【PaddlePaddle Hackathon】1、在 Paddle 中新增 AdaptiveLogSoftmaxWithLoss #37024
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Thanks for your contribution! |
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@zhiboniu 您好，请问iscan的报错怎么看呀，点进ditails看不到 |
zhiboniu
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Nov 10, 2021
python/paddle/fluid/tests/unittests/test_adaptive_log_softmax_with_loss.py
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python/paddle/fluid/tests/unittests/test_adaptive_log_softmax_with_loss.py
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python/paddle/fluid/tests/unittests/test_adaptive_log_softmax_with_loss.py
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@zhiboniu 已更新 |
zhiboniu
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Nov 15, 2021
python/paddle/fluid/tests/unittests/test_adaptive_log_softmax_with_loss.py
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zhiboniu
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Nov 29, 2021
| class TestNNAdaptiveLogSoftmaxWithLossAPI(unittest.TestCase): | ||
| def setUp(self): | ||
| paddle.disable_static() | ||
| paddle.seed(10) |
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Sorry to inform you that c934522's CIs have passed for more than 7 days. To prevent PR conflicts, you need to re-run all CIs manually. |
zhiboniu
reviewed
Dec 10, 2021
| # argmax outside of shortlist | ||
| asfm = nn.AdaptiveLogSoftmaxWithLoss( | ||
| 8, 10, [4, 8], div_value=2., head_bias=True) | ||
| asfm.head.weight.detach().abs() |
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参考上面两个对话，你当前还是没有能验证实现的正确性。
以test_linear.py为例，可以按如下步骤完成：
1）固定可学习参数权重值为1。
2）获得模型推理结果概率分布，不仅仅是argmax值。
3）使用numpy实现推理公式。
4）使用同一个随机输入验证paddle输出概率分布与numpy计算结果相同。
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很抱歉，经过我们的反复讨论，你的PR暂未达到合入标准，请阅读飞桨原生算子开发规范，你可以重新提交新的PR，我们先将此PR关闭，感谢你的贡献。 |
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