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pytorch-cn's Introduction

pytorch-cn


本项目由awfssv, ycszen, KeithYin, kophy, swordspoet, dyl745001196, koshinryuu, tfygg, weigp, ZijunDeng, yichuan9527等PyTorch爱好者发起,并已获得PyTorch官方授权。我们目的是建立PyTorch的中文文档,并力所能及地提供更多的帮助和建议。

本项目网址为pytorch-cn,文档翻译QQ群:628478868

如果你在使用pytorch和pytorch-cn的过程中有任何问题,欢迎在issue中讨论,可能你的问题也是别人的问题。

翻译进度

现在正在进行审阅任务(大家赶紧认领吧~) 第一个名字代表翻译人,第二个代表审阅人

Notes

  • Autograd mechanics (ycszen)(DL-ljw)
  • CUDA semantics (ycszen)
  • Extending PyTorch (KeithYin)
  • Multiprocessing best practices (ycszen)
  • Serialization semantics (ycszen)

Package Reference

  • torch(koshinryuu)(飞彦)
  • torch.Tensor(weigp)(飞彦)
  • torch.Storage(kophy)
  • torch.nn
    • Parameters(KeithYin)
    • Containers(KeithYin)
    • Convolution Layers(yichuan9527)
    • Pooling Layers(yichuan9527)
    • Non-linear Activations(swordspoet)
    • Normalization layers(XavierLin)
    • Recurrent layers(KeithYin)(Mosout)
    • Linear layers( )(Mosout)
    • Dropout layers( )(Mosout)
    • Sparse layers(Mosout)
    • Distance functions
    • Loss functions(KeithYin)(DL-ljw)
    • Vision layers(KeithYin)
    • Multi-GPU layers(KeithYin)
    • Utilities(KeithYin)
  • torch.nn.functional
    • Convolution functions(ycszen)(铁血丹心)
    • Pooling functions(ycszen)(铁血丹心)
    • Non-linear activations functions(ycszen)
    • Normalization functions(ycszen)
    • Linear functions(dyl745001196)
    • Dropout functions(dyl745001196)
    • Distance functions(dyl745001196)
    • Loss functions(tfygg)(DL-ljw)
    • Vision functions(KeithYin)
  • torch.nn.init(kophy)(luc)
  • torch.optim(ZijunDeng)(祁杰)
  • torch.autograd(KeithYin)(祁杰)
  • torch.multiprocessing(songbo.han)
  • torch.legacy(ycszen)
  • torch.cuda(ycszen)
  • torch.utils.ffi(ycszen)
  • torch.utils.data(ycszen)
  • torch.utils.model_zoo(ycszen)

torchvision Reference

  • torchvision (KeithYin)
  • torchvision.datasets (KeithYin)(loop)
  • torchvision.models (KeithYin)
  • torchvision.transforms (KeithYin)(loop)
  • torchvision.utils (KeithYin)

pytorch-cn's People

Contributors

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pytorch-cn's Issues

torch.range 及 torch.arange区间说明错误

两个方法的结果区间和原始文档及实际测试结果写反了。
原文如下:
torch.arange: Returns a 1-D tensor of size ⌊end−startstep⌋ with values from the interval [start, end) taken with common difference step beginning from start.
torch.range: Returns a 1-D tensor of size ⌊end−startstep⌋+1 with values from start to end with step step. Step is the gap between two values in the tensor.

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