大道至简，SQL也可以实现神经网络-优快云博客

本文链接：https://blog.youkuaiyun.com/m0_52122378/article/details/122682545

本文探讨了如何使用纯SQL实现简单的神经网络训练。作者通过在SQL中定义参数，利用列来表示权重矩阵和偏置向量，通过多层嵌套查询实现了神经网络的训练过程。

摘要生成于 C知道，由 DeepSeek-R1 满血版支持，前往体验 >

最近写SQL写多了，突发奇想SQL是不是也能实现简单的神经网络训练呢？于是带着这个问题在GitHub上找了找，还真有....那么本文就来分享一下如何用纯SQL实现一个神经网络吧！

题外话，可能有很多人会有疑问，你一个搞算法的，为啥在写SQL？这....就说来话长了... 总之，技多不压身嘛！

回归正题，我们再用SQL建模时，利用列来定义参数，从输入层到隐藏层，我们用 w1_00, w1_01, w1_10, w1_11表示权重矩阵W1，用b1_0, b1_1表示偏置向量B1。从隐藏层到输出层，我们用 w2_00, w2_01, w2_10, w2_11表示权重矩阵W2，用b2_0, b2_1表示偏置向量B2。这样我们通过一个多层嵌套的查询语句实现了整个训练过程。

add_iteration_sql = """
SELECT
x1,x2,y,
  w_00 - (2.0)*(dw_00+(1e-3)*w_00) AS w1_00,
  w_01 - (2.0)*(dw_01+(1e-3)*w_01) AS w1_01,
  w_10 - (2.0)*(dw_10+(1e-3)*w_10) AS w1_10,
  w_11 - (2.0)*(dw_11+(1e-3)*w_11) AS w1_11,
  b_0 - (2.0)*db_0 AS b_0,
  b_1 - (2.0)*db_1 AS b_1,
  w2_00 - (2.0)*(dw2_00+(1e-3)*w2_00) AS w2_00,
  w2_01 - (2.0)*(dw2_01+(1e-3)*w2_01) AS w2_01,
  w2_10 - (2.0)*(dw2_10+(1e-3)*w2_10) AS w2_10,
  w2_11 - (2.0)*(dw2_11+(1e-3)*w2_11) AS w2_11,
  b2_0 - (2.0)*db2_0 AS b2_0,
  b2_1 - (2.0)*db2_1 AS b2_1
FROM (
  SELECT
    *,
    SUM(x1*dhidden_0) OVER () AS dw_00,
    SUM(x1*dhidden_1) OVER () AS dw_01,
    SUM(x2*dhidden_0) OVER () AS dw_10,
    SUM(x2*dhidden_1) OVER () AS dw_11,
    SUM(dhidden_0) OVER () AS db_0,
    SUM(dhidden_1) OVER () AS db_1
  FROM (
    SELECT
      *,
      SUM(d0*dscores_0) OVER () AS dw2_00,
      SUM(d0*dscores_1) OVER () AS dw2_01,
      SUM(d1*dscores_0) OVER () AS dw2_10,
      SUM(d1*dscores_1) OVER () AS dw2_11,
      SUM(dscores_0) OVER () AS db2_0,