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https://github.com/zebrajr/pytorch.git
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7fcaf3b49e
Introducing two updates. 1. Add param to He initialization scheme in torch.nn.init Problem solved: The function calculate_gain can take an argument to specify the type of non-linearity used. However, it wasn't possible to pass this argument directly to the He / Kaiming weight initialization function. 2. Add util to clip gradient value in torch.nn.utils.clip_grad Problem solved: DL libraries typically provide users with easy access to functions for clipping the gradients both using the norm and a fixed value. However, the utils clip_grad.py only had a function to clip the gradient norm. * add param to He initialization scheme in torch.nn.init * add util to clip gradient value in torch/nn/utils/clip_grad.py * update doc in torch.nn.utils.clip_grad * update and add test for torch.nn.utils.clip_grad * update function signature in torch.nn.utils.clip_grad to match suffix_ convention * ensure backward compatibility in torch.nn.utils.clip_grad * remove DeprecationWarning in torch.nn.utils.clip_grad * extend test and implementation of torch.nn.utils.clip_grad * update test and implementation torch.nn.utils.clip_grad |
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| .. | ||
| _static | ||
| _templates | ||
| notes | ||
| scripts | ||
| autograd.rst | ||
| bottleneck.rst | ||
| checkpoint.rst | ||
| conf.py | ||
| cpp_extension.rst | ||
| cuda.rst | ||
| data.rst | ||
| device.rst | ||
| distributed.rst | ||
| distributions.rst | ||
| ffi.rst | ||
| index.rst | ||
| legacy.rst | ||
| model_zoo.rst | ||
| multiprocessing.rst | ||
| nn.rst | ||
| onnx.rst | ||
| optim.rst | ||
| sparse.rst | ||
| storage.rst | ||
| tensors.rst | ||
| torch.rst | ||