大道至简，SQL也可以实现神经网络

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,
      SUM(dscores_1) OVER () AS db2_1,
      CASE
        WHEN (d0) <= 0.0 THEN 0.0
        ELSE (dscores_0*w2_00 + dscores_1*w2_01)
      END AS dhidden_0,
      CASE
        WHEN (d1) <= 0.0 THEN 0.0
        ELSE (dscores_0*w2_10 + dscores_1*w2_11)
      END AS dhidden_1
    FROM (
      SELECT
        *,
        (CASE
            WHEN y = 0 THEN (probs_0 - 1)/num_examples
            ELSE probs_0/num_examples END) AS dscores_0,
        (CASE
            WHEN y = 1 THEN (probs_1 - 1)/num_examples
            ELSE probs_1/num_examples END) AS dscores_1
      FROM (
        SELECT
          *,
          (sum_correct_logprobs/num_examples) + 1e-3*(0.5*(w_00*w_00 + w_01*w_01 + w_10*w_10 + w_11*w_11) + 0.5*(w2_00*w2_00 + w2_01*w2_01 + w2_10*w2_10 + w2_11*w2_11)) AS loss
        FROM (
          SELECT
            *,
            SUM(correct_logprobs) OVER () sum_correct_logprobs,
            COUNT(1) OVER () num_examples
          FROM (
            SELECT
              *,
              (CASE
                  WHEN y = 0 THEN -1*LOG(probs_0)
                  ELSE -1*LOG(probs_1) END) AS correct_logprobs
            FROM (
              SELECT
                *,
                EXP(scores_0)/(EXP(scores_0) + EXP(scores_1)) AS probs_0,
                EXP(scores_1)/(EXP(scores_0) + EXP(scores_1)) AS probs_1
              FROM (
                SELECT
                  *,
                  ((d0*w2_00 + d1*w2_10) + b2_0) AS scores_0,
                  ((d0*w2_01 + d1*w2_11) + b2_1) AS scores_1
                FROM (
                  SELECT
                    *,
                    (CASE
                        WHEN ((x1*w_00 + x2*w_10) + b_0) > 0.0 THEN ((x1*w_00 + x2*w_10) + b_0)
                        ELSE 0.0 END) AS d0,
                    (CASE
                        WHEN ((x1*w_01 + x2*w_11) + b_0) > 0.0 THEN ((x1*w_01 + x2*w_11) + b_1)
                        ELSE 0.0 END) AS d1
                  FROM (
                    {}))))))))))""";

Generate Query

def generate_query(add_iteration_sql, root_table_sql, iterations):"""
    returns SQL query for deep neural network training
    param root_table_sql: SQL inner query producing a table with the training data and the initial values of the model parameters
    param add_iteration_sql: string format for adding one iteration of forward pass and backpropagation
    iterations: number of training iterations to be performed
    """inner_table = Nonefinal_query = Nonefor i in range(iterations):if inner_table is None:inner_table = root_table_sqlelse:inner_table = final_queryfinal_query = add_iteration_sql.format(inner_table)return final_query
print(generate_query(add_iteration_sql, root_table_sql, 10))

SELECT(SUM(CASE
WHEN y_hat = y THEN1
ELSE0END)/COUNT(1))*100.0AS accuracy_perc
FROM (
SELECT*,(CASE
WHEN scores_0 > scores_1 THEN0
ELSE1END) AS y_hat
FROM (
SELECT*,((d0*w2_00 + d1*w2_10) + b2_0) AS scores_0,((d0*w2_01 + d1*w2_11) + b2_1) AS scores_1
FROM (
SELECT*,(CASE
WHEN ((x1*w_00 + x2*w_10) + b_0) > 0.0THEN ((x1*w_00 + x2*w_10) + b_0)
ELSE0.0END) AS d0,(CASE
WHEN ((x1*w_01 + x2*w_11) + b_0) > 0.0THEN ((x1*w_01 + x2*w_11) + b_1)
ELSE0.0END) AS d1
FROM ( (
SELECT*
FROM
`dota.2009.example_table_for_sql2nn` )))))

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