[SCU][Docathon][Add API Legend No.36]为tensordot API添加图例 -part #6971
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感谢你贡献飞桨文档，文档预览构建中，Docs-New 跑完后即可预览，预览链接：http://preview-pr-6971.paddle-docs-preview.paddlepaddle.org.cn/documentation/docs/zh/api/index_cn.html |
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docs/api/paddle/tensordot_cn.rst
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| @@ -5,7 +5,13 @@ tensordot | |||
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| .. py:function:: paddle.tensordot(x, y, axes=2, name=None) | |||
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| Tensor 缩并运算（Tensor Contraction），即沿着 axes 给定的多个轴对两个 Tensor 对应元素的乘积进行加和操作。 | |||
| Tensor 缩并运算（Tensor Tensordot）,即计算的是两个张量在某些轴上的乘积并求和。 | |||
docs/api/paddle/tensordot_cn.rst
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| Tensor 缩并运算（Tensor Contraction），即沿着 axes 给定的多个轴对两个 Tensor 对应元素的乘积进行加和操作。 | ||
| Tensor 缩并运算（Tensor Tensordot）,即计算的是两个张量在某些轴上的乘积并求和。 | ||
| 可以选择沿一个或多个轴进行点积操作,操作后返回的结果张量维度是 A 和 B 上未参与点积的维度的并集。 | ||
| 图例中展示了一个shape = [2,2,2]的a张量,和shape = [2,3]的b张量,res为a,b两个张量沿着axis = 1轴做tensordot的结果 |
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不是沿着 axis=1 轴，参考 axes 的参数说明 axes 可以是一个非负整数。若输入的是一个整数 n，则表示对 x 的后 n 个轴和对 y 的前 n 个轴进行缩并运算，是对 [2,2,2] 的最后一个轴和 [2,3] 的第一个轴进行缩并，最终取得 [2,2,3]
docs/api/paddle/tensordot_cn.rst
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| 可以选择沿一个或多个轴进行点积操作,操作后返回的结果张量维度是 A 和 B 上未参与点积的维度的并集。 | ||
| 图例中展示了一个shape = [2,2,2]的a张量,和shape = [2,3]的b张量,res为a,b两个张量沿着axis = 1轴做tensordot的结果 | ||
| res = paddle.tensordot(x, y, axes = 1) | ||
| .. image:: ../../images/api_legend/concat.png |
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整个文档改完后注意预览效果，根据预览效果再修改
docs/images/api_legend/tensordot.png
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- 每个 tensor 标上轴 axis
- 优化一下图以更加清晰
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辛苦老师看看还有什么问题 |
docs/images/api_legend/tensordot.png
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图例axis = 0 和 axis = 1 弄反了，最终结果 res 的维度也标错了。 比如像 b张量 shape = [2,3]， axis=0 的时候为 2，那怎么会像图中有 4,5,4 三个元素呢
docs/api/paddle/tensordot_cn.rst
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| 可以选择沿一个或多个轴进行点积操作,操作后返回的结果张量维度是 A 和 B 上未参与点积的维度的并集。 | ||
| 图例中展示了一个 shape = [2,2,2]的 a 张量,和 shape = [2,3]的 b 张量 | ||
| shape = [2,2,3]的 res 张量为 a,b 两个张量沿着 a 张量的最后一个轴和 b 张量的第一个轴进行缩并的结果 | ||
| res = paddle.tensordot(x, y, axes = 1) |
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| 可以选择沿一个或多个轴进行点积操作,操作后返回的结果张量维度是 A 和 B 上未参与点积的维度的并集。 | |
| 图例中展示了一个 shape = [2,2,2]的 a 张量,和 shape = [2,3]的 b 张量 | |
| shape = [2,2,3]的 res 张量为 a,b 两个张量沿着 a 张量的最后一个轴和 b 张量的第一个轴进行缩并的结果 | |
| res = paddle.tensordot(x, y, axes = 1) | |
| **图解说明** | |
| 可以选择沿一个或多个轴进行点积操作，操作后返回的结果张量维度是 A 和 B 上未参与点积的维度的并集。 | |
| 图例中展示了一个 shape = [2,2,2]的 ``a`` 张量，和 shape = [2,3]的 ``b`` 张量。 | |
| shape = [2,2,3]的 ``res`` 张量为 a,b 两个张量沿着 a 张量的最后一个轴和 b 张量的第一个轴进行缩并的结果（即参数 ``axes = 1`` 的情况） | |
操作后返回的结果张量维度是 A 和 B 上未参与点积的维度的并集这个 A 和 B是啥？- 注意下标点符号和格式
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为tensordot API添加图例并修改tensordot_cn.rst
中文文档链接：docs/api/paddle/tensordot_cn.rst
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@sunzhongkai588