在Ubuntu上,有多种可视化工具可用于PyTorch,以下是一些推荐的工具及其使用方法:
torch.utils.tensorboard
模块。pip install tensorboard
from torch.utils.tensorboard import SummaryWriter writer = SummaryWriter("runs") for epoch in range(num_epochs): # 训练代码 writer.add_scalar("Loss/train", loss, epoch) writer.add_graph(model, input_tensor) writer.close()
tensorboard --logdir=runs
在浏览器中访问 http://localhost:6006
查看可视化结果。pip install torchviz graphviz
from torchviz import make_dot input_tensor = torch.randn(1, 3, 224, 224) output = model(input_tensor) dot = make_dot(output, params=dict(model.named_parameters())) dot.render("model_graph", format="png")
.pt
或.onnx
格式。pip install netron
pip install visdom
import visdom viz = visdom.Visdom() viz.line(Y=[loss], X=[epoch], win="loss", update="append")
pip install wandb wandb.init(project="my_project") wandb.log({"loss": loss, "accuracy": acc})
pip install captum
from captum.attr import IntegratedGradients ig = IntegratedGradients(model) attr = ig.attribute(input, target=label)
这些工具可以帮助你更好地理解和调试PyTorch模型,选择合适的工具取决于你的具体需求。