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补充GaussianNLLLoss中文文档。;test=docs_preview #5623
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整体错误较多,请仔细参考 api文档写作规范!! 再进行修改
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.. _cn_api_paddle_nn_GaussianNLLLoss: | |||
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SmoothL1Loss |
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SmoothL1Loss?是不是写错了
参数 | ||
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- **full** (bool) - 是否在损失计算中包括常数项。默认情况下为 False。 |
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- 英文这边参数都有“可选”,即(bool,可选),中英文请保持一致。 下面的参数也同样注意
- 另外,请增加默认值为false代表什么意思,即
默认情况下为 False,代表...
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- **full** (bool) - 是否在损失计算中包括常数项。默认情况下为 False。 | ||
- **epsilon** (float) - 用于限制 variance 的值,使其不会导致除 0 的出现。默认值为 1e-6 |
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增加句号
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- **full** (bool) - 是否在损失计算中包括常数项。默认情况下为 False。 | ||
- **epsilon** (float) - 用于限制 variance 的值,使其不会导致除 0 的出现。默认值为 1e-6 | ||
- **reduction** (str,可选) - 指定应用于输出结果的计算方式,可选值有 ``none``、``mean`` 和 ``sum``。默认为 ``mean``,计算 ``mini-batch`` loss 均值。设置为 `sum` 时,计算 `mini-batch` loss 的总和。设置为 ``none`` 时,则返回 loss Tensor。 |
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该行最后的 mini-batch
用双引号
- **reduction** (str,可选) - 指定应用于输出结果的计算方式,可选值有 ``none``、``mean`` 和 ``sum``。默认为 ``mean``,计算 ``mini-batch`` loss 均值。设置为 `sum` 时,计算 `mini-batch` loss 的总和。设置为 ``none`` 时,则返回 loss Tensor。 | ||
- **name** (str,可选) - 具体用法请参见 :ref:`api_guide_Name`,一般无需设置,默认值为 None。 | ||
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输入 |
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输入?应该是形状吧
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- **input** (Tensor):输入 :attr:`Tensor`,其形状为 :math:`[N, *]`,其中 :math:`*` 表示任何数量的额外维度。 | ||
- **label** (Tensor):输入 :attr:`Tensor`, 形状、数据类型和 :attr:`input` 相同。 | ||
- **variance** (Tensor): 输入 :attr:`Tensor`,形状和 :attr:`input` 相同。 |
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这部分描述好像和英文没有一致?请再检查一遍,务必做到中英文描述是一致的
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.. py:function:: paddle.nn.functional.gaussian_nll_loss(input, label, variance, full=False, epsilon=1e-6, reduction='mean', name=None) | ||
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返回 `gaussian negative log likelihood loss`。可在 :ref:`_cn_api_paddle_nn_GaussianNLLLoss` 查看详情。 |
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:ref:_cn_api_paddle_nn_GaussianNLLLoss
引用方式不对, _cn_api_paddle_nn_GaussianNLLLoss 最前面的下划线应该去掉
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.. _cn_api_nn_functional_nll_loss: | |||
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nll_loss |
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api名字也写错了吧?,应该是 gaussian_nll_loss
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.. _cn_api_nn_functional_nll_loss: |
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这样会造成误解,改成.. _cn_api_nn_functional_gaussian_nll_loss:
代码示例 | ||
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COPY-FROM: paddle.nn.functional.nll_loss |
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copy from也错了,应该是copy的是 gaussian_nll_loss 的代码
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除comment里提到的问题之外,还有两个内容需要补充
- 在 api_label 中加入新增的两个api的label(label是每格rst第一行去掉cn)
- 在paddle.nn下的overview中增加对GaaussianNLLLoss的描述,参考:
https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/api/paddle/nn/Overview_cn.html#loss-layers
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返回 | ||
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`Tensor`,返回存储表示 `gaussian negative log likelihood loss` 的损失值。数据类型与:attr:`input`相同。当 reduction 为:attr:`none`时,形状与:attr:`input`相同。 |
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.. py:class:: paddle.nn.GaussianNLLLoss(full=False, epsilon=1e-6, reduction='mean', name=None) | ||
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计算输入 :attr:`input` 和标签 :attr:`label`、 :attr:`variance` 间的 GaussianNLL 损失, | ||
:attr:`label` 被视为高斯分布的样本,其期望和方差由神经网络预测给出。对于一个 :attr:`label` 张量建模为具有高斯分布的张量的期望值 :attr:`input` 和张量的正方差 :attr:`var`,数学计算公式如下: |
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总感觉这段话读起来不太通顺..尤其是最后一句 对于一个 label 张量建模为具有高斯分布的张量的期望值 input 和张量的正方差 var
,建议从头开始的整段描述再斟酌、优化一下
\text{loss} = \frac{1}{2}\left(\log\left(\text{max}\left(\text{var}, | ||
\ \text{epsilon}\right)\right) + \frac{\left(\text{input} - \text{label}\right)^2} | ||
{\text{max}\left(\text{var}, \ \text{epsilon}\right)}\right) + \text{const.} | ||
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:::::::::: | ||
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- **input** (Tensor):输入 :attr:`Tensor`,其形状为 :math:`(N, *)` 或者 :math:`(*)`,其中 :math:`*` 表示任何数量的额外维度。数据类型为 float32 或 float64。 | ||
- **label** (Tensor):输入 :attr:`Tensor`,其形状为 :math:`(N, *)` 或者 :math:`(*)`,形状与 :attr:`input` 相同,或者其中一维的大小为 1,这时会进行 broadcast 操作。数据类型为 float32 或 float64。 |
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“或者”后面的建议描述再清楚些,如 或者形状与input相同但....
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- **input** (Tensor):输入 :attr:`Tensor`,其形状为 :math:`(N, *)` 或者 :math:`(*)`,其中 :math:`*` 表示任何数量的额外维度。数据类型为 float32 或 float64。 | ||
- **label** (Tensor):输入 :attr:`Tensor`,其形状为 :math:`(N, *)` 或者 :math:`(*)`,形状与 :attr:`input` 相同,或者其中一维的大小为 1,这时会进行 broadcast 操作。数据类型为 float32 或 float64。 | ||
- **variance** (Tensor): 输入 :attr:`Tensor`,其形状为 :math:`(N, *)` 或者 :math:`(*)`,形状与 :attr:`input` 相同,或其中一维的大小为 1,或缺少一维,这时会进行 broadcast 操作。数据类型为 float32 或 float64。 |
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- 同样的,“或者”后面的描述建议清晰些
或缺少一维
这句话再斟酌一下,会造成误解- 英文文档还有 output的形状描述,请统一
参数 | ||
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- **input** (Tensor):输入 :attr:`Tensor`,其形状为 :math:`(N, *)` 或者 :math:`(*)`,其中 :math:`*` 表示任何数量的额外维度。将被拟合成为高斯分布。数据类型为 float32 或 float64。 | ||
- **label** (Tensor):输入 :attr:`Tensor`,其形状为 :math:`(N, *)` 或者 :math:`(*)`,形状、数据类型和 :attr:`input` 相同,或者其中一维的大小为 1,这时会进行 broadcast 操作。为服从高斯分布的样本。数据类型为 float32 或 float64。 |
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同样的,按上述要求对“或者”后的描述更清晰化一些
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内容写的很好,还有一些写作typo需要修改一下~
docs/api/paddle/nn/Overview_cn.rst
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@@ -258,6 +258,7 @@ Loss 层 | |||
" :ref:`paddle.nn.MarginRankingLoss <cn_api_nn_loss_MarginRankingLoss>` ", "MarginRankingLoss 层" | |||
" :ref:`paddle.nn.MSELoss <cn_api_paddle_nn_MSELoss>` ", "均方差误差损失层" | |||
" :ref:`paddle.nn.NLLLoss <cn_api_nn_loss_NLLLoss>` ", "NLLLoss 层" | |||
" :ref:`paddle.nn.GaussianNLLLoss <cn_api_nn_loss_GaussianNLLLoss>` ", "GaussianNLLLoss 层" |
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标签应当和 api/label 及 相应api文档第一行标签 一致
- 建议改为
cn_api_paddle_nn_GaussianNLLLoss
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.. _cn_api_nn_GaussianNLLLoss: |
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标签应当和 api/label 一致
- 建议改为
_cn_api_paddle_nn_GaussianNLLLoss
.. py:class:: paddle.nn.GaussianNLLLoss(full=False, epsilon=1e-6, reduction='mean', name=None) | ||
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该接口创建一个 GaussianNLLLoss 实例,计算输入 :attr:`input` 和标签 :attr:`label`、 :attr:`variance` 间的 GaussianNLL 损失, | ||
:attr:`label` 被视为服从高斯分布的样本,其期望:attr:`input`和方差:attr:`variance`由神经网络预测给出。 |
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.. py:function:: paddle.nn.functional.gaussian_nll_loss(input, label, variance, full=False, epsilon=1e-6, reduction='mean', name=None) | ||
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计算输入 :attr:`input` 、:attr:`variance` 和标签 :attr:`label` 间的 GaussianNLL 损失, | ||
:attr:`label` 被视为高斯分布的样本,其期望:attr:`input`和方差:attr:`variance`由神经网络预测给出。 |
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请修改,注意细节!
.. py:class:: paddle.nn.GaussianNLLLoss(full=False, epsilon=1e-6, reduction='mean', name=None) | ||
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该接口创建一个 GaussianNLLLoss 实例,计算输入 :attr:`input` 和标签 :attr:`label`、 :attr:`variance` 间的 GaussianNLL 损失, | ||
:attr:`label` 被视为服从高斯分布的样本,期望 :attr:`input` 和 方差:attr:`variance` 由神经网络预测给出。 |
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docs/api/paddle/nn/Overview_cn.rst
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@@ -481,6 +482,7 @@ Embedding 相关函数 | |||
" :ref:`paddle.nn.functional.margin_ranking_loss <cn_api_nn_cn_margin_ranking_loss>` ", "用于计算 margin rank loss 损失" | |||
" :ref:`paddle.nn.functional.mse_loss <cn_paddle_nn_functional_mse_loss>` ", "用于计算均方差误差" | |||
" :ref:`paddle.nn.functional.nll_loss <cn_api_nn_functional_nll_loss>` ", "用于计算 nll 损失" | |||
" :ref:`paddle.nn.functional.gaussian_nll_loss <n_api_paddle_nn_functional_gaussian_nll_loss>` ", "用于计算 gaussiannll 损失" |
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n_api_paddle_nn_functional_gaussian_nll_loss
最前面少了个c吧?
docs/api/paddle/nn/Overview_cn.rst
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@@ -258,6 +258,7 @@ Loss 层 | |||
" :ref:`paddle.nn.MarginRankingLoss <cn_api_nn_loss_MarginRankingLoss>` ", "MarginRankingLoss 层" | |||
" :ref:`paddle.nn.MSELoss <cn_api_paddle_nn_MSELoss>` ", "均方差误差损失层" | |||
" :ref:`paddle.nn.NLLLoss <cn_api_nn_loss_NLLLoss>` ", "NLLLoss 层" | |||
" :ref:`paddle.nn.GaussianNLLLoss <cn_api_paddle_nn_loss_GaussianNLLLoss>` ", "GaussianNLLLoss 层" |
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相应文档的rst第一行 标签是 cn_api_paddle_nn_GaussianNLLLoss
。中间没有loss,请修改
@Atlantisming 文档没有其他问题了,但该分支有个conflict需要解决下,初步看是因为之前有其他开发者新增了一个loss API,在overview里加了一行,刚好和你冲突了。 拉取最新的代码解决即可~ |
代码及英文文档链接:PaddlePaddle/Paddle#50843
rfc文档链接:PaddlePaddle/community#446