DOC Adds code comment for _ConvNd.reset_parameters #58931
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💊 CI failures summary and remediationsAs of commit 0e77bc5 (more details on the Dr. CI page):
🕵️ 3 new failures recognized by patternsThe following CI failures do not appear to be due to upstream breakages:
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| Job | Step | Action |
|---|---|---|
 Windows CI (pytorch-win-vs2019-cpu-py3) / render_test_results |
Unknown | 🔁 rerun |
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jbschlosser
approved these changes
May 25, 2021
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| def reset_parameters(self) -> None: | ||
| # Setting a=sqrt(5) in kaiming_uniform is the same as initializing with | ||
| # uniform(-1/sqrt(k), 1/sqrt(k)), where k = weight.size(1) * prod(*kernel_size) |
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Can you also add a line to the effect of: "See for more details." where link points to Soumith's comment explaining the calculation (#15314 (comment))
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@jbschlosser merged this pull request in 8130f2f. |
deniskokarev
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Jun 9, 2021
Summary: Fixes pytorch#55741 by adding a comment regarding the behavior of `kaiming_uniform_` The docstring is correct in this case. For example: ```python import math import matplotlib.pyplot as plt import torch import torch.nn as nn in_channels = 120 groups = 2 kernel = (3, 8) m = nn.Conv2d(in_channels=in_channels, groups=groups, out_channels=100, kernel_size=kernel) k = math.sqrt(groups / (in_channels * math.prod(kernel))) print(f"k: {k:0.6f}") print(f"min weight: {m.weight.min().item():0.6f}") print(f"max weight: {m.weight.max().item():0.6f}") ``` outputs: ``` k: 0.026352 min weight: -0.026352 max weight: 0.026352 ``` And when we plot the distribution, it is uniform with the correct bounds: ```python _ = plt.hist(m.weight.detach().numpy().ravel()) ```  Pull Request resolved: pytorch#58931 Reviewed By: anjali411 Differential Revision: D28689863 Pulled By: jbschlosser fbshipit-source-id: 98eebf265dfdaceed91f1991fc4b1592c0b3cf37
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Fixes #55741 by adding a comment regarding the behavior of
kaiming_uniform_The docstring is correct in this case. For example:
outputs:
And when we plot the distribution, it is uniform with the correct bounds: