大道至简，SQL也可以实现神经网络

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 (
                    {}))))))))))""";

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 = None
    final_query = None
    for i in range(iterations):
        if inner_table is None:
            inner_table = root_table_sql
        else:
            inner_table = final_query
        final_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 THEN 1
        ELSE 0 END)/COUNT(1))*100.0 AS accuracy_perc
FROM (
  SELECT
    *,
    (CASE
        WHEN scores_0 > scores_1 THEN 0
        ELSE 1 END) 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) > 0.0 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 ( (
          SELECT
            *
          FROM
            `dota.2009.example_table_for_sql2nn` )))))