We have mentioned how to use the data, how to build the network, how to use the loss function, and now we talk about backpropagation and the optimizer. 为什么要使用反向传播?Why we use backpropagation? 这是因为我们从网络架构出来的结果与我们的Ground Truth进行对比误差,再送回网络进行训练,降低...
Vector of dimensionality batch_size loss = criterion(outputs, labels) loss.backward() optimizer.step() running_loss += loss.item() print(f"Epoch {epoch+1}/{epochs}, Loss: {running_loss / len(train_loader)}") def test(model, test_loader, device): model.to(device) model.eval() correc...
optimizer.step()# 每100个batch计算当前的损失,并在所有进程中进行聚合然后打印if(batch_idx +1) %100==0:# 将当前的loss转换为tensor,并在所有进程间进行求和loss_tensor = torch.tensor([loss.item()]).cuda(rank) dist.all_reduce(loss_tensor)# 计算所有进程的平均损失mean_loss = loss_tensor.item(...
在将device 设置为 GPU 时,.to(device) 是一种将设备参数(和缓存器)发送到 GPU 的便捷方式,且在将 device 设置为 CPU 时不会做任何处理。在将网络参数传递给优化器之前,把它们传递给适当的设备非常重要,不然的话优化器不能正确地追踪参数。 神经网络(nn.Module)和优化器(optim.Optimizer)都能保存和加载它们...
to(rank) loss_fn(outputs, labels).backward() optimizer.step() def main(): world_size = 2 mp.spawn(example, args=(world_size,), nprocs=world_size, join=True) if __name__=="__main__": main() 2.2 多机分布式 多机的启动方式可以是直接传递参数并在代码内部解析环境变量,或者通过torch....
log_softmax(x, dim=1) return output def train(args, model, device, train_loader, optimizer, epoch): model.train() for batch_idx, (data, target) in enumerate(train_loader): data, target = data.to(device), target.to(device) optimizer.zero_grad() output = model(data) loss = F.nll...
optimizer . zero_grad ()# calculate new gradients # apply new gradients optimizer . step ()并不是所有的变量都可以自动更新。但是你应该可以从最后一段代码中看到重点：我们仍然需要在计算新梯度之前将它手动归零。这是 PyTorch 的核心理念之一。有时我们会不太明白为什么要这么做，但另一方面，这样...
Optimizer This model uses SGD with momentum optimizer with the following hyperparameters: Momentum (0.875) Learning rate (LR) = 0.256 for 256 batch size, for other batch sizes we linearly scale the learning rate. Learning rate schedule - we use cosine LR schedule ...
import torch import torch.nn.functional as F from datasets import load_dataset+from accelerate import Accelerator-device = 'cpu'+accelerator = Accelerator()-model = torch.nn.Transformer().to(device)+model = torch.nn.Transformer()optimizer = torch.optim.Adam(model.parameters()) dataset = load_...
Table Notes (click to expand) All checkpoints were trained for 90 epochs using the SGD optimizer withlr0=0.001andweight_decay=5e-5at an image size of 224 pixels, using default settings. Training runs are logged athttps://wandb.ai/glenn-jocher/YOLOv5-Classifier-v6-2. ...