text: -- This SQL code was generated by sklearn2sql (development version).
  -- Copyright 2018
  
  -- Model : KerasClassifier_LSTM
  -- Dataset : digits
  -- Database : firebird
  
  
  -- This SQL code can contain one or more statements, to be executed in the order they appear in this file.
  
  
  
  -- Model deployment code
  
  WITH RECURSIVE keras_input AS 
  (SELECT "ADS"."KEY" AS "KEY", "ADS"."Feature_0" AS "Feature_0", "ADS"."Feature_1" AS "Feature_1", "ADS"."Feature_2" AS "Feature_2", "ADS"."Feature_3" AS "Feature_3", "ADS"."Feature_4" AS "Feature_4", "ADS"."Feature_5" AS "Feature_5", "ADS"."Feature_6" AS "Feature_6", "ADS"."Feature_7" AS "Feature_7", "ADS"."Feature_8" AS "Feature_8", "ADS"."Feature_9" AS "Feature_9", "ADS"."Feature_10" AS "Feature_10", "ADS"."Feature_11" AS "Feature_11", "ADS"."Feature_12" AS "Feature_12", "ADS"."Feature_13" AS "Feature_13", "ADS"."Feature_14" AS "Feature_14", "ADS"."Feature_15" AS "Feature_15", "ADS"."Feature_16" AS "Feature_16", "ADS"."Feature_17" AS "Feature_17", "ADS"."Feature_18" AS "Feature_18", "ADS"."Feature_19" AS "Feature_19", "ADS"."Feature_20" AS "Feature_20", "ADS"."Feature_21" AS "Feature_21", "ADS"."Feature_22" AS "Feature_22", "ADS"."Feature_23" AS "Feature_23", "ADS"."Feature_24" AS "Feature_24", "ADS"."Feature_25" AS "Feature_25", "ADS"."Feature_26" AS "Feature_26", "ADS"."Feature_27" AS "Feature_27", "ADS"."Feature_28" AS "Feature_28", "ADS"."Feature_29" AS "Feature_29", "ADS"."Feature_30" AS "Feature_30", "ADS"."Feature_31" AS "Feature_31", "ADS"."Feature_32" AS "Feature_32", "ADS"."Feature_33" AS "Feature_33", "ADS"."Feature_34" AS "Feature_34", "ADS"."Feature_35" AS "Feature_35", "ADS"."Feature_36" AS "Feature_36", "ADS"."Feature_37" AS "Feature_37", "ADS"."Feature_38" AS "Feature_38", "ADS"."Feature_39" AS "Feature_39", "ADS"."Feature_40" AS "Feature_40", "ADS"."Feature_41" AS "Feature_41", "ADS"."Feature_42" AS "Feature_42", "ADS"."Feature_43" AS "Feature_43", "ADS"."Feature_44" AS "Feature_44", "ADS"."Feature_45" AS "Feature_45", "ADS"."Feature_46" AS "Feature_46", "ADS"."Feature_47" AS "Feature_47", "ADS"."Feature_48" AS "Feature_48", "ADS"."Feature_49" AS "Feature_49", "ADS"."Feature_50" AS "Feature_50", "ADS"."Feature_51" AS "Feature_51", "ADS"."Feature_52" AS "Feature_52", "ADS"."Feature_53" AS "Feature_53", "ADS"."Feature_54" AS "Feature_54", "ADS"."Feature_55" AS "Feature_55", "ADS"."Feature_56" AS "Feature_56", "ADS"."Feature_57" AS "Feature_57", "ADS"."Feature_58" AS "Feature_58", "ADS"."Feature_59" AS "Feature_59", "ADS"."Feature_60" AS "Feature_60", "ADS"."Feature_61" AS "Feature_61", "ADS"."Feature_62" AS "Feature_62", "ADS"."Feature_63" AS "Feature_63" 
  FROM digits AS "ADS"), 
  keras_input_1 AS 
  (SELECT keras_input."KEY" AS "KEY", keras_input."Feature_0" AS "Feature_0", keras_input."Feature_1" AS "Feature_1", keras_input."Feature_2" AS "Feature_2", keras_input."Feature_3" AS "Feature_3", keras_input."Feature_4" AS "Feature_4", keras_input."Feature_5" AS "Feature_5", keras_input."Feature_6" AS "Feature_6", keras_input."Feature_7" AS "Feature_7", keras_input."Feature_8" AS "Feature_8", keras_input."Feature_9" AS "Feature_9", keras_input."Feature_10" AS "Feature_10", keras_input."Feature_11" AS "Feature_11", keras_input."Feature_12" AS "Feature_12", keras_input."Feature_13" AS "Feature_13", keras_input."Feature_14" AS "Feature_14", keras_input."Feature_15" AS "Feature_15", keras_input."Feature_16" AS "Feature_16", keras_input."Feature_17" AS "Feature_17", keras_input."Feature_18" AS "Feature_18", keras_input."Feature_19" AS "Feature_19", keras_input."Feature_20" AS "Feature_20", keras_input."Feature_21" AS "Feature_21", keras_input."Feature_22" AS "Feature_22", keras_input."Feature_23" AS "Feature_23", keras_input."Feature_24" AS "Feature_24", keras_input."Feature_25" AS "Feature_25", keras_input."Feature_26" AS "Feature_26", keras_input."Feature_27" AS "Feature_27", keras_input."Feature_28" AS "Feature_28", keras_input."Feature_29" AS "Feature_29", keras_input."Feature_30" AS "Feature_30", keras_input."Feature_31" AS "Feature_31", keras_input."Feature_32" AS "Feature_32", keras_input."Feature_33" AS "Feature_33", keras_input."Feature_34" AS "Feature_34", keras_input."Feature_35" AS "Feature_35", keras_input."Feature_36" AS "Feature_36", keras_input."Feature_37" AS "Feature_37", keras_input."Feature_38" AS "Feature_38", keras_input."Feature_39" AS "Feature_39", keras_input."Feature_40" AS "Feature_40", keras_input."Feature_41" AS "Feature_41", keras_input."Feature_42" AS "Feature_42", keras_input."Feature_43" AS "Feature_43", keras_input."Feature_44" AS "Feature_44", keras_input."Feature_45" AS "Feature_45", keras_input."Feature_46" AS "Feature_46", keras_input."Feature_47" AS "Feature_47", keras_input."Feature_48" AS "Feature_48", keras_input."Feature_49" AS "Feature_49", keras_input."Feature_50" AS "Feature_50", keras_input."Feature_51" AS "Feature_51", keras_input."Feature_52" AS "Feature_52", keras_input."Feature_53" AS "Feature_53", keras_input."Feature_54" AS "Feature_54", keras_input."Feature_55" AS "Feature_55", keras_input."Feature_56" AS "Feature_56", keras_input."Feature_57" AS "Feature_57", keras_input."Feature_58" AS "Feature_58", keras_input."Feature_59" AS "Feature_59", keras_input."Feature_60" AS "Feature_60", keras_input."Feature_61" AS "Feature_61", keras_input."Feature_62" AS "Feature_62", keras_input."Feature_63" AS "Feature_63" 
  FROM keras_input), 
  keras_input_1_rn AS 
  (SELECT ROW_NUMBER() OVER (ORDER BY keras_input_1."KEY" ASC) AS rn, keras_input_1."KEY" AS "KEY", keras_input_1."Feature_0" AS "Feature_0", keras_input_1."Feature_1" AS "Feature_1", keras_input_1."Feature_2" AS "Feature_2", keras_input_1."Feature_3" AS "Feature_3", keras_input_1."Feature_4" AS "Feature_4", keras_input_1."Feature_5" AS "Feature_5", keras_input_1."Feature_6" AS "Feature_6", keras_input_1."Feature_7" AS "Feature_7", keras_input_1."Feature_8" AS "Feature_8", keras_input_1."Feature_9" AS "Feature_9", keras_input_1."Feature_10" AS "Feature_10", keras_input_1."Feature_11" AS "Feature_11", keras_input_1."Feature_12" AS "Feature_12", keras_input_1."Feature_13" AS "Feature_13", keras_input_1."Feature_14" AS "Feature_14", keras_input_1."Feature_15" AS "Feature_15", keras_input_1."Feature_16" AS "Feature_16", keras_input_1."Feature_17" AS "Feature_17", keras_input_1."Feature_18" AS "Feature_18", keras_input_1."Feature_19" AS "Feature_19", keras_input_1."Feature_20" AS "Feature_20", keras_input_1."Feature_21" AS "Feature_21", keras_input_1."Feature_22" AS "Feature_22", keras_input_1."Feature_23" AS "Feature_23", keras_input_1."Feature_24" AS "Feature_24", keras_input_1."Feature_25" AS "Feature_25", keras_input_1."Feature_26" AS "Feature_26", keras_input_1."Feature_27" AS "Feature_27", keras_input_1."Feature_28" AS "Feature_28", keras_input_1."Feature_29" AS "Feature_29", keras_input_1."Feature_30" AS "Feature_30", keras_input_1."Feature_31" AS "Feature_31", keras_input_1."Feature_32" AS "Feature_32", keras_input_1."Feature_33" AS "Feature_33", keras_input_1."Feature_34" AS "Feature_34", keras_input_1."Feature_35" AS "Feature_35", keras_input_1."Feature_36" AS "Feature_36", keras_input_1."Feature_37" AS "Feature_37", keras_input_1."Feature_38" AS "Feature_38", keras_input_1."Feature_39" AS "Feature_39", keras_input_1."Feature_40" AS "Feature_40", keras_input_1."Feature_41" AS "Feature_41", keras_input_1."Feature_42" AS "Feature_42", keras_input_1."Feature_43" AS "Feature_43", keras_input_1."Feature_44" AS "Feature_44", keras_input_1."Feature_45" AS "Feature_45", keras_input_1."Feature_46" AS "Feature_46", keras_input_1."Feature_47" AS "Feature_47", keras_input_1."Feature_48" AS "Feature_48", keras_input_1."Feature_49" AS "Feature_49", keras_input_1."Feature_50" AS "Feature_50", keras_input_1."Feature_51" AS "Feature_51", keras_input_1."Feature_52" AS "Feature_52", keras_input_1."Feature_53" AS "Feature_53", keras_input_1."Feature_54" AS "Feature_54", keras_input_1."Feature_55" AS "Feature_55", keras_input_1."Feature_56" AS "Feature_56", keras_input_1."Feature_57" AS "Feature_57", keras_input_1."Feature_58" AS "Feature_58", keras_input_1."Feature_59" AS "Feature_59", keras_input_1."Feature_60" AS "Feature_60", keras_input_1."Feature_61" AS "Feature_61", keras_input_1."Feature_62" AS "Feature_62", keras_input_1."Feature_63" AS "Feature_63" 
  FROM keras_input_1), 
  lstm_input_kernel_bias AS 
  (SELECT keras_input_1_rn.rn AS rn, keras_input_1_rn."KEY" AS "KEY", keras_input_1_rn."Feature_0" AS "Feature_0", keras_input_1_rn."Feature_1" AS "Feature_1", keras_input_1_rn."Feature_2" AS "Feature_2", keras_input_1_rn."Feature_3" AS "Feature_3", keras_input_1_rn."Feature_4" AS "Feature_4", keras_input_1_rn."Feature_5" AS "Feature_5", keras_input_1_rn."Feature_6" AS "Feature_6", keras_input_1_rn."Feature_7" AS "Feature_7", keras_input_1_rn."Feature_8" AS "Feature_8", keras_input_1_rn."Feature_9" AS "Feature_9", keras_input_1_rn."Feature_10" AS "Feature_10", keras_input_1_rn."Feature_11" AS "Feature_11", keras_input_1_rn."Feature_12" AS "Feature_12", keras_input_1_rn."Feature_13" AS "Feature_13", keras_input_1_rn."Feature_14" AS "Feature_14", keras_input_1_rn."Feature_15" AS "Feature_15", keras_input_1_rn."Feature_16" AS "Feature_16", keras_input_1_rn."Feature_17" AS "Feature_17", keras_input_1_rn."Feature_18" AS "Feature_18", keras_input_1_rn."Feature_19" AS "Feature_19", keras_input_1_rn."Feature_20" AS "Feature_20", keras_input_1_rn."Feature_21" AS "Feature_21", keras_input_1_rn."Feature_22" AS "Feature_22", keras_input_1_rn."Feature_23" AS "Feature_23", keras_input_1_rn."Feature_24" AS "Feature_24", keras_input_1_rn."Feature_25" AS "Feature_25", keras_input_1_rn."Feature_26" AS "Feature_26", keras_input_1_rn."Feature_27" AS "Feature_27", keras_input_1_rn."Feature_28" AS "Feature_28", keras_input_1_rn."Feature_29" AS "Feature_29", keras_input_1_rn."Feature_30" AS "Feature_30", keras_input_1_rn."Feature_31" AS "Feature_31", keras_input_1_rn."Feature_32" AS "Feature_32", keras_input_1_rn."Feature_33" AS "Feature_33", keras_input_1_rn."Feature_34" AS "Feature_34", keras_input_1_rn."Feature_35" AS "Feature_35", keras_input_1_rn."Feature_36" AS "Feature_36", keras_input_1_rn."Feature_37" AS "Feature_37", keras_input_1_rn."Feature_38" AS "Feature_38", keras_input_1_rn."Feature_39" AS "Feature_39", keras_input_1_rn."Feature_40" AS "Feature_40", keras_input_1_rn."Feature_41" AS "Feature_41", keras_input_1_rn."Feature_42" AS "Feature_42", keras_input_1_rn."Feature_43" AS "Feature_43", keras_input_1_rn."Feature_44" AS "Feature_44", keras_input_1_rn."Feature_45" AS "Feature_45", keras_input_1_rn."Feature_46" AS "Feature_46", keras_input_1_rn."Feature_47" AS "Feature_47", keras_input_1_rn."Feature_48" AS "Feature_48", keras_input_1_rn."Feature_49" AS "Feature_49", keras_input_1_rn."Feature_50" AS "Feature_50", keras_input_1_rn."Feature_51" AS "Feature_51", keras_input_1_rn."Feature_52" AS "Feature_52", keras_input_1_rn."Feature_53" AS "Feature_53", keras_input_1_rn."Feature_54" AS "Feature_54", keras_input_1_rn."Feature_55" AS "Feature_55", keras_input_1_rn."Feature_56" AS "Feature_56", keras_input_1_rn."Feature_57" AS "Feature_57", keras_input_1_rn."Feature_58" AS "Feature_58", keras_input_1_rn."Feature_59" AS "Feature_59", keras_input_1_rn."Feature_60" AS "Feature_60", keras_input_1_rn."Feature_61" AS "Feature_61", keras_input_1_rn."Feature_62" AS "Feature_62", keras_input_1_rn."Feature_63" AS "Feature_63", -0.014513546131630592 + -0.036601840652618484 * keras_input_1_rn."Feature_0" + 0.011517927290297948 * keras_input_1_rn."Feature_1" + -0.039459637853037965 * keras_input_1_rn."Feature_2" + -0.020406682142337096 * keras_input_1_rn."Feature_3" + 0.25990273766018424 * keras_input_1_rn."Feature_4" + -0.220896408979118 * keras_input_1_rn."Feature_5" + -0.14067857381191326 * keras_input_1_rn."Feature_6" + 0.22227375650406744 * keras_input_1_rn."Feature_7" + 0.07700761137253392 * keras_input_1_rn."Feature_8" + -0.014607435539321428 * keras_input_1_rn."Feature_9" + 0.008807734997587226 * keras_input_1_rn."Feature_10" + 0.06440974049378029 * keras_input_1_rn."Feature_11" + 0.07923750321067655 * keras_input_1_rn."Feature_12" + 0.04784725770791951 * keras_input_1_rn."Feature_13" + -0.2511986105685018 * keras_input_1_rn."Feature_14" + -0.03646962027017997 * keras_input_1_rn."Feature_15" + 0.17983071003227985 * keras_input_1_rn."Feature_16" + 0.2411491637205537 * keras_input_1_rn."Feature_17" + -0.22126552429881047 * keras_input_1_rn."Feature_18" + -0.14291347537526097 * keras_input_1_rn."Feature_19" + -0.07667066824773089 * keras_input_1_rn."Feature_20" + -0.11444629531376084 * keras_input_1_rn."Feature_21" + -0.2721131530783455 * keras_input_1_rn."Feature_22" + 0.21858999635442422 * keras_input_1_rn."Feature_23" + 0.2065181469749776 * keras_input_1_rn."Feature_24" + 0.1693703162021717 * keras_input_1_rn."Feature_25" + -0.049963313398188686 * keras_input_1_rn."Feature_26" + -0.22110064980378943 * keras_input_1_rn."Feature_27" + 0.16918857142664295 * keras_input_1_rn."Feature_28" + -0.03909954877826029 * keras_input_1_rn."Feature_29" + -0.24314174806553876 * keras_input_1_rn."Feature_30" + 0.062215380381549834 * keras_input_1_rn."Feature_31" + -0.11208158564154694 * keras_input_1_rn."Feature_32" + 0.19275995556052555 * keras_input_1_rn."Feature_33" + 0.22426640608262702 * keras_input_1_rn."Feature_34" + 0.19620586598688963 * keras_input_1_rn."Feature_35" + -0.06823753152191131 * keras_input_1_rn."Feature_36" + -0.2119102488292307 * keras_input_1_rn."Feature_37" + 0.05928153165735667 * keras_input_1_rn."Feature_38" + 0.25079985380599523 * keras_input_1_rn."Feature_39" + -0.21961466332877896 * keras_input_1_rn."Feature_40" + 0.07188870430678478 * keras_input_1_rn."Feature_41" + -0.13077792244348307 * keras_input_1_rn."Feature_42" + -0.06077055330018808 * keras_input_1_rn."Feature_43" + -0.03269592967083147 * keras_input_1_rn."Feature_44" + -0.2059377085955574 * keras_input_1_rn."Feature_45" + 0.2498427134786635 * keras_input_1_rn."Feature_46" + 0.08904110874296933 * keras_input_1_rn."Feature_47" + -0.1943517737939037 * keras_input_1_rn."Feature_48" + -0.054404502068174106 * keras_input_1_rn."Feature_49" + 0.08578046012325341 * keras_input_1_rn."Feature_50" + 0.010335604331623765 * keras_input_1_rn."Feature_51" + -0.03642884994629138 * keras_input_1_rn."Feature_52" + -0.213595637979412 * keras_input_1_rn."Feature_53" + 0.055425780337399526 * keras_input_1_rn."Feature_54" + -0.27829806349884 * keras_input_1_rn."Feature_55" + -0.03196454852427902 * keras_input_1_rn."Feature_56" + 0.2193703232543981 * keras_input_1_rn."Feature_57" + -0.19460434404780377 * keras_input_1_rn."Feature_58" + 0.06020714388639455 * keras_input_1_rn."Feature_59" + -0.16296446226442332 * keras_input_1_rn."Feature_60" + -0.10976788991353313 * keras_input_1_rn."Feature_61" + 0.26935367318444203 * keras_input_1_rn."Feature_62" + 0.07563083643050472 * keras_input_1_rn."Feature_63" AS dot_prod_i_1, -0.018733688187573927 + 0.2054827858072384 * keras_input_1_rn."Feature_0" + 0.2055582318519602 * keras_input_1_rn."Feature_1" + 0.09330880478887545 * keras_input_1_rn."Feature_2" + -0.03578162379408593 * keras_input_1_rn."Feature_3" + 0.021737651908510198 * keras_input_1_rn."Feature_4" + 0.21330996094961355 * keras_input_1_rn."Feature_5" + 0.12257239655089884 * keras_input_1_rn."Feature_6" + -0.11365566493929818 * keras_input_1_rn."Feature_7" + 0.035816013714728145 * keras_input_1_rn."Feature_8" + 0.2257975982247139 * keras_input_1_rn."Feature_9" + -0.24310589328786453 * keras_input_1_rn."Feature_10" + 0.2370043588225157 * keras_input_1_rn."Feature_11" + -0.26217929123365136 * keras_input_1_rn."Feature_12" + 0.09320609832585261 * keras_input_1_rn."Feature_13" + 0.04802408803328458 * keras_input_1_rn."Feature_14" + 0.04041669911815077 * keras_input_1_rn."Feature_15" + 0.23219574686416278 * keras_input_1_rn."Feature_16" + -0.17598248375480538 * keras_input_1_rn."Feature_17" + -0.08098410378367811 * keras_input_1_rn."Feature_18" + 0.09686336735791651 * keras_input_1_rn."Feature_19" + -0.12559348937749995 * keras_input_1_rn."Feature_20" + -0.20130607371982423 * keras_input_1_rn."Feature_21" + -0.12569272022415418 * keras_input_1_rn."Feature_22" + -0.05111777348703472 * keras_input_1_rn."Feature_23" + -0.1581114646271321 * keras_input_1_rn."Feature_24" + 0.023676849547994476 * keras_input_1_rn."Feature_25" + 0.1792380209495286 * keras_input_1_rn."Feature_26" + 0.19502738496787828 * keras_input_1_rn."Feature_27" + 0.073721643985655 * keras_input_1_rn."Feature_28" + -0.20536188445539608 * keras_input_1_rn."Feature_29" + -0.056911762326729315 * keras_input_1_rn."Feature_30" + 0.12081153034619274 * keras_input_1_rn."Feature_31" + -0.14342894128343237 * keras_input_1_rn."Feature_32" + -0.08721301900644546 * keras_input_1_rn."Feature_33" + -0.2509172645729346 * keras_input_1_rn."Feature_34" + 0.009933032820197083 * keras_input_1_rn."Feature_35" + -0.028123778218887414 * keras_input_1_rn."Feature_36" + 0.072616742770809 * keras_input_1_rn."Feature_37" + -0.25005284711822134 * keras_input_1_rn."Feature_38" + 0.12143832793887505 * keras_input_1_rn."Feature_39" + 0.14466225086743675 * keras_input_1_rn."Feature_40" + 0.149546473219293 * keras_input_1_rn."Feature_41" + -0.08996873693307522 * 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keras_input_1_rn."Feature_42" + 0.05764930803837774 * keras_input_1_rn."Feature_43" + -0.04509610748918686 * keras_input_1_rn."Feature_44" + -0.07697821850520874 * keras_input_1_rn."Feature_45" + 0.18275051701714715 * keras_input_1_rn."Feature_46" + 0.18991619678229993 * keras_input_1_rn."Feature_47" + 0.10349251105123863 * keras_input_1_rn."Feature_48" + 0.046129932420150226 * keras_input_1_rn."Feature_49" + 0.150384596351039 * keras_input_1_rn."Feature_50" + 0.0076912704307283124 * keras_input_1_rn."Feature_51" + -0.11350241131534196 * keras_input_1_rn."Feature_52" + -0.20415725251038588 * keras_input_1_rn."Feature_53" + 0.020680996307804792 * keras_input_1_rn."Feature_54" + 0.13451821573673411 * keras_input_1_rn."Feature_55" + -0.040499748917876804 * keras_input_1_rn."Feature_56" + 0.09139957652147727 * keras_input_1_rn."Feature_57" + -0.11752542558691557 * keras_input_1_rn."Feature_58" + 0.053570182083291874 * keras_input_1_rn."Feature_59" + 0.15621814464674522 * keras_input_1_rn."Feature_60" + 0.000999979047171495 * keras_input_1_rn."Feature_61" + -0.1750000808947103 * keras_input_1_rn."Feature_62" + 0.19061804233888646 * keras_input_1_rn."Feature_63" AS dot_prod_o_2, -0.09795758682753292 + -0.1000153396080499 * keras_input_1_rn."Feature_0" + -0.11957898332382128 * keras_input_1_rn."Feature_1" + -0.04586640340085161 * keras_input_1_rn."Feature_2" + 0.18492040011351885 * keras_input_1_rn."Feature_3" + 0.04013627960475569 * keras_input_1_rn."Feature_4" + -0.3039619447039238 * keras_input_1_rn."Feature_5" + -0.2770772166810477 * keras_input_1_rn."Feature_6" + 0.11362651281158309 * keras_input_1_rn."Feature_7" + 0.16630112456352025 * keras_input_1_rn."Feature_8" + -0.31212411272850493 * keras_input_1_rn."Feature_9" + 0.15872324313264488 * keras_input_1_rn."Feature_10" + -0.03125418151714731 * keras_input_1_rn."Feature_11" + 0.17014756465696312 * keras_input_1_rn."Feature_12" + -0.06855486999834012 * keras_input_1_rn."Feature_13" + 0.15755356895164838 * keras_input_1_rn."Feature_14" + -0.08926465984755544 * keras_input_1_rn."Feature_15" + 0.19537683585142696 * keras_input_1_rn."Feature_16" + -0.3034899545981652 * keras_input_1_rn."Feature_17" + -0.27597599702269443 * keras_input_1_rn."Feature_18" + -0.08087337443973358 * keras_input_1_rn."Feature_19" + 0.04832869993821806 * keras_input_1_rn."Feature_20" + -0.3263085393171142 * keras_input_1_rn."Feature_21" + 0.037204457067665946 * keras_input_1_rn."Feature_22" + -0.12627994112567817 * keras_input_1_rn."Feature_23" + 0.1804811184159728 * keras_input_1_rn."Feature_24" + 0.054612178155256735 * keras_input_1_rn."Feature_25" + -0.08749623645182383 * keras_input_1_rn."Feature_26" + -0.01419097003980243 * keras_input_1_rn."Feature_27" + -0.2518277687344895 * keras_input_1_rn."Feature_28" + -0.1830970005574936 * keras_input_1_rn."Feature_29" + 0.13784519956208252 * keras_input_1_rn."Feature_30" + -0.0785485309942346 * keras_input_1_rn."Feature_31" + 0.09648770498451448 * keras_input_1_rn."Feature_32" + 0.18838864799484345 * keras_input_1_rn."Feature_33" + -0.15998758964193968 * keras_input_1_rn."Feature_34" + -0.265081547974718 * keras_input_1_rn."Feature_35" + -0.05290677107986602 * keras_input_1_rn."Feature_36" + -0.06220154949088009 * keras_input_1_rn."Feature_37" + 0.1671703884204278 * keras_input_1_rn."Feature_38" + 0.22776161012643859 * keras_input_1_rn."Feature_39" + 0.12854020113360268 * keras_input_1_rn."Feature_40" + 0.03095728591203299 * keras_input_1_rn."Feature_41" + -0.2909975944149185 * keras_input_1_rn."Feature_42" + -0.10217658454138619 * keras_input_1_rn."Feature_43" + -0.12676693547360907 * keras_input_1_rn."Feature_44" + 0.0708395040944966 * keras_input_1_rn."Feature_45" + 0.14490961485600107 * keras_input_1_rn."Feature_46" + -0.028194878437227656 * keras_input_1_rn."Feature_47" + -0.25052498965693315 * keras_input_1_rn."Feature_48" + 0.17666183973193514 * keras_input_1_rn."Feature_49" + 0.13221317507179414 * keras_input_1_rn."Feature_50" + 0.08000231480514673 * keras_input_1_rn."Feature_51" + 0.02305942215923736 * keras_input_1_rn."Feature_52" + -0.23629136393512193 * keras_input_1_rn."Feature_53" + 0.1638742982074632 * keras_input_1_rn."Feature_54" + 0.06095309785505218 * keras_input_1_rn."Feature_55" + 0.14503259394492735 * keras_input_1_rn."Feature_56" + -0.23430583387047013 * keras_input_1_rn."Feature_57" + 0.062093546732590765 * keras_input_1_rn."Feature_58" + -0.3225656763826477 * keras_input_1_rn."Feature_59" + 0.10260989068653777 * keras_input_1_rn."Feature_60" + 0.10152793004344199 * keras_input_1_rn."Feature_61" + -0.015164961604331412 * keras_input_1_rn."Feature_62" + 0.16885244890567 * keras_input_1_rn."Feature_63" AS dot_prod_o_3 
  FROM keras_input_1_rn), 
  rnn_cte_lstm_1(rn_1, "KEY", "PreviousState_1", "PreviousState_2", "PreviousState_3", "PreviousState_4", "PreviousState_5", "PreviousState_6", "State_1", "State_2", "State_3", "State_4", "State_5", "State_6") AS 
  (SELECT lstm_input_kernel_bias.rn AS rn_1, lstm_input_kernel_bias."KEY" AS "KEY", CAST(0.0 AS DOUBLE PRECISION) AS "PreviousState_1", CAST(0.0 AS DOUBLE PRECISION) AS "PreviousState_2", CAST(0.0 AS DOUBLE PRECISION) AS "PreviousState_3", CAST(0.0 AS DOUBLE PRECISION) AS "PreviousState_4", CAST(0.0 AS DOUBLE PRECISION) AS "PreviousState_5", CAST(0.0 AS DOUBLE PRECISION) AS "PreviousState_6", maxvalue(lstm_input_kernel_bias.dot_prod_o_1 + 0.0, 0) * tanh(maxvalue(lstm_input_kernel_bias.dot_prod_i_1 + 0.0, 0) * tanh(lstm_input_kernel_bias.dot_prod_c_1 + 0.0) + maxvalue(lstm_input_kernel_bias.dot_prod_f_1 + 0.0, 0) * 0.0) AS "State_1", maxvalue(lstm_input_kernel_bias.dot_prod_o_2 + 0.0, 0) * tanh(maxvalue(lstm_input_kernel_bias.dot_prod_i_2 + 0.0, 0) * tanh(lstm_input_kernel_bias.dot_prod_c_2 + 0.0) + maxvalue(lstm_input_kernel_bias.dot_prod_f_2 + 0.0, 0) * 0.0) AS "State_2", maxvalue(lstm_input_kernel_bias.dot_prod_o_3 + 0.0, 0) * tanh(maxvalue(lstm_input_kernel_bias.dot_prod_i_3 + 0.0, 0) * tanh(lstm_input_kernel_bias.dot_prod_c_3 + 0.0) + maxvalue(lstm_input_kernel_bias.dot_prod_f_3 + 0.0, 0) * 0.0) AS "State_3", maxvalue(lstm_input_kernel_bias.dot_prod_i_1 + 0.0, 0) * tanh(lstm_input_kernel_bias.dot_prod_c_1 + 0.0) + maxvalue(lstm_input_kernel_bias.dot_prod_f_1 + 0.0, 0) * 0.0 AS "State_4", maxvalue(lstm_input_kernel_bias.dot_prod_i_2 + 0.0, 0) * tanh(lstm_input_kernel_bias.dot_prod_c_2 + 0.0) + maxvalue(lstm_input_kernel_bias.dot_prod_f_2 + 0.0, 0) * 0.0 AS "State_5", maxvalue(lstm_input_kernel_bias.dot_prod_i_3 + 0.0, 0) * tanh(lstm_input_kernel_bias.dot_prod_c_3 + 0.0) + maxvalue(lstm_input_kernel_bias.dot_prod_f_3 + 0.0, 0) * 0.0 AS "State_6" 
  FROM lstm_input_kernel_bias 
  WHERE lstm_input_kernel_bias.rn = 1 UNION ALL SELECT lstm_input_kernel_bias.rn AS rn, lstm_input_kernel_bias."KEY" AS "KEY", rnn_cte_lstm_1."State_1" AS "PreviousState_1", rnn_cte_lstm_1."State_2" AS "PreviousState_2", rnn_cte_lstm_1."State_3" AS "PreviousState_3", rnn_cte_lstm_1."State_4" AS "PreviousState_4", rnn_cte_lstm_1."State_5" AS "PreviousState_5", rnn_cte_lstm_1."State_6" AS "PreviousState_6", maxvalue(lstm_input_kernel_bias.dot_prod_o_1 + -0.14824536238424982 * rnn_cte_lstm_1."State_1" + 0.08253020083536827 * rnn_cte_lstm_1."State_2" + -0.08763056336675054 * rnn_cte_lstm_1."State_3", 0) * tanh(maxvalue(lstm_input_kernel_bias.dot_prod_i_1 + 0.10396235546030086 * rnn_cte_lstm_1."State_1" + 0.2487056155077179 * rnn_cte_lstm_1."State_2" + -0.09242046264251788 * rnn_cte_lstm_1."State_3", 0) * tanh(lstm_input_kernel_bias.dot_prod_c_1 + 0.01614537063079912 * rnn_cte_lstm_1."State_1" + -0.023239700724073803 * rnn_cte_lstm_1."State_2" + 0.523162297866245 * rnn_cte_lstm_1."State_3") + maxvalue(lstm_input_kernel_bias.dot_prod_f_1 + -0.4168294149364666 * rnn_cte_lstm_1."State_1" + 0.1545536618879541 * rnn_cte_lstm_1."State_2" + -0.024703716341743575 * rnn_cte_lstm_1."State_3", 0) * rnn_cte_lstm_1."State_4") AS "State_1", maxvalue(lstm_input_kernel_bias.dot_prod_o_2 + 0.08662667743445306 * rnn_cte_lstm_1."State_1" + 0.6184497291063422 * rnn_cte_lstm_1."State_2" + -0.2143162799313478 * rnn_cte_lstm_1."State_3", 0) * tanh(maxvalue(lstm_input_kernel_bias.dot_prod_i_2 + -0.19559477723174404 * rnn_cte_lstm_1."State_1" + -0.34211541582473526 * rnn_cte_lstm_1."State_2" + -0.1183209058709506 * rnn_cte_lstm_1."State_3", 0) * tanh(lstm_input_kernel_bias.dot_prod_c_2 + 0.17448831523202973 * rnn_cte_lstm_1."State_1" + 0.3769112758218438 * rnn_cte_lstm_1."State_2" + 0.13484420873857633 * rnn_cte_lstm_1."State_3") + maxvalue(lstm_input_kernel_bias.dot_prod_f_2 + 0.33552079367368043 * rnn_cte_lstm_1."State_1" + -0.075264273565803 * rnn_cte_lstm_1."State_2" + 0.5888972000391576 * rnn_cte_lstm_1."State_3", 0) * rnn_cte_lstm_1."State_5") AS "State_2", maxvalue(lstm_input_kernel_bias.dot_prod_o_3 + 0.6248732477230643 * rnn_cte_lstm_1."State_1" + -0.34157207668489803 * rnn_cte_lstm_1."State_2" + -0.4324586295694866 * rnn_cte_lstm_1."State_3", 0) * tanh(maxvalue(lstm_input_kernel_bias.dot_prod_i_3 + -0.08246075095429538 * rnn_cte_lstm_1."State_1" + 0.2806416787871721 * rnn_cte_lstm_1."State_2" + -0.19913415568947318 * rnn_cte_lstm_1."State_3", 0) * tanh(lstm_input_kernel_bias.dot_prod_c_3 + -0.07157507510853936 * rnn_cte_lstm_1."State_1" + -0.166539959629415 * rnn_cte_lstm_1."State_2" + -0.1566599942447909 * rnn_cte_lstm_1."State_3") + maxvalue(lstm_input_kernel_bias.dot_prod_f_3 + 0.4441732904955501 * rnn_cte_lstm_1."State_1" + 0.22077978616056787 * rnn_cte_lstm_1."State_2" + 0.035138264535756164 * rnn_cte_lstm_1."State_3", 0) * rnn_cte_lstm_1."State_6") AS "State_3", maxvalue(lstm_input_kernel_bias.dot_prod_i_1 + 0.10396235546030086 * rnn_cte_lstm_1."State_1" + 0.2487056155077179 * rnn_cte_lstm_1."State_2" + -0.09242046264251788 * rnn_cte_lstm_1."State_3", 0) * tanh(lstm_input_kernel_bias.dot_prod_c_1 + 0.01614537063079912 * rnn_cte_lstm_1."State_1" + -0.023239700724073803 * rnn_cte_lstm_1."State_2" + 0.523162297866245 * rnn_cte_lstm_1."State_3") + maxvalue(lstm_input_kernel_bias.dot_prod_f_1 + -0.4168294149364666 * rnn_cte_lstm_1."State_1" + 0.1545536618879541 * rnn_cte_lstm_1."State_2" + -0.024703716341743575 * rnn_cte_lstm_1."State_3", 0) * rnn_cte_lstm_1."State_4" AS "State_4", maxvalue(lstm_input_kernel_bias.dot_prod_i_2 + -0.19559477723174404 * rnn_cte_lstm_1."State_1" + -0.34211541582473526 * rnn_cte_lstm_1."State_2" + -0.1183209058709506 * rnn_cte_lstm_1."State_3", 0) * tanh(lstm_input_kernel_bias.dot_prod_c_2 + 0.17448831523202973 * rnn_cte_lstm_1."State_1" + 0.3769112758218438 * rnn_cte_lstm_1."State_2" + 0.13484420873857633 * rnn_cte_lstm_1."State_3") + maxvalue(lstm_input_kernel_bias.dot_prod_f_2 + 0.33552079367368043 * rnn_cte_lstm_1."State_1" + -0.075264273565803 * rnn_cte_lstm_1."State_2" + 0.5888972000391576 * rnn_cte_lstm_1."State_3", 0) * rnn_cte_lstm_1."State_5" AS "State_5", maxvalue(lstm_input_kernel_bias.dot_prod_i_3 + -0.08246075095429538 * rnn_cte_lstm_1."State_1" + 0.2806416787871721 * rnn_cte_lstm_1."State_2" + -0.19913415568947318 * rnn_cte_lstm_1."State_3", 0) * tanh(lstm_input_kernel_bias.dot_prod_c_3 + -0.07157507510853936 * rnn_cte_lstm_1."State_1" + -0.166539959629415 * rnn_cte_lstm_1."State_2" + -0.1566599942447909 * rnn_cte_lstm_1."State_3") + maxvalue(lstm_input_kernel_bias.dot_prod_f_3 + 0.4441732904955501 * rnn_cte_lstm_1."State_1" + 0.22077978616056787 * rnn_cte_lstm_1."State_2" + 0.035138264535756164 * rnn_cte_lstm_1."State_3", 0) * rnn_cte_lstm_1."State_6" AS "State_6" 
  FROM lstm_input_kernel_bias, rnn_cte_lstm_1 
  WHERE lstm_input_kernel_bias.rn = rnn_cte_lstm_1.rn_1 + 1), 
  lstm_1 AS 
  (SELECT rnn_cte_lstm_1."KEY" AS "KEY", CAST(rnn_cte_lstm_1."State_1" AS DOUBLE PRECISION) AS output_1, CAST(rnn_cte_lstm_1."State_2" AS DOUBLE PRECISION) AS output_2, CAST(rnn_cte_lstm_1."State_3" AS DOUBLE PRECISION) AS output_3 
  FROM rnn_cte_lstm_1), 
  lstm_1_1 AS 
  (SELECT lstm_1."KEY" AS "KEY", lstm_1.output_1 AS output_1, lstm_1.output_2 AS output_2, lstm_1.output_3 AS output_3 
  FROM lstm_1), 
  layer_dense_1 AS 
  (SELECT lstm_1_1."KEY" AS "KEY", -0.059914774743699196 + -0.31999679596224806 * lstm_1_1.output_1 + -0.7172651709728686 * lstm_1_1.output_2 + 0.49549777202966905 * lstm_1_1.output_3 AS output_1, 0.004135252436141509 + 0.6408428836846676 * lstm_1_1.output_1 + -0.4003596437198973 * lstm_1_1.output_2 + 0.134946121774154 * lstm_1_1.output_3 AS output_2, 0.014243481758067049 + -0.30579435747461514 * lstm_1_1.output_1 + 0.3676802806726856 * lstm_1_1.output_2 + -0.18762435082822193 * lstm_1_1.output_3 AS output_3, -0.012181723285804835 + -0.1330582270787425 * lstm_1_1.output_1 + -0.19683684324950462 * lstm_1_1.output_2 + 0.16040194130282068 * lstm_1_1.output_3 AS output_4, 0.025502928763087296 + -0.30547204243913345 * lstm_1_1.output_1 + -0.2817719075797715 * lstm_1_1.output_2 + 0.25017051422564296 * lstm_1_1.output_3 AS output_5, -0.014756452969997095 + 0.19959448785453204 * lstm_1_1.output_1 + -0.32367777573975587 * lstm_1_1.output_2 + -0.583105990626854 * lstm_1_1.output_3 AS output_6, -0.02381064377306744 + 0.36244202293207134 * lstm_1_1.output_1 + 0.6135640158927792 * lstm_1_1.output_2 + 0.3928725856279571 * lstm_1_1.output_3 AS output_7, -0.048669105500714814 + -0.1832591063179966 * lstm_1_1.output_1 + 0.4485901097797643 * lstm_1_1.output_2 + -0.3759271416949052 * lstm_1_1.output_3 AS output_8, -0.0858893283988944 + 0.6037231251594958 * lstm_1_1.output_1 + 0.35024922354400856 * lstm_1_1.output_2 + 0.44499267752521593 * lstm_1_1.output_3 AS output_9, 0.004402719295877966 + 0.43485053415136116 * lstm_1_1.output_1 + -0.025872842273815987 * lstm_1_1.output_2 + 0.5365692043216336 * lstm_1_1.output_3 AS output_10 
  FROM lstm_1_1), 
  layer_dense_1_1 AS 
  (SELECT layer_dense_1."KEY" AS "KEY", layer_dense_1.output_1 AS output_1, layer_dense_1.output_2 AS output_2, layer_dense_1.output_3 AS output_3, layer_dense_1.output_4 AS output_4, layer_dense_1.output_5 AS output_5, layer_dense_1.output_6 AS output_6, layer_dense_1.output_7 AS output_7, layer_dense_1.output_8 AS output_8, layer_dense_1.output_9 AS output_9, layer_dense_1.output_10 AS output_10 
  FROM layer_dense_1), 
  score_soft_max_step1 AS 
  (SELECT layer_dense_1_1."KEY" AS "KEY", layer_dense_1_1.output_1 AS "Score_0", exp(CASE WHEN (minvalue(100.0, maxvalue(-100.0, layer_dense_1_1.output_1)) >= -709.782712893384) THEN minvalue(100.0, maxvalue(-100.0, layer_dense_1_1.output_1)) ELSE -709.782712893384 END) AS "exp_Score_0", layer_dense_1_1.output_2 AS "Score_1", exp(CASE WHEN (minvalue(100.0, maxvalue(-100.0, layer_dense_1_1.output_2)) >= -709.782712893384) THEN minvalue(100.0, maxvalue(-100.0, layer_dense_1_1.output_2)) ELSE -709.782712893384 END) AS "exp_Score_1", layer_dense_1_1.output_3 AS "Score_2", exp(CASE WHEN (minvalue(100.0, maxvalue(-100.0, layer_dense_1_1.output_3)) >= -709.782712893384) THEN minvalue(100.0, maxvalue(-100.0, layer_dense_1_1.output_3)) ELSE -709.782712893384 END) AS "exp_Score_2", layer_dense_1_1.output_4 AS "Score_3", exp(CASE WHEN (minvalue(100.0, maxvalue(-100.0, layer_dense_1_1.output_4)) >= -709.782712893384) THEN minvalue(100.0, maxvalue(-100.0, layer_dense_1_1.output_4)) ELSE -709.782712893384 END) AS "exp_Score_3", layer_dense_1_1.output_5 AS "Score_4", exp(CASE WHEN (minvalue(100.0, maxvalue(-100.0, layer_dense_1_1.output_5)) >= -709.782712893384) THEN minvalue(100.0, maxvalue(-100.0, layer_dense_1_1.output_5)) ELSE -709.782712893384 END) AS "exp_Score_4", layer_dense_1_1.output_6 AS "Score_5", exp(CASE WHEN (minvalue(100.0, maxvalue(-100.0, layer_dense_1_1.output_6)) >= -709.782712893384) THEN minvalue(100.0, maxvalue(-100.0, layer_dense_1_1.output_6)) ELSE -709.782712893384 END) AS "exp_Score_5", layer_dense_1_1.output_7 AS "Score_6", exp(CASE WHEN (minvalue(100.0, maxvalue(-100.0, layer_dense_1_1.output_7)) >= -709.782712893384) THEN minvalue(100.0, maxvalue(-100.0, layer_dense_1_1.output_7)) ELSE -709.782712893384 END) AS "exp_Score_6", layer_dense_1_1.output_8 AS "Score_7", exp(CASE WHEN (minvalue(100.0, maxvalue(-100.0, layer_dense_1_1.output_8)) >= -709.782712893384) THEN minvalue(100.0, maxvalue(-100.0, layer_dense_1_1.output_8)) ELSE -709.782712893384 END) AS "exp_Score_7", layer_dense_1_1.output_9 AS "Score_8", exp(CASE WHEN (minvalue(100.0, maxvalue(-100.0, layer_dense_1_1.output_9)) >= -709.782712893384) THEN minvalue(100.0, maxvalue(-100.0, layer_dense_1_1.output_9)) ELSE -709.782712893384 END) AS "exp_Score_8", layer_dense_1_1.output_10 AS "Score_9", exp(CASE WHEN (minvalue(100.0, maxvalue(-100.0, layer_dense_1_1.output_10)) >= -709.782712893384) THEN minvalue(100.0, maxvalue(-100.0, layer_dense_1_1.output_10)) ELSE -709.782712893384 END) AS "exp_Score_9" 
  FROM layer_dense_1_1), 
  score_class_union_soft AS 
  (SELECT soft_scu."KEY" AS "KEY", soft_scu.class AS class, soft_scu."exp_Score" AS "exp_Score" 
  FROM (SELECT score_soft_max_step1."KEY" AS "KEY", 0 AS class, score_soft_max_step1."exp_Score_0" AS "exp_Score" 
  FROM score_soft_max_step1 UNION ALL SELECT score_soft_max_step1."KEY" AS "KEY", 1 AS class, score_soft_max_step1."exp_Score_1" AS "exp_Score" 
  FROM score_soft_max_step1 UNION ALL SELECT score_soft_max_step1."KEY" AS "KEY", 2 AS class, score_soft_max_step1."exp_Score_2" AS "exp_Score" 
  FROM score_soft_max_step1 UNION ALL SELECT score_soft_max_step1."KEY" AS "KEY", 3 AS class, score_soft_max_step1."exp_Score_3" AS "exp_Score" 
  FROM score_soft_max_step1 UNION ALL SELECT score_soft_max_step1."KEY" AS "KEY", 4 AS class, score_soft_max_step1."exp_Score_4" AS "exp_Score" 
  FROM score_soft_max_step1 UNION ALL SELECT score_soft_max_step1."KEY" AS "KEY", 5 AS class, score_soft_max_step1."exp_Score_5" AS "exp_Score" 
  FROM score_soft_max_step1 UNION ALL SELECT score_soft_max_step1."KEY" AS "KEY", 6 AS class, score_soft_max_step1."exp_Score_6" AS "exp_Score" 
  FROM score_soft_max_step1 UNION ALL SELECT score_soft_max_step1."KEY" AS "KEY", 7 AS class, score_soft_max_step1."exp_Score_7" AS "exp_Score" 
  FROM score_soft_max_step1 UNION ALL SELECT score_soft_max_step1."KEY" AS "KEY", 8 AS class, score_soft_max_step1."exp_Score_8" AS "exp_Score" 
  FROM score_soft_max_step1 UNION ALL SELECT score_soft_max_step1."KEY" AS "KEY", 9 AS class, score_soft_max_step1."exp_Score_9" AS "exp_Score" 
  FROM score_soft_max_step1) AS soft_scu), 
  score_soft_max AS 
  (SELECT score_soft_max_step1."KEY" AS "KEY", score_soft_max_step1."Score_0" AS "Score_0", score_soft_max_step1."exp_Score_0" AS "exp_Score_0", score_soft_max_step1."Score_1" AS "Score_1", score_soft_max_step1."exp_Score_1" AS "exp_Score_1", score_soft_max_step1."Score_2" AS "Score_2", score_soft_max_step1."exp_Score_2" AS "exp_Score_2", score_soft_max_step1."Score_3" AS "Score_3", score_soft_max_step1."exp_Score_3" AS "exp_Score_3", score_soft_max_step1."Score_4" AS "Score_4", score_soft_max_step1."exp_Score_4" AS "exp_Score_4", score_soft_max_step1."Score_5" AS "Score_5", score_soft_max_step1."exp_Score_5" AS "exp_Score_5", score_soft_max_step1."Score_6" AS "Score_6", score_soft_max_step1."exp_Score_6" AS "exp_Score_6", score_soft_max_step1."Score_7" AS "Score_7", score_soft_max_step1."exp_Score_7" AS "exp_Score_7", score_soft_max_step1."Score_8" AS "Score_8", score_soft_max_step1."exp_Score_8" AS "exp_Score_8", score_soft_max_step1."Score_9" AS "Score_9", score_soft_max_step1."exp_Score_9" AS "exp_Score_9", sum_exp_t."KEY_sum" AS "KEY_sum", sum_exp_t."sum_ExpScore" AS "sum_ExpScore" 
  FROM score_soft_max_step1 LEFT OUTER JOIN (SELECT score_class_union_soft."KEY" AS "KEY_sum", sum(score_class_union_soft."exp_Score") AS "sum_ExpScore" 
  FROM score_class_union_soft GROUP BY score_class_union_soft."KEY") AS sum_exp_t ON score_soft_max_step1."KEY" = sum_exp_t."KEY_sum"), 
  layer_softmax AS 
  (SELECT score_soft_max."KEY" AS "KEY", score_soft_max."exp_Score_0" / score_soft_max."sum_ExpScore" AS output_1, score_soft_max."exp_Score_1" / score_soft_max."sum_ExpScore" AS output_2, score_soft_max."exp_Score_2" / score_soft_max."sum_ExpScore" AS output_3, score_soft_max."exp_Score_3" / score_soft_max."sum_ExpScore" AS output_4, score_soft_max."exp_Score_4" / score_soft_max."sum_ExpScore" AS output_5, score_soft_max."exp_Score_5" / score_soft_max."sum_ExpScore" AS output_6, score_soft_max."exp_Score_6" / score_soft_max."sum_ExpScore" AS output_7, score_soft_max."exp_Score_7" / score_soft_max."sum_ExpScore" AS output_8, score_soft_max."exp_Score_8" / score_soft_max."sum_ExpScore" AS output_9, score_soft_max."exp_Score_9" / score_soft_max."sum_ExpScore" AS output_10 
  FROM score_soft_max), 
  orig_cte AS 
  (SELECT layer_softmax."KEY" AS "KEY", CAST(NULL AS DOUBLE PRECISION) AS "Score_0", CAST(NULL AS DOUBLE PRECISION) AS "Score_1", CAST(NULL AS DOUBLE PRECISION) AS "Score_2", CAST(NULL AS DOUBLE PRECISION) AS "Score_3", CAST(NULL AS DOUBLE PRECISION) AS "Score_4", CAST(NULL AS DOUBLE PRECISION) AS "Score_5", CAST(NULL AS DOUBLE PRECISION) AS "Score_6", CAST(NULL AS DOUBLE PRECISION) AS "Score_7", CAST(NULL AS DOUBLE PRECISION) AS "Score_8", CAST(NULL AS DOUBLE PRECISION) AS "Score_9", layer_softmax.output_1 AS "Proba_0", layer_softmax.output_2 AS "Proba_1", layer_softmax.output_3 AS "Proba_2", layer_softmax.output_4 AS "Proba_3", layer_softmax.output_5 AS "Proba_4", layer_softmax.output_6 AS "Proba_5", layer_softmax.output_7 AS "Proba_6", layer_softmax.output_8 AS "Proba_7", layer_softmax.output_9 AS "Proba_8", layer_softmax.output_10 AS "Proba_9", CAST(NULL AS DOUBLE PRECISION) AS "LogProba_0", CAST(NULL AS DOUBLE PRECISION) AS "LogProba_1", CAST(NULL AS DOUBLE PRECISION) AS "LogProba_2", CAST(NULL AS DOUBLE PRECISION) AS "LogProba_3", CAST(NULL AS DOUBLE PRECISION) AS "LogProba_4", CAST(NULL AS DOUBLE PRECISION) AS "LogProba_5", CAST(NULL AS DOUBLE PRECISION) AS "LogProba_6", CAST(NULL AS DOUBLE PRECISION) AS "LogProba_7", CAST(NULL AS DOUBLE PRECISION) AS "LogProba_8", CAST(NULL AS DOUBLE PRECISION) AS "LogProba_9", CAST(NULL AS BIGINT) AS "Decision", CAST(NULL AS DOUBLE PRECISION) AS "DecisionProba" 
  FROM layer_softmax), 
  score_class_union AS 
  (SELECT scu."KEY_u" AS "KEY_u", scu.class AS class, scu."LogProba" AS "LogProba", scu."Proba" AS "Proba", scu."Score" AS "Score" 
  FROM (SELECT orig_cte."KEY" AS "KEY_u", 0 AS class, orig_cte."LogProba_0" AS "LogProba", orig_cte."Proba_0" AS "Proba", orig_cte."Score_0" AS "Score" 
  FROM orig_cte UNION ALL SELECT orig_cte."KEY" AS "KEY_u", 1 AS class, orig_cte."LogProba_1" AS "LogProba", orig_cte."Proba_1" AS "Proba", orig_cte."Score_1" AS "Score" 
  FROM orig_cte UNION ALL SELECT orig_cte."KEY" AS "KEY_u", 2 AS class, orig_cte."LogProba_2" AS "LogProba", orig_cte."Proba_2" AS "Proba", orig_cte."Score_2" AS "Score" 
  FROM orig_cte UNION ALL SELECT orig_cte."KEY" AS "KEY_u", 3 AS class, orig_cte."LogProba_3" AS "LogProba", orig_cte."Proba_3" AS "Proba", orig_cte."Score_3" AS "Score" 
  FROM orig_cte UNION ALL SELECT orig_cte."KEY" AS "KEY_u", 4 AS class, orig_cte."LogProba_4" AS "LogProba", orig_cte."Proba_4" AS "Proba", orig_cte."Score_4" AS "Score" 
  FROM orig_cte UNION ALL SELECT orig_cte."KEY" AS "KEY_u", 5 AS class, orig_cte."LogProba_5" AS "LogProba", orig_cte."Proba_5" AS "Proba", orig_cte."Score_5" AS "Score" 
  FROM orig_cte UNION ALL SELECT orig_cte."KEY" AS "KEY_u", 6 AS class, orig_cte."LogProba_6" AS "LogProba", orig_cte."Proba_6" AS "Proba", orig_cte."Score_6" AS "Score" 
  FROM orig_cte UNION ALL SELECT orig_cte."KEY" AS "KEY_u", 7 AS class, orig_cte."LogProba_7" AS "LogProba", orig_cte."Proba_7" AS "Proba", orig_cte."Score_7" AS "Score" 
  FROM orig_cte UNION ALL SELECT orig_cte."KEY" AS "KEY_u", 8 AS class, orig_cte."LogProba_8" AS "LogProba", orig_cte."Proba_8" AS "Proba", orig_cte."Score_8" AS "Score" 
  FROM orig_cte UNION ALL SELECT orig_cte."KEY" AS "KEY_u", 9 AS class, orig_cte."LogProba_9" AS "LogProba", orig_cte."Proba_9" AS "Proba", orig_cte."Score_9" AS "Score" 
  FROM orig_cte) AS scu), 
  score_max AS 
  (SELECT orig_cte."KEY" AS "KEY", orig_cte."Score_0" AS "Score_0", orig_cte."Score_1" AS "Score_1", orig_cte."Score_2" AS "Score_2", orig_cte."Score_3" AS "Score_3", orig_cte."Score_4" AS "Score_4", orig_cte."Score_5" AS "Score_5", orig_cte."Score_6" AS "Score_6", orig_cte."Score_7" AS "Score_7", orig_cte."Score_8" AS "Score_8", orig_cte."Score_9" AS "Score_9", orig_cte."Proba_0" AS "Proba_0", orig_cte."Proba_1" AS "Proba_1", orig_cte."Proba_2" AS "Proba_2", orig_cte."Proba_3" AS "Proba_3", orig_cte."Proba_4" AS "Proba_4", orig_cte."Proba_5" AS "Proba_5", orig_cte."Proba_6" AS "Proba_6", orig_cte."Proba_7" AS "Proba_7", orig_cte."Proba_8" AS "Proba_8", orig_cte."Proba_9" AS "Proba_9", orig_cte."LogProba_0" AS "LogProba_0", orig_cte."LogProba_1" AS "LogProba_1", orig_cte."LogProba_2" AS "LogProba_2", orig_cte."LogProba_3" AS "LogProba_3", orig_cte."LogProba_4" AS "LogProba_4", orig_cte."LogProba_5" AS "LogProba_5", orig_cte."LogProba_6" AS "LogProba_6", orig_cte."LogProba_7" AS "LogProba_7", orig_cte."LogProba_8" AS "LogProba_8", orig_cte."LogProba_9" AS "LogProba_9", orig_cte."Decision" AS "Decision", orig_cte."DecisionProba" AS "DecisionProba", max_select."KEY_m" AS "KEY_m", max_select."max_Proba" AS "max_Proba" 
  FROM orig_cte LEFT OUTER JOIN (SELECT score_class_union."KEY_u" AS "KEY_m", max(score_class_union."Proba") AS "max_Proba" 
  FROM score_class_union GROUP BY score_class_union."KEY_u") AS max_select ON orig_cte."KEY" = max_select."KEY_m"), 
  union_with_max AS 
  (SELECT score_class_union."KEY_u" AS "KEY_u", score_class_union.class AS class, score_class_union."LogProba" AS "LogProba", score_class_union."Proba" AS "Proba", score_class_union."Score" AS "Score", score_max."KEY" AS "KEY", score_max."Score_0" AS "Score_0", score_max."Score_1" AS "Score_1", score_max."Score_2" AS "Score_2", score_max."Score_3" AS "Score_3", score_max."Score_4" AS "Score_4", score_max."Score_5" AS "Score_5", score_max."Score_6" AS "Score_6", score_max."Score_7" AS "Score_7", score_max."Score_8" AS "Score_8", score_max."Score_9" AS "Score_9", score_max."Proba_0" AS "Proba_0", score_max."Proba_1" AS "Proba_1", score_max."Proba_2" AS "Proba_2", score_max."Proba_3" AS "Proba_3", score_max."Proba_4" AS "Proba_4", score_max."Proba_5" AS "Proba_5", score_max."Proba_6" AS "Proba_6", score_max."Proba_7" AS "Proba_7", score_max."Proba_8" AS "Proba_8", score_max."Proba_9" AS "Proba_9", score_max."LogProba_0" AS "LogProba_0", score_max."LogProba_1" AS "LogProba_1", score_max."LogProba_2" AS "LogProba_2", score_max."LogProba_3" AS "LogProba_3", score_max."LogProba_4" AS "LogProba_4", score_max."LogProba_5" AS "LogProba_5", score_max."LogProba_6" AS "LogProba_6", score_max."LogProba_7" AS "LogProba_7", score_max."LogProba_8" AS "LogProba_8", score_max."LogProba_9" AS "LogProba_9", score_max."Decision" AS "Decision", score_max."DecisionProba" AS "DecisionProba", score_max."KEY_m" AS "KEY_m", score_max."max_Proba" AS "max_Proba" 
  FROM score_class_union LEFT OUTER JOIN score_max ON score_class_union."KEY_u" = score_max."KEY"), 
  arg_max_cte AS 
  (SELECT score_max."KEY" AS "KEY", score_max."Score_0" AS "Score_0", score_max."Score_1" AS "Score_1", score_max."Score_2" AS "Score_2", score_max."Score_3" AS "Score_3", score_max."Score_4" AS "Score_4", score_max."Score_5" AS "Score_5", score_max."Score_6" AS "Score_6", score_max."Score_7" AS "Score_7", score_max."Score_8" AS "Score_8", score_max."Score_9" AS "Score_9", score_max."Proba_0" AS "Proba_0", score_max."Proba_1" AS "Proba_1", score_max."Proba_2" AS "Proba_2", score_max."Proba_3" AS "Proba_3", score_max."Proba_4" AS "Proba_4", score_max."Proba_5" AS "Proba_5", score_max."Proba_6" AS "Proba_6", score_max."Proba_7" AS "Proba_7", score_max."Proba_8" AS "Proba_8", score_max."Proba_9" AS "Proba_9", score_max."LogProba_0" AS "LogProba_0", score_max."LogProba_1" AS "LogProba_1", score_max."LogProba_2" AS "LogProba_2", score_max."LogProba_3" AS "LogProba_3", score_max."LogProba_4" AS "LogProba_4", score_max."LogProba_5" AS "LogProba_5", score_max."LogProba_6" AS "LogProba_6", score_max."LogProba_7" AS "LogProba_7", score_max."LogProba_8" AS "LogProba_8", score_max."LogProba_9" AS "LogProba_9", score_max."Decision" AS "Decision", score_max."DecisionProba" AS "DecisionProba", score_max."KEY_m" AS "KEY_m", score_max."max_Proba" AS "max_Proba", "arg_max_t_Proba"."KEY_Proba" AS "KEY_Proba", "arg_max_t_Proba"."arg_max_Proba" AS "arg_max_Proba" 
  FROM score_max LEFT OUTER JOIN (SELECT union_with_max."KEY" AS "KEY_Proba", max(union_with_max.class) AS "arg_max_Proba" 
  FROM union_with_max 
  WHERE union_with_max."max_Proba" <= union_with_max."Proba" GROUP BY union_with_max."KEY") AS "arg_max_t_Proba" ON score_max."KEY" = "arg_max_t_Proba"."KEY_Proba")
   SELECT arg_max_cte."KEY" AS "KEY", arg_max_cte."Score_0" AS "Score_0", arg_max_cte."Score_1" AS "Score_1", arg_max_cte."Score_2" AS "Score_2", arg_max_cte."Score_3" AS "Score_3", arg_max_cte."Score_4" AS "Score_4", arg_max_cte."Score_5" AS "Score_5", arg_max_cte."Score_6" AS "Score_6", arg_max_cte."Score_7" AS "Score_7", arg_max_cte."Score_8" AS "Score_8", arg_max_cte."Score_9" AS "Score_9", arg_max_cte."Proba_0" AS "Proba_0", arg_max_cte."Proba_1" AS "Proba_1", arg_max_cte."Proba_2" AS "Proba_2", arg_max_cte."Proba_3" AS "Proba_3", arg_max_cte."Proba_4" AS "Proba_4", arg_max_cte."Proba_5" AS "Proba_5", arg_max_cte."Proba_6" AS "Proba_6", arg_max_cte."Proba_7" AS "Proba_7", arg_max_cte."Proba_8" AS "Proba_8", arg_max_cte."Proba_9" AS "Proba_9", CASE WHEN (arg_max_cte."Proba_0" IS NULL OR arg_max_cte."Proba_0" > 0.0) THEN ln(arg_max_cte."Proba_0") ELSE -1.79769313486231e+308 END AS "LogProba_0", CASE WHEN (arg_max_cte."Proba_1" IS NULL OR arg_max_cte."Proba_1" > 0.0) THEN ln(arg_max_cte."Proba_1") ELSE -1.79769313486231e+308 END AS "LogProba_1", CASE WHEN (arg_max_cte."Proba_2" IS NULL OR arg_max_cte."Proba_2" > 0.0) THEN ln(arg_max_cte."Proba_2") ELSE -1.79769313486231e+308 END AS "LogProba_2", CASE WHEN (arg_max_cte."Proba_3" IS NULL OR arg_max_cte."Proba_3" > 0.0) THEN ln(arg_max_cte."Proba_3") ELSE -1.79769313486231e+308 END AS "LogProba_3", CASE WHEN (arg_max_cte."Proba_4" IS NULL OR arg_max_cte."Proba_4" > 0.0) THEN ln(arg_max_cte."Proba_4") ELSE -1.79769313486231e+308 END AS "LogProba_4", CASE WHEN (arg_max_cte."Proba_5" IS NULL OR arg_max_cte."Proba_5" > 0.0) THEN ln(arg_max_cte."Proba_5") ELSE -1.79769313486231e+308 END AS "LogProba_5", CASE WHEN (arg_max_cte."Proba_6" IS NULL OR arg_max_cte."Proba_6" > 0.0) THEN ln(arg_max_cte."Proba_6") ELSE -1.79769313486231e+308 END AS "LogProba_6", CASE WHEN (arg_max_cte."Proba_7" IS NULL OR arg_max_cte."Proba_7" > 0.0) THEN ln(arg_max_cte."Proba_7") ELSE -1.79769313486231e+308 END AS "LogProba_7", CASE WHEN (arg_max_cte."Proba_8" IS NULL OR arg_max_cte."Proba_8" > 0.0) THEN ln(arg_max_cte."Proba_8") ELSE -1.79769313486231e+308 END AS "LogProba_8", CASE WHEN (arg_max_cte."Proba_9" IS NULL OR arg_max_cte."Proba_9" > 0.0) THEN ln(arg_max_cte."Proba_9") ELSE -1.79769313486231e+308 END AS "LogProba_9", arg_max_cte."arg_max_Proba" AS "Decision", arg_max_cte."max_Proba" AS "DecisionProba" 
  FROM arg_max_cte