text: -- This SQL code was generated by sklearn2sql (development version).
  -- Copyright 2018
  
  -- Model : KerasClassifier_GRU
  -- Dataset : digits
  -- Database : pgsql
  
  
  -- 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), 
  gru_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", -0.12309344591683183 + -0.038138244804945265 * keras_input_1_rn."Feature_0" + -0.09011044953938964 * keras_input_1_rn."Feature_1" + -0.24833913356658788 * keras_input_1_rn."Feature_2" + 0.007607457573214553 * keras_input_1_rn."Feature_3" + -0.1072207111114659 * keras_input_1_rn."Feature_4" + -0.10788489520409253 * keras_input_1_rn."Feature_5" + -0.15064139885908104 * keras_input_1_rn."Feature_6" + 0.26216582578354053 * keras_input_1_rn."Feature_7" + 0.25417999983016937 * keras_input_1_rn."Feature_8" + -0.20947815889990154 * keras_input_1_rn."Feature_9" + -0.14770332878952455 * keras_input_1_rn."Feature_10" + -0.08329578291599436 * keras_input_1_rn."Feature_11" + -0.2366610587105116 * keras_input_1_rn."Feature_12" + -0.37477595157130356 * keras_input_1_rn."Feature_13" + 0.1774429977146858 * keras_input_1_rn."Feature_14" + -0.060901315225626015 * keras_input_1_rn."Feature_15" + 0.048475770010508255 * keras_input_1_rn."Feature_16" + -0.12452068692391734 * keras_input_1_rn."Feature_17" + 0.04951016604932797 * keras_input_1_rn."Feature_18" + 0.11947897380315407 * keras_input_1_rn."Feature_19" + -0.0275652590398507 * keras_input_1_rn."Feature_20" + -0.44426973931887886 * keras_input_1_rn."Feature_21" + -0.09294996825360159 * keras_input_1_rn."Feature_22" + -0.3367213626260055 * keras_input_1_rn."Feature_23" + 0.09768635908174578 * keras_input_1_rn."Feature_24" + -0.0350394252859554 * keras_input_1_rn."Feature_25" + 0.0037327254285176585 * keras_input_1_rn."Feature_26" + -0.12151248451183114 * keras_input_1_rn."Feature_27" + -0.15329927099133342 * keras_input_1_rn."Feature_28" + -0.2711957367701058 * keras_input_1_rn."Feature_29" + -0.3062543949411958 * keras_input_1_rn."Feature_30" + 0.03516591205734079 * keras_input_1_rn."Feature_31" + 0.18737930990275387 * keras_input_1_rn."Feature_32" + 0.11099234874747646 * keras_input_1_rn."Feature_33" + 0.260231065244664 * keras_input_1_rn."Feature_34" + 0.28656848931616274 * keras_input_1_rn."Feature_35" + -0.22289357058075532 * keras_input_1_rn."Feature_36" + 0.05616744899910297 * keras_input_1_rn."Feature_37" + -0.2395247201895297 * keras_input_1_rn."Feature_38" + 0.27714779416796126 * keras_input_1_rn."Feature_39" + -0.2418054337850144 * keras_input_1_rn."Feature_40" + -0.1564489815304202 * keras_input_1_rn."Feature_41" + 0.09457607429881079 * keras_input_1_rn."Feature_42" + 0.07203964955692962 * keras_input_1_rn."Feature_43" + -0.41993351687334424 * keras_input_1_rn."Feature_44" + 0.21451933815870114 * keras_input_1_rn."Feature_45" + 0.2691895688409417 * keras_input_1_rn."Feature_46" + 0.25924686653206835 * keras_input_1_rn."Feature_47" + 0.1654332344803938 * keras_input_1_rn."Feature_48" + -0.3019011651704979 * keras_input_1_rn."Feature_49" + 0.17515267216763553 * keras_input_1_rn."Feature_50" + 0.20421436927753586 * keras_input_1_rn."Feature_51" + -0.23802170976806988 * keras_input_1_rn."Feature_52" + 0.08275656131585647 * keras_input_1_rn."Feature_53" + -0.15592217712205958 * keras_input_1_rn."Feature_54" + -0.08113332089794997 * keras_input_1_rn."Feature_55" + 0.17629045534918097 * keras_input_1_rn."Feature_56" + -0.02317585722741398 * keras_input_1_rn."Feature_57" + -0.3749522843768838 * keras_input_1_rn."Feature_58" + -0.04145000243150485 * keras_input_1_rn."Feature_59" + -0.24802054768236928 * keras_input_1_rn."Feature_60" + 0.3706372259547449 * keras_input_1_rn."Feature_61" + -0.07311361746274371 * keras_input_1_rn."Feature_62" + -0.15746164898720172 * keras_input_1_rn."Feature_63" AS dot_prod_z_1, 0.0 + 0.21410816091725565 * keras_input_1_rn."Feature_0" + -0.03943415881184331 * keras_input_1_rn."Feature_1" + 0.23223151304709516 * keras_input_1_rn."Feature_2" + 0.28926354604410814 * keras_input_1_rn."Feature_3" + 0.10410760097950555 * keras_input_1_rn."Feature_4" + 0.2664615049361782 * keras_input_1_rn."Feature_5" + -0.025523485574457994 * keras_input_1_rn."Feature_6" + -0.061921023243611345 * keras_input_1_rn."Feature_7" + 0.0396511937119447 * keras_input_1_rn."Feature_8" + 0.07875185491762221 * keras_input_1_rn."Feature_9" + 0.23982970752022797 * keras_input_1_rn."Feature_10" + 0.29089684112178754 * keras_input_1_rn."Feature_11" + 0.1341609398513855 * keras_input_1_rn."Feature_12" + 0.2623140775995036 * keras_input_1_rn."Feature_13" + -0.11842649155442647 * keras_input_1_rn."Feature_14" + -0.2501183810766876 * keras_input_1_rn."Feature_15" + 0.0373194318819553 * keras_input_1_rn."Feature_16" + 0.06976430295333114 * keras_input_1_rn."Feature_17" + 0.2555892193522267 * keras_input_1_rn."Feature_18" + -0.1027519871168153 * keras_input_1_rn."Feature_19" + -0.2482333727468275 * keras_input_1_rn."Feature_20" + 0.1431067353370763 * keras_input_1_rn."Feature_21" + 0.2650158465741021 * keras_input_1_rn."Feature_22" + 0.03455258097605174 * keras_input_1_rn."Feature_23" + -0.2565399257034385 * keras_input_1_rn."Feature_24" + 0.1702649661593782 * keras_input_1_rn."Feature_25" + 0.1077000703205871 * keras_input_1_rn."Feature_26" + 0.08561359394649098 * keras_input_1_rn."Feature_27" + 0.056485644149378234 * keras_input_1_rn."Feature_28" + 0.14704458296981193 * keras_input_1_rn."Feature_29" + 0.04211323632068692 * keras_input_1_rn."Feature_30" + 0.02482355650782636 * keras_input_1_rn."Feature_31" + 0.2419424290876207 * keras_input_1_rn."Feature_32" + -0.13332861979016017 * keras_input_1_rn."Feature_33" + -0.15680510379698084 * keras_input_1_rn."Feature_34" + -0.09887079351932787 * keras_input_1_rn."Feature_35" + -0.07829083648717136 * keras_input_1_rn."Feature_36" + 0.19219486156651872 * keras_input_1_rn."Feature_37" + 0.1228967451861594 * keras_input_1_rn."Feature_38" + 0.17813552601798982 * keras_input_1_rn."Feature_39" + -0.11272147853746786 * keras_input_1_rn."Feature_40" + -0.02032037678284998 * keras_input_1_rn."Feature_41" + -0.2139048874510249 * keras_input_1_rn."Feature_42" + 0.26108810752035433 * keras_input_1_rn."Feature_43" + -0.11140669815124016 * keras_input_1_rn."Feature_44" + 0.2892635444596274 * keras_input_1_rn."Feature_45" + -0.03471022277232966 * keras_input_1_rn."Feature_46" + -0.06008155611494442 * keras_input_1_rn."Feature_47" + -0.16474837431397357 * keras_input_1_rn."Feature_48" + 0.03506618967311531 * keras_input_1_rn."Feature_49" + 0.024793278724403345 * keras_input_1_rn."Feature_50" + 0.08073427501208336 * keras_input_1_rn."Feature_51" + 0.20805575514419944 * keras_input_1_rn."Feature_52" + -0.1766350765620194 * keras_input_1_rn."Feature_53" + 0.2169490195339338 * keras_input_1_rn."Feature_54" + 0.10664287829794211 * keras_input_1_rn."Feature_55" + 0.08954293307425759 * keras_input_1_rn."Feature_56" + -0.00490147576583283 * keras_input_1_rn."Feature_57" + -0.18564708272128896 * keras_input_1_rn."Feature_58" + 0.2752990411447061 * keras_input_1_rn."Feature_59" + -0.03310944591842152 * keras_input_1_rn."Feature_60" + -0.15403501974888686 * keras_input_1_rn."Feature_61" + 0.14443601036297987 * keras_input_1_rn."Feature_62" + 0.15887050887930926 * keras_input_1_rn."Feature_63" AS dot_prod_z_2, 0.0 + -0.1811174227506493 * keras_input_1_rn."Feature_0" + -0.2316850684790943 * keras_input_1_rn."Feature_1" + -0.2843382285899399 * keras_input_1_rn."Feature_2" + -0.2503052551792232 * keras_input_1_rn."Feature_3" + -0.21404771580623538 * keras_input_1_rn."Feature_4" + 0.1286067542849363 * keras_input_1_rn."Feature_5" + 0.005844288023291566 * keras_input_1_rn."Feature_6" + -0.2306966458818063 * keras_input_1_rn."Feature_7" + 0.10019236798534881 * keras_input_1_rn."Feature_8" + -0.021455091648768354 * keras_input_1_rn."Feature_9" + 0.1848292135955109 * keras_input_1_rn."Feature_10" + 0.13228944739427184 * keras_input_1_rn."Feature_11" + 0.10388326260181557 * keras_input_1_rn."Feature_12" + -0.12954968299378675 * keras_input_1_rn."Feature_13" + -0.2817287698385793 * keras_input_1_rn."Feature_14" + -0.1543680522468948 * keras_input_1_rn."Feature_15" + -0.25900850056124414 * keras_input_1_rn."Feature_16" + 0.16421205197774985 * keras_input_1_rn."Feature_17" + -0.03388401561292542 * keras_input_1_rn."Feature_18" + 0.12021389031679669 * keras_input_1_rn."Feature_19" + 0.01464142254594547 * keras_input_1_rn."Feature_20" + 0.2685035713885339 * keras_input_1_rn."Feature_21" + -0.1096745442603537 * keras_input_1_rn."Feature_22" + 0.06547241348288546 * keras_input_1_rn."Feature_23" + 0.12527514291828523 * keras_input_1_rn."Feature_24" + -0.06163217015477629 * keras_input_1_rn."Feature_25" + -0.009854585672252059 * keras_input_1_rn."Feature_26" + -0.2473762136535261 * keras_input_1_rn."Feature_27" + 0.139932909314534 * keras_input_1_rn."Feature_28" + -0.2016990882477625 * keras_input_1_rn."Feature_29" + 0.23287537462688418 * keras_input_1_rn."Feature_30" + -0.011688433692694733 * keras_input_1_rn."Feature_31" + 0.27349061601852825 * keras_input_1_rn."Feature_32" + -0.2630499452210739 * keras_input_1_rn."Feature_33" + 0.06950574043200375 * keras_input_1_rn."Feature_34" + -0.03127147551734033 * keras_input_1_rn."Feature_35" + -0.003677468049945065 * keras_input_1_rn."Feature_36" + -0.08191905844239386 * keras_input_1_rn."Feature_37" + 0.17100447312976852 * keras_input_1_rn."Feature_38" + -0.054572880178119676 * keras_input_1_rn."Feature_39" + -0.1401022226600388 * keras_input_1_rn."Feature_40" + 0.18501315781602728 * keras_input_1_rn."Feature_41" + 0.16366608641476343 * keras_input_1_rn."Feature_42" + -0.21835916297658095 * keras_input_1_rn."Feature_43" + 0.26303577070213285 * keras_input_1_rn."Feature_44" + 0.14726940516275583 * keras_input_1_rn."Feature_45" + -0.1377417108157103 * keras_input_1_rn."Feature_46" + -0.051668058419775315 * keras_input_1_rn."Feature_47" + 0.18939523508351636 * keras_input_1_rn."Feature_48" + 0.2620166361339919 * keras_input_1_rn."Feature_49" + 0.23450393411065568 * keras_input_1_rn."Feature_50" + -0.09860424741634516 * keras_input_1_rn."Feature_51" + 0.06506772369754471 * keras_input_1_rn."Feature_52" + 0.12533603458199444 * keras_input_1_rn."Feature_53" + 0.08002580259745151 * keras_input_1_rn."Feature_54" + -0.2569664400347594 * keras_input_1_rn."Feature_55" + 0.041869583408725985 * keras_input_1_rn."Feature_56" + -0.16541889291914438 * keras_input_1_rn."Feature_57" + 0.12657420739854658 * keras_input_1_rn."Feature_58" + 0.15417334044705672 * keras_input_1_rn."Feature_59" + 0.023772063160238555 * keras_input_1_rn."Feature_60" + 0.09314177567484966 * keras_input_1_rn."Feature_61" + -0.14437441690207298 * keras_input_1_rn."Feature_62" + 0.1505383000458817 * keras_input_1_rn."Feature_63" AS dot_prod_r_1, 0.0 + -0.10915331861008476 * keras_input_1_rn."Feature_0" + 0.04824283937940965 * keras_input_1_rn."Feature_1" + -0.10547660167209344 * keras_input_1_rn."Feature_2" + -0.14990642543276023 * keras_input_1_rn."Feature_3" + 0.2879330308427555 * keras_input_1_rn."Feature_4" + -0.14743831654017087 * keras_input_1_rn."Feature_5" + -0.1622209703339519 * keras_input_1_rn."Feature_6" + 0.0714368322932647 * keras_input_1_rn."Feature_7" + 0.0248537731887028 * keras_input_1_rn."Feature_8" + 0.12089808011231423 * keras_input_1_rn."Feature_9" + 0.1581035477605321 * keras_input_1_rn."Feature_10" + 0.2300602314973748 * keras_input_1_rn."Feature_11" + 0.29042973531720445 * keras_input_1_rn."Feature_12" + 0.09859826959312723 * keras_input_1_rn."Feature_13" + 0.19875345581761955 * keras_input_1_rn."Feature_14" + -0.22357677656056993 * keras_input_1_rn."Feature_15" + -0.04963972794016336 * keras_input_1_rn."Feature_16" + -0.056817548123074074 * keras_input_1_rn."Feature_17" + 0.2652553775566551 * keras_input_1_rn."Feature_18" + -0.021919504574755777 * keras_input_1_rn."Feature_19" + -0.27153358351326856 * keras_input_1_rn."Feature_20" + -0.17345750067549626 * keras_input_1_rn."Feature_21" + -0.044909009103400815 * keras_input_1_rn."Feature_22" + -0.200408616387735 * keras_input_1_rn."Feature_23" + -0.04567492252933311 * keras_input_1_rn."Feature_24" + -0.25537655723503067 * keras_input_1_rn."Feature_25" + 0.2259653929706827 * keras_input_1_rn."Feature_26" + 0.03429888111007179 * keras_input_1_rn."Feature_27" + 0.0928099488864303 * keras_input_1_rn."Feature_28" + 0.11080169224828257 * keras_input_1_rn."Feature_29" + 0.01576907675713929 * keras_input_1_rn."Feature_30" + 0.12187405815312846 * keras_input_1_rn."Feature_31" + 0.17917674658829746 * keras_input_1_rn."Feature_32" + 0.07391867530538532 * keras_input_1_rn."Feature_33" + 0.08172573567679303 * keras_input_1_rn."Feature_34" + 0.20116040461845158 * keras_input_1_rn."Feature_35" + 0.2579985192957837 * keras_input_1_rn."Feature_36" + 0.038913716584022706 * keras_input_1_rn."Feature_37" + -0.26869108672498093 * keras_input_1_rn."Feature_38" + -0.00822751660729093 * keras_input_1_rn."Feature_39" + -0.1915740843654592 * keras_input_1_rn."Feature_40" + 0.20090013029034204 * keras_input_1_rn."Feature_41" + -0.08980882155714379 * keras_input_1_rn."Feature_42" + 0.001999944130885223 * keras_input_1_rn."Feature_43" + 0.21635021248177017 * keras_input_1_rn."Feature_44" + -0.25136579570162704 * keras_input_1_rn."Feature_45" + 0.010157382890016886 * keras_input_1_rn."Feature_46" + -0.22554669323158216 * keras_input_1_rn."Feature_47" + 0.1300691834545048 * keras_input_1_rn."Feature_48" + -0.04514428363931855 * keras_input_1_rn."Feature_49" + 0.06280686575229794 * keras_input_1_rn."Feature_50" + 0.16621411592439211 * keras_input_1_rn."Feature_51" + -0.28414704095396354 * keras_input_1_rn."Feature_52" + 0.09222472364476964 * keras_input_1_rn."Feature_53" + -0.03906817456524314 * keras_input_1_rn."Feature_54" + -0.28567524013926804 * keras_input_1_rn."Feature_55" + 0.22904305964009058 * keras_input_1_rn."Feature_56" + -0.2598334641888888 * keras_input_1_rn."Feature_57" + -0.1706202959774124 * keras_input_1_rn."Feature_58" + 0.06935284342467335 * keras_input_1_rn."Feature_59" + 0.05629458876819643 * keras_input_1_rn."Feature_60" + 0.26129167714013907 * keras_input_1_rn."Feature_61" + -0.10820735958625255 * keras_input_1_rn."Feature_62" + 0.22257529894422984 * keras_input_1_rn."Feature_63" AS dot_prod_r_2, -0.00684090495716213 + -0.21265558946346613 * keras_input_1_rn."Feature_0" + -0.07253853573515562 * keras_input_1_rn."Feature_1" + -0.32435856446397526 * keras_input_1_rn."Feature_2" + 0.10034889678239384 * keras_input_1_rn."Feature_3" + -0.27737794945758293 * keras_input_1_rn."Feature_4" + -0.15328155831091994 * keras_input_1_rn."Feature_5" + -0.26269367492294204 * keras_input_1_rn."Feature_6" + -0.4615373605538931 * keras_input_1_rn."Feature_7" + -0.1559617560474218 * keras_input_1_rn."Feature_8" + -0.041955123785416305 * keras_input_1_rn."Feature_9" + -0.012455790351054229 * keras_input_1_rn."Feature_10" + -0.1005685414463763 * keras_input_1_rn."Feature_11" + -0.0647561971330823 * keras_input_1_rn."Feature_12" + 0.3431206653975547 * keras_input_1_rn."Feature_13" + 0.10329835894597571 * keras_input_1_rn."Feature_14" + -0.5484669176784419 * keras_input_1_rn."Feature_15" + -0.08491971757359218 * keras_input_1_rn."Feature_16" + 0.009970492325176028 * keras_input_1_rn."Feature_17" + -0.011267534478218936 * keras_input_1_rn."Feature_18" + 0.21425222676551428 * keras_input_1_rn."Feature_19" + 0.35742704260454644 * keras_input_1_rn."Feature_20" + 0.544347542021428 * keras_input_1_rn."Feature_21" + 0.41234623702336676 * keras_input_1_rn."Feature_22" + -0.3112098956419665 * keras_input_1_rn."Feature_23" + 0.27102797362169573 * keras_input_1_rn."Feature_24" + -0.16314900687761016 * keras_input_1_rn."Feature_25" + -0.05585909488896556 * keras_input_1_rn."Feature_26" + 0.0561478144042932 * keras_input_1_rn."Feature_27" + -0.05113103949800241 * keras_input_1_rn."Feature_28" + -0.25179687499936615 * keras_input_1_rn."Feature_29" + -0.04816123749351311 * keras_input_1_rn."Feature_30" + 0.08845132834126893 * keras_input_1_rn."Feature_31" + 0.14691416648224104 * keras_input_1_rn."Feature_32" + -0.19907360626156287 * keras_input_1_rn."Feature_33" + -0.2396945486019098 * keras_input_1_rn."Feature_34" + 0.09843217125085818 * keras_input_1_rn."Feature_35" + -0.15906802238216078 * keras_input_1_rn."Feature_36" + -0.28677852379802593 * keras_input_1_rn."Feature_37" + -0.28000310717741544 * keras_input_1_rn."Feature_38" + 0.22087379505765842 * keras_input_1_rn."Feature_39" + -0.1771355077594712 * keras_input_1_rn."Feature_40" + -0.22611417724440946 * keras_input_1_rn."Feature_41" + 0.2260764789833629 * keras_input_1_rn."Feature_42" + -0.3636957036145088 * keras_input_1_rn."Feature_43" + -0.08637004545863336 * keras_input_1_rn."Feature_44" + 0.10718514577159775 * keras_input_1_rn."Feature_45" + -0.08323192011976313 * keras_input_1_rn."Feature_46" + -0.2677109000320322 * keras_input_1_rn."Feature_47" + 0.02186703335049564 * keras_input_1_rn."Feature_48" + -0.1274764865364142 * keras_input_1_rn."Feature_49" + 0.14767259552249232 * keras_input_1_rn."Feature_50" + -0.0007884536111032009 * keras_input_1_rn."Feature_51" + 0.12955919479008188 * keras_input_1_rn."Feature_52" + 0.1103065355494478 * keras_input_1_rn."Feature_53" + 0.016405157044847168 * keras_input_1_rn."Feature_54" + -0.09976530478827002 * keras_input_1_rn."Feature_55" + -0.19156826248659617 * keras_input_1_rn."Feature_56" + -0.020601266985657906 * keras_input_1_rn."Feature_57" + -0.0632521131802963 * keras_input_1_rn."Feature_58" + 0.1369110960086822 * keras_input_1_rn."Feature_59" + 0.1985664219167767 * keras_input_1_rn."Feature_60" + 0.2576335417154253 * keras_input_1_rn."Feature_61" + 0.09680850415401504 * keras_input_1_rn."Feature_62" + -0.15334889076951722 * keras_input_1_rn."Feature_63" AS dot_prod_h_1, 0.0 + 0.12606118197494143 * keras_input_1_rn."Feature_0" + -0.1042135979559996 * keras_input_1_rn."Feature_1" + 0.07546351405508211 * keras_input_1_rn."Feature_2" + -0.07811007314324511 * keras_input_1_rn."Feature_3" + 0.21531957193641976 * keras_input_1_rn."Feature_4" + 0.03172832916006707 * keras_input_1_rn."Feature_5" + 0.12379950422558511 * keras_input_1_rn."Feature_6" + 0.2875388236161221 * keras_input_1_rn."Feature_7" + -0.02134868032827053 * keras_input_1_rn."Feature_8" + 0.1347905471044869 * keras_input_1_rn."Feature_9" + 0.14110483288785886 * keras_input_1_rn."Feature_10" + -0.2797491602177458 * keras_input_1_rn."Feature_11" + -0.275213777828885 * keras_input_1_rn."Feature_12" + -0.28805693515295194 * keras_input_1_rn."Feature_13" + 0.12129403170990366 * keras_input_1_rn."Feature_14" + 0.11839611573278586 * keras_input_1_rn."Feature_15" + -0.07100196613090576 * keras_input_1_rn."Feature_16" + 0.17328180459928577 * keras_input_1_rn."Feature_17" + -0.059389072484219596 * keras_input_1_rn."Feature_18" + -0.2676061352895698 * keras_input_1_rn."Feature_19" + 0.011414765100711544 * keras_input_1_rn."Feature_20" + 0.25601879343317163 * keras_input_1_rn."Feature_21" + 0.18213025734119226 * keras_input_1_rn."Feature_22" + 0.060515166209866966 * keras_input_1_rn."Feature_23" + -0.1818328860629173 * keras_input_1_rn."Feature_24" + 0.2777767625179703 * keras_input_1_rn."Feature_25" + 0.004967499420992505 * keras_input_1_rn."Feature_26" + -0.009774802349705236 * keras_input_1_rn."Feature_27" + 0.04648026328531035 * keras_input_1_rn."Feature_28" + 0.17378821393984945 * keras_input_1_rn."Feature_29" + 0.1056018663458973 * keras_input_1_rn."Feature_30" + -0.09301164610836823 * keras_input_1_rn."Feature_31" + 0.2015942396868859 * keras_input_1_rn."Feature_32" + 0.20357800214575433 * keras_input_1_rn."Feature_33" + 0.0434282006581711 * keras_input_1_rn."Feature_34" + -0.2795117985719012 * keras_input_1_rn."Feature_35" + -0.25077650411301716 * keras_input_1_rn."Feature_36" + -0.22203703339090558 * keras_input_1_rn."Feature_37" + -0.25082795325068824 * keras_input_1_rn."Feature_38" + 0.008024262975822871 * keras_input_1_rn."Feature_39" + 0.20936697509670182 * keras_input_1_rn."Feature_40" + 0.1469230065983163 * keras_input_1_rn."Feature_41" + 0.2613012656451293 * keras_input_1_rn."Feature_42" + -0.2805472019067756 * keras_input_1_rn."Feature_43" + -0.0012586791263094477 * keras_input_1_rn."Feature_44" + 0.0773556829874395 * keras_input_1_rn."Feature_45" + -0.20799949415124297 * keras_input_1_rn."Feature_46" + -0.11302721270454139 * keras_input_1_rn."Feature_47" + -0.1559946910934968 * keras_input_1_rn."Feature_48" + 0.18805701992274698 * keras_input_1_rn."Feature_49" + 0.010257149641189456 * keras_input_1_rn."Feature_50" + 0.09259284834989762 * keras_input_1_rn."Feature_51" + 0.020116427901734124 * keras_input_1_rn."Feature_52" + -0.022531582053546828 * keras_input_1_rn."Feature_53" + 0.12563146643795753 * keras_input_1_rn."Feature_54" + 0.0770882704310516 * keras_input_1_rn."Feature_55" + 0.2816560750815838 * keras_input_1_rn."Feature_56" + -0.1738167106114335 * keras_input_1_rn."Feature_57" + 0.04548122805400967 * keras_input_1_rn."Feature_58" + -0.12200759872579453 * keras_input_1_rn."Feature_59" + -0.2836780617896003 * keras_input_1_rn."Feature_60" + 0.1984492268797397 * keras_input_1_rn."Feature_61" + -0.014868232573675089 * keras_input_1_rn."Feature_62" + -0.06329222200406662 * keras_input_1_rn."Feature_63" AS dot_prod_h_2 
  FROM keras_input_1_rn), 
  rnn_cte_gru_1(rn_1, "KEY", "PreviousState_1", "PreviousState_2", "State_1", "State_2") AS 
  (SELECT gru_input_kernel_bias.rn AS rn_1, gru_input_kernel_bias."KEY" AS "KEY", CAST(0.0 AS FLOAT) AS "PreviousState_1", CAST(0.0 AS FLOAT) AS "PreviousState_2", (1.0 - greatest(0.0, least(1.0, 0.2 * (0.0 + gru_input_kernel_bias.dot_prod_z_1) + 0.5))) * ((exp(0.0 + gru_input_kernel_bias.dot_prod_h_1) - exp(-(0.0 + gru_input_kernel_bias.dot_prod_h_1))) / (exp(0.0 + gru_input_kernel_bias.dot_prod_h_1) + exp(-(0.0 + gru_input_kernel_bias.dot_prod_h_1)))) AS "State_1", (1.0 - greatest(0.0, least(1.0, 0.2 * (0.0 + gru_input_kernel_bias.dot_prod_z_2) + 0.5))) * ((exp(0.0 + gru_input_kernel_bias.dot_prod_h_2) - exp(-(0.0 + gru_input_kernel_bias.dot_prod_h_2))) / (exp(0.0 + gru_input_kernel_bias.dot_prod_h_2) + exp(-(0.0 + gru_input_kernel_bias.dot_prod_h_2)))) AS "State_2" 
  FROM gru_input_kernel_bias 
  WHERE gru_input_kernel_bias.rn = 1 UNION ALL SELECT gru_input_kernel_bias.rn AS rn, gru_input_kernel_bias."KEY" AS "KEY", CAST(0.0 AS FLOAT) AS "PreviousState_1", CAST(0.0 AS FLOAT) AS "PreviousState_2", greatest(0.0, least(1.0, 0.2 * (0.07889305402131672 * CAST(0.0 AS FLOAT) + -0.12001338771399168 * CAST(0.0 AS FLOAT) + gru_input_kernel_bias.dot_prod_z_1) + 0.5)) * CAST(0.0 AS FLOAT) + (1.0 - greatest(0.0, least(1.0, 0.2 * (0.07889305402131672 * CAST(0.0 AS FLOAT) + -0.12001338771399168 * CAST(0.0 AS FLOAT) + gru_input_kernel_bias.dot_prod_z_1) + 0.5))) * ((exp(0.19308494093258327 * greatest(0.0, least(1.0, 0.2 * (-0.1888178280225422 * CAST(0.0 AS FLOAT) + -0.24466095906319685 * CAST(0.0 AS FLOAT) + gru_input_kernel_bias.dot_prod_r_1) + 0.5)) * CAST(0.0 AS FLOAT) + 0.7665848473643189 * greatest(0.0, least(1.0, 0.2 * (-0.5245068706087566 * CAST(0.0 AS FLOAT) + -0.3341770861035541 * CAST(0.0 AS FLOAT) + gru_input_kernel_bias.dot_prod_r_2) + 0.5)) * CAST(0.0 AS FLOAT) + gru_input_kernel_bias.dot_prod_h_1) - exp(-(0.19308494093258327 * greatest(0.0, least(1.0, 0.2 * (-0.1888178280225422 * CAST(0.0 AS FLOAT) + -0.24466095906319685 * CAST(0.0 AS FLOAT) + gru_input_kernel_bias.dot_prod_r_1) + 0.5)) * CAST(0.0 AS FLOAT) + 0.7665848473643189 * greatest(0.0, least(1.0, 0.2 * (-0.5245068706087566 * CAST(0.0 AS FLOAT) + -0.3341770861035541 * CAST(0.0 AS FLOAT) + gru_input_kernel_bias.dot_prod_r_2) + 0.5)) * CAST(0.0 AS FLOAT) + gru_input_kernel_bias.dot_prod_h_1))) / (exp(0.19308494093258327 * greatest(0.0, least(1.0, 0.2 * (-0.1888178280225422 * CAST(0.0 AS FLOAT) + -0.24466095906319685 * CAST(0.0 AS FLOAT) + gru_input_kernel_bias.dot_prod_r_1) + 0.5)) * CAST(0.0 AS FLOAT) + 0.7665848473643189 * greatest(0.0, least(1.0, 0.2 * (-0.5245068706087566 * CAST(0.0 AS FLOAT) + -0.3341770861035541 * CAST(0.0 AS FLOAT) + gru_input_kernel_bias.dot_prod_r_2) + 0.5)) * CAST(0.0 AS FLOAT) + gru_input_kernel_bias.dot_prod_h_1) + exp(-(0.19308494093258327 * greatest(0.0, least(1.0, 0.2 * (-0.1888178280225422 * CAST(0.0 AS FLOAT) + -0.24466095906319685 * CAST(0.0 AS FLOAT) + gru_input_kernel_bias.dot_prod_r_1) + 0.5)) * CAST(0.0 AS FLOAT) + 0.7665848473643189 * greatest(0.0, least(1.0, 0.2 * (-0.5245068706087566 * CAST(0.0 AS FLOAT) + -0.3341770861035541 * CAST(0.0 AS FLOAT) + gru_input_kernel_bias.dot_prod_r_2) + 0.5)) * CAST(0.0 AS FLOAT) + gru_input_kernel_bias.dot_prod_h_1)))) AS "State_1", greatest(0.0, least(1.0, 0.2 * (-0.034960511057912716 * CAST(0.0 AS FLOAT) + -0.14062053889322335 * CAST(0.0 AS FLOAT) + gru_input_kernel_bias.dot_prod_z_2) + 0.5)) * CAST(0.0 AS FLOAT) + (1.0 - greatest(0.0, least(1.0, 0.2 * (-0.034960511057912716 * CAST(0.0 AS FLOAT) + -0.14062053889322335 * CAST(0.0 AS FLOAT) + gru_input_kernel_bias.dot_prod_z_2) + 0.5))) * ((exp(0.8028151871906004 * greatest(0.0, least(1.0, 0.2 * (-0.1888178280225422 * CAST(0.0 AS FLOAT) + -0.24466095906319685 * CAST(0.0 AS FLOAT) + gru_input_kernel_bias.dot_prod_r_1) + 0.5)) * CAST(0.0 AS FLOAT) + -0.4545734405306024 * greatest(0.0, least(1.0, 0.2 * (-0.5245068706087566 * CAST(0.0 AS FLOAT) + -0.3341770861035541 * CAST(0.0 AS FLOAT) + gru_input_kernel_bias.dot_prod_r_2) + 0.5)) * CAST(0.0 AS FLOAT) + gru_input_kernel_bias.dot_prod_h_2) - exp(-(0.8028151871906004 * greatest(0.0, least(1.0, 0.2 * (-0.1888178280225422 * CAST(0.0 AS FLOAT) + -0.24466095906319685 * CAST(0.0 AS FLOAT) + gru_input_kernel_bias.dot_prod_r_1) + 0.5)) * CAST(0.0 AS FLOAT) + -0.4545734405306024 * greatest(0.0, least(1.0, 0.2 * (-0.5245068706087566 * CAST(0.0 AS FLOAT) + -0.3341770861035541 * CAST(0.0 AS FLOAT) + gru_input_kernel_bias.dot_prod_r_2) + 0.5)) * CAST(0.0 AS FLOAT) + gru_input_kernel_bias.dot_prod_h_2))) / (exp(0.8028151871906004 * greatest(0.0, least(1.0, 0.2 * (-0.1888178280225422 * CAST(0.0 AS FLOAT) + -0.24466095906319685 * CAST(0.0 AS FLOAT) + gru_input_kernel_bias.dot_prod_r_1) + 0.5)) * CAST(0.0 AS FLOAT) + -0.4545734405306024 * greatest(0.0, least(1.0, 0.2 * (-0.5245068706087566 * CAST(0.0 AS FLOAT) + -0.3341770861035541 * CAST(0.0 AS FLOAT) + gru_input_kernel_bias.dot_prod_r_2) + 0.5)) * CAST(0.0 AS FLOAT) + gru_input_kernel_bias.dot_prod_h_2) + exp(-(0.8028151871906004 * greatest(0.0, least(1.0, 0.2 * (-0.1888178280225422 * CAST(0.0 AS FLOAT) + -0.24466095906319685 * CAST(0.0 AS FLOAT) + gru_input_kernel_bias.dot_prod_r_1) + 0.5)) * CAST(0.0 AS FLOAT) + -0.4545734405306024 * greatest(0.0, least(1.0, 0.2 * (-0.5245068706087566 * CAST(0.0 AS FLOAT) + -0.3341770861035541 * CAST(0.0 AS FLOAT) + gru_input_kernel_bias.dot_prod_r_2) + 0.5)) * CAST(0.0 AS FLOAT) + gru_input_kernel_bias.dot_prod_h_2)))) AS "State_2" 
  FROM gru_input_kernel_bias, rnn_cte_gru_1 
  WHERE gru_input_kernel_bias.rn = rnn_cte_gru_1.rn_1 + 1), 
  gru_1 AS 
  (SELECT rnn_cte_gru_1."KEY" AS "KEY", CAST(rnn_cte_gru_1."State_1" AS FLOAT) AS output_1, CAST(rnn_cte_gru_1."State_2" AS FLOAT) AS output_2 
  FROM rnn_cte_gru_1), 
  gru_1_1 AS 
  (SELECT gru_1."KEY" AS "KEY", gru_1.output_1 AS output_1, gru_1.output_2 AS output_2 
  FROM gru_1), 
  layer_dense_1 AS 
  (SELECT gru_1_1."KEY" AS "KEY", -0.24438575675281415 + 0.9386956509233452 * gru_1_1.output_1 + 0.5424135932957269 * gru_1_1.output_2 AS output_1, -0.2525185799361129 + 1.009645990948237 * gru_1_1.output_1 + -0.08930802016302786 * gru_1_1.output_2 AS output_2, 0.09116795882918467 + 0.6998769474138282 * gru_1_1.output_1 + -0.008677480827115724 * gru_1_1.output_2 AS output_3, -0.20844370263678128 + 0.9822244282520982 * gru_1_1.output_1 + -0.6799937517884553 * gru_1_1.output_2 AS output_4, 0.1298079645035531 + -2.1840309420650463 * gru_1_1.output_1 + -0.5253217508274673 * gru_1_1.output_2 AS output_5, 0.01485199046082474 + -2.250048454361522 * gru_1_1.output_1 + 0.33692237829211147 * gru_1_1.output_2 AS output_6, 0.5765452668035467 + -0.7793741282912945 * gru_1_1.output_1 + 0.626520689170675 * gru_1_1.output_2 AS output_7, 0.08790579294814325 + -2.1316207028777665 * gru_1_1.output_1 + 0.29123293384802795 * gru_1_1.output_2 AS output_8, -0.23444882570744519 + 0.9045786690212952 * gru_1_1.output_1 + 0.6351489760483454 * gru_1_1.output_2 AS output_9, -0.17834769022264998 + 0.896253467667346 * gru_1_1.output_1 + -0.6547915681479377 * gru_1_1.output_2 AS output_10 
  FROM gru_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(least(100.0, greatest(-100.0, layer_dense_1_1.output_1))) AS "exp_Score_0", layer_dense_1_1.output_2 AS "Score_1", exp(least(100.0, greatest(-100.0, layer_dense_1_1.output_2))) AS "exp_Score_1", layer_dense_1_1.output_3 AS "Score_2", exp(least(100.0, greatest(-100.0, layer_dense_1_1.output_3))) AS "exp_Score_2", layer_dense_1_1.output_4 AS "Score_3", exp(least(100.0, greatest(-100.0, layer_dense_1_1.output_4))) AS "exp_Score_3", layer_dense_1_1.output_5 AS "Score_4", exp(least(100.0, greatest(-100.0, layer_dense_1_1.output_5))) AS "exp_Score_4", layer_dense_1_1.output_6 AS "Score_5", exp(least(100.0, greatest(-100.0, layer_dense_1_1.output_6))) AS "exp_Score_5", layer_dense_1_1.output_7 AS "Score_6", exp(least(100.0, greatest(-100.0, layer_dense_1_1.output_7))) AS "exp_Score_6", layer_dense_1_1.output_8 AS "Score_7", exp(least(100.0, greatest(-100.0, layer_dense_1_1.output_8))) AS "exp_Score_7", layer_dense_1_1.output_9 AS "Score_8", exp(least(100.0, greatest(-100.0, layer_dense_1_1.output_9))) AS "exp_Score_8", layer_dense_1_1.output_10 AS "Score_9", exp(least(100.0, greatest(-100.0, layer_dense_1_1.output_10))) 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 FLOAT) AS "Score_0", CAST(NULL AS FLOAT) AS "Score_1", CAST(NULL AS FLOAT) AS "Score_2", CAST(NULL AS FLOAT) AS "Score_3", CAST(NULL AS FLOAT) AS "Score_4", CAST(NULL AS FLOAT) AS "Score_5", CAST(NULL AS FLOAT) AS "Score_6", CAST(NULL AS FLOAT) AS "Score_7", CAST(NULL AS FLOAT) AS "Score_8", CAST(NULL AS FLOAT) 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 FLOAT) AS "LogProba_0", CAST(NULL AS FLOAT) AS "LogProba_1", CAST(NULL AS FLOAT) AS "LogProba_2", CAST(NULL AS FLOAT) AS "LogProba_3", CAST(NULL AS FLOAT) AS "LogProba_4", CAST(NULL AS FLOAT) AS "LogProba_5", CAST(NULL AS FLOAT) AS "LogProba_6", CAST(NULL AS FLOAT) AS "LogProba_7", CAST(NULL AS FLOAT) AS "LogProba_8", CAST(NULL AS FLOAT) AS "LogProba_9", CAST(NULL AS BIGINT) AS "Decision", CAST(NULL AS FLOAT) 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