最近寫SQL寫多了，突發奇想SQL是不是也能實現簡單的神經網絡訓練呢？于是帶著這個問題在GitHub上找了找，還真有....那么本文就來分享一下如何用純SQL實現一個神經網絡吧！

題外話，可能有很多人會有疑問，你一個搞算法的，為啥在寫SQL？這....就說來話長了... 總之，技多不壓身嘛！

回歸正題，我們再用SQL建模時，利用列來定義參數，從輸入層到隱藏層，我們用 w1_00, w1_01, w1_10, w1_11表示權重矩陣W1，用b1_0, b1_1表示偏置向量B1。從隱藏層到輸出層，我們用 w2_00, w2_01, w2_10, w2_11表示權重矩陣W2，用b2_0, b2_1表示偏置向量B2。這樣我們通過一個多層嵌套的查詢語句實現了整個訓練過程。

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` )))))

總結

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