CUDA_VISIBLE_DEVICES 环境标志来管理 PyTorch 可用的 GPU。如上所述,要手动控制在哪个 GPU 上创建张量,最佳做法是使用 torch.cuda.device 上下文管理器。 print("外部的设备是0") # 在设备0上...
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 ...
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...
The coefficientageThe coefficientsex_femaleThe coefficientforsex_maleis-8.762584065506853The coefficientforbmiis0.3807106266997645The coefficientforchildren_0is-0.06605803000190659The coefficientforchildren_1is-0.946643170369065The coefficientforchildren_2is0.2108032984623088The coefficientforchildren_3is0.8800441822437507The...
def train(model, device, train_loader, optimizer, epoch): global batch_size # model.train() state = model.zero_state(batch_size) for batch_idx, (data, target) in enumerate(train_loader): print(f"The batch_idx value is {batch_idx}") ...
That can be used during a penetration test to enumerate and maybe can be used in CTF for manipulation,combine,transform and permutation some words or file text :p [292星][2m] [Py] christhecoolhut/pinctf Using Intel's PIN tool to solve CTF problems [275星][11m] [Py] hongrisec/ctf-...
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...
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()# ...
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 ...
for step, (x_batch_train, y_batch_train) in enumerate(train_dataset): # Open a GradientTape to record the operations run # during the forward pass, which enables autodifferentiation. with tf.GradientTape() as tape: # Run the forward pass of the layer. ...