text: -- This SQL code was generated by sklearn2sql (development version).
  -- Copyright 2018
  
  -- Model : KerasClassifier_LSTM
  -- Dataset : FourClass_100
  -- Database : mysql
  
  
  -- 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`, `ADS`.`Feature_64` AS `Feature_64`, `ADS`.`Feature_65` AS `Feature_65`, `ADS`.`Feature_66` AS `Feature_66`, `ADS`.`Feature_67` AS `Feature_67`, `ADS`.`Feature_68` AS `Feature_68`, `ADS`.`Feature_69` AS `Feature_69`, `ADS`.`Feature_70` AS `Feature_70`, `ADS`.`Feature_71` AS `Feature_71`, `ADS`.`Feature_72` AS `Feature_72`, `ADS`.`Feature_73` AS `Feature_73`, `ADS`.`Feature_74` AS `Feature_74`, `ADS`.`Feature_75` AS `Feature_75`, `ADS`.`Feature_76` AS `Feature_76`, `ADS`.`Feature_77` AS `Feature_77`, `ADS`.`Feature_78` AS `Feature_78`, `ADS`.`Feature_79` AS `Feature_79`, `ADS`.`Feature_80` AS `Feature_80`, `ADS`.`Feature_81` AS `Feature_81`, `ADS`.`Feature_82` AS `Feature_82`, `ADS`.`Feature_83` AS `Feature_83`, `ADS`.`Feature_84` AS `Feature_84`, `ADS`.`Feature_85` AS `Feature_85`, `ADS`.`Feature_86` AS `Feature_86`, `ADS`.`Feature_87` AS `Feature_87`, `ADS`.`Feature_88` AS `Feature_88`, `ADS`.`Feature_89` AS `Feature_89`, `ADS`.`Feature_90` AS `Feature_90`, `ADS`.`Feature_91` AS `Feature_91`, `ADS`.`Feature_92` AS `Feature_92`, `ADS`.`Feature_93` AS `Feature_93`, `ADS`.`Feature_94` AS `Feature_94`, `ADS`.`Feature_95` AS `Feature_95`, `ADS`.`Feature_96` AS `Feature_96`, `ADS`.`Feature_97` AS `Feature_97`, `ADS`.`Feature_98` AS `Feature_98`, `ADS`.`Feature_99` AS `Feature_99` 
  FROM `FourClass_100` 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`, keras_input.`Feature_64` AS `Feature_64`, keras_input.`Feature_65` AS `Feature_65`, keras_input.`Feature_66` AS `Feature_66`, keras_input.`Feature_67` AS `Feature_67`, keras_input.`Feature_68` AS `Feature_68`, keras_input.`Feature_69` AS `Feature_69`, keras_input.`Feature_70` AS `Feature_70`, keras_input.`Feature_71` AS `Feature_71`, keras_input.`Feature_72` AS `Feature_72`, keras_input.`Feature_73` AS `Feature_73`, keras_input.`Feature_74` AS `Feature_74`, keras_input.`Feature_75` AS `Feature_75`, keras_input.`Feature_76` AS `Feature_76`, keras_input.`Feature_77` AS `Feature_77`, keras_input.`Feature_78` AS `Feature_78`, keras_input.`Feature_79` AS `Feature_79`, keras_input.`Feature_80` AS `Feature_80`, keras_input.`Feature_81` AS `Feature_81`, keras_input.`Feature_82` AS `Feature_82`, keras_input.`Feature_83` AS `Feature_83`, keras_input.`Feature_84` AS `Feature_84`, keras_input.`Feature_85` AS `Feature_85`, keras_input.`Feature_86` AS `Feature_86`, keras_input.`Feature_87` AS `Feature_87`, keras_input.`Feature_88` AS `Feature_88`, keras_input.`Feature_89` AS `Feature_89`, keras_input.`Feature_90` AS `Feature_90`, keras_input.`Feature_91` AS `Feature_91`, keras_input.`Feature_92` AS `Feature_92`, keras_input.`Feature_93` AS `Feature_93`, keras_input.`Feature_94` AS `Feature_94`, keras_input.`Feature_95` AS `Feature_95`, keras_input.`Feature_96` AS `Feature_96`, keras_input.`Feature_97` AS `Feature_97`, keras_input.`Feature_98` AS `Feature_98`, keras_input.`Feature_99` AS `Feature_99` 
  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`, keras_input_1.`Feature_64` AS `Feature_64`, keras_input_1.`Feature_65` AS `Feature_65`, keras_input_1.`Feature_66` AS `Feature_66`, keras_input_1.`Feature_67` AS `Feature_67`, keras_input_1.`Feature_68` AS `Feature_68`, keras_input_1.`Feature_69` AS `Feature_69`, keras_input_1.`Feature_70` AS `Feature_70`, keras_input_1.`Feature_71` AS `Feature_71`, keras_input_1.`Feature_72` AS `Feature_72`, keras_input_1.`Feature_73` AS `Feature_73`, keras_input_1.`Feature_74` AS `Feature_74`, keras_input_1.`Feature_75` AS `Feature_75`, keras_input_1.`Feature_76` AS `Feature_76`, keras_input_1.`Feature_77` AS `Feature_77`, keras_input_1.`Feature_78` AS `Feature_78`, keras_input_1.`Feature_79` AS `Feature_79`, keras_input_1.`Feature_80` AS `Feature_80`, keras_input_1.`Feature_81` AS `Feature_81`, keras_input_1.`Feature_82` AS `Feature_82`, keras_input_1.`Feature_83` AS `Feature_83`, keras_input_1.`Feature_84` AS `Feature_84`, keras_input_1.`Feature_85` AS `Feature_85`, keras_input_1.`Feature_86` AS `Feature_86`, keras_input_1.`Feature_87` AS `Feature_87`, keras_input_1.`Feature_88` AS `Feature_88`, keras_input_1.`Feature_89` AS `Feature_89`, keras_input_1.`Feature_90` AS `Feature_90`, keras_input_1.`Feature_91` AS `Feature_91`, keras_input_1.`Feature_92` AS `Feature_92`, keras_input_1.`Feature_93` AS `Feature_93`, keras_input_1.`Feature_94` AS `Feature_94`, keras_input_1.`Feature_95` AS `Feature_95`, keras_input_1.`Feature_96` AS `Feature_96`, keras_input_1.`Feature_97` AS `Feature_97`, keras_input_1.`Feature_98` AS `Feature_98`, keras_input_1.`Feature_99` AS `Feature_99` 
  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`, keras_input_1_rn.`Feature_64` AS `Feature_64`, keras_input_1_rn.`Feature_65` AS `Feature_65`, keras_input_1_rn.`Feature_66` AS `Feature_66`, keras_input_1_rn.`Feature_67` AS `Feature_67`, keras_input_1_rn.`Feature_68` AS `Feature_68`, keras_input_1_rn.`Feature_69` AS `Feature_69`, keras_input_1_rn.`Feature_70` AS `Feature_70`, keras_input_1_rn.`Feature_71` AS `Feature_71`, keras_input_1_rn.`Feature_72` AS `Feature_72`, keras_input_1_rn.`Feature_73` AS `Feature_73`, keras_input_1_rn.`Feature_74` AS `Feature_74`, keras_input_1_rn.`Feature_75` AS `Feature_75`, keras_input_1_rn.`Feature_76` AS `Feature_76`, keras_input_1_rn.`Feature_77` AS `Feature_77`, keras_input_1_rn.`Feature_78` AS `Feature_78`, keras_input_1_rn.`Feature_79` AS `Feature_79`, keras_input_1_rn.`Feature_80` AS `Feature_80`, keras_input_1_rn.`Feature_81` AS `Feature_81`, keras_input_1_rn.`Feature_82` AS `Feature_82`, keras_input_1_rn.`Feature_83` AS `Feature_83`, keras_input_1_rn.`Feature_84` AS `Feature_84`, keras_input_1_rn.`Feature_85` AS `Feature_85`, keras_input_1_rn.`Feature_86` AS `Feature_86`, keras_input_1_rn.`Feature_87` AS `Feature_87`, keras_input_1_rn.`Feature_88` AS `Feature_88`, keras_input_1_rn.`Feature_89` AS `Feature_89`, keras_input_1_rn.`Feature_90` AS `Feature_90`, keras_input_1_rn.`Feature_91` AS `Feature_91`, keras_input_1_rn.`Feature_92` AS `Feature_92`, keras_input_1_rn.`Feature_93` AS `Feature_93`, keras_input_1_rn.`Feature_94` AS `Feature_94`, keras_input_1_rn.`Feature_95` AS `Feature_95`, keras_input_1_rn.`Feature_96` AS `Feature_96`, keras_input_1_rn.`Feature_97` AS `Feature_97`, keras_input_1_rn.`Feature_98` AS `Feature_98`, keras_input_1_rn.`Feature_99` AS `Feature_99`, 0.08118845900641283 + 0.009953598922133742 * keras_input_1_rn.`Feature_0` + 0.08303363682435017 * keras_input_1_rn.`Feature_1` + -0.008368095144431991 * keras_input_1_rn.`Feature_2` + 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keras_input_1_rn.`Feature_33` + 0.010712953562497955 * keras_input_1_rn.`Feature_34` + -0.14841489656473147 * keras_input_1_rn.`Feature_35` + -0.1585018131038488 * keras_input_1_rn.`Feature_36` + 0.15945171133812708 * keras_input_1_rn.`Feature_37` + -0.014233618163350788 * keras_input_1_rn.`Feature_38` + -0.07737119354917978 * keras_input_1_rn.`Feature_39` + -0.21000996076723824 * keras_input_1_rn.`Feature_40` + -0.025644598570757293 * keras_input_1_rn.`Feature_41` + 0.04504765953308658 * keras_input_1_rn.`Feature_42` + 0.030368399320903877 * keras_input_1_rn.`Feature_43` + -0.07549864234485919 * keras_input_1_rn.`Feature_44` + 0.026696521934395458 * keras_input_1_rn.`Feature_45` + 0.027190004974335612 * keras_input_1_rn.`Feature_46` + -0.1297404324209661 * keras_input_1_rn.`Feature_47` + 0.14646541675749541 * keras_input_1_rn.`Feature_48` + 0.13956440471481604 * keras_input_1_rn.`Feature_49` + -0.06290434776682484 * keras_input_1_rn.`Feature_50` + -0.09096379057681014 * keras_input_1_rn.`Feature_51` + -0.07855532472081411 * keras_input_1_rn.`Feature_52` + 0.11053015594565245 * keras_input_1_rn.`Feature_53` + -0.07057316415904293 * keras_input_1_rn.`Feature_54` + -0.020349639049112564 * keras_input_1_rn.`Feature_55` + 0.028052780341293066 * keras_input_1_rn.`Feature_56` + -0.05100390131382037 * keras_input_1_rn.`Feature_57` + -0.17201894880506427 * keras_input_1_rn.`Feature_58` + -0.13500787925847485 * keras_input_1_rn.`Feature_59` + -0.16109348873203924 * keras_input_1_rn.`Feature_60` + -0.12203302713888907 * keras_input_1_rn.`Feature_61` + 0.049816600313425 * keras_input_1_rn.`Feature_62` + -0.06705334584423635 * keras_input_1_rn.`Feature_63` + 0.19767295452412778 * keras_input_1_rn.`Feature_64` + -0.007263158257098286 * keras_input_1_rn.`Feature_65` + -0.09732802041623874 * keras_input_1_rn.`Feature_66` + 0.17565207045997153 * keras_input_1_rn.`Feature_67` + -0.02504549268274313 * keras_input_1_rn.`Feature_68` + 0.14867096721655015 * keras_input_1_rn.`Feature_69` + 0.0605119354083553 * keras_input_1_rn.`Feature_70` + -0.049402106640688 * keras_input_1_rn.`Feature_71` + 0.01646456377204266 * keras_input_1_rn.`Feature_72` + 0.14271325729037138 * keras_input_1_rn.`Feature_73` + -0.0027592049572004755 * keras_input_1_rn.`Feature_74` + 0.1415541958745264 * keras_input_1_rn.`Feature_75` + -0.10687057973912098 * keras_input_1_rn.`Feature_76` + -0.08887900902374962 * keras_input_1_rn.`Feature_77` + 0.19911967543554815 * keras_input_1_rn.`Feature_78` + 0.012874166333097523 * keras_input_1_rn.`Feature_79` + -0.15545567618835204 * keras_input_1_rn.`Feature_80` + 0.05161234789832004 * keras_input_1_rn.`Feature_81` + -0.02542548502883647 * keras_input_1_rn.`Feature_82` + -0.2023247074195303 * keras_input_1_rn.`Feature_83` + -0.13989030812517117 * keras_input_1_rn.`Feature_84` + -0.11920620978805231 * keras_input_1_rn.`Feature_85` + -0.18030998618414135 * keras_input_1_rn.`Feature_86` + 0.08490889412022098 * keras_input_1_rn.`Feature_87` + 0.1530544948455928 * keras_input_1_rn.`Feature_88` + 0.17684094047623153 * keras_input_1_rn.`Feature_89` + 0.171602888947801 * keras_input_1_rn.`Feature_90` + -0.1173355438274555 * keras_input_1_rn.`Feature_91` + -0.1257124963206798 * keras_input_1_rn.`Feature_92` + 0.006381430446497206 * keras_input_1_rn.`Feature_93` + 0.13934804594852576 * keras_input_1_rn.`Feature_94` + -0.23366226764058978 * keras_input_1_rn.`Feature_95` + 0.07213858236413573 * keras_input_1_rn.`Feature_96` + -0.14059118117126254 * keras_input_1_rn.`Feature_97` + -0.16336760591560645 * keras_input_1_rn.`Feature_98` + -0.0827980880737102 * keras_input_1_rn.`Feature_99` AS dot_prod_c_1, -0.021527898014933336 + -0.09821898828674462 * keras_input_1_rn.`Feature_0` + 0.31619343037865427 * keras_input_1_rn.`Feature_1` + 0.19947652366829896 * keras_input_1_rn.`Feature_2` + -0.0045670363907687195 * keras_input_1_rn.`Feature_3` + 0.02728207421429715 * keras_input_1_rn.`Feature_4` + 0.24983745990049297 * keras_input_1_rn.`Feature_5` + 0.19133527314976442 * keras_input_1_rn.`Feature_6` + -0.16539348235822388 * keras_input_1_rn.`Feature_7` + -0.0018767900772309926 * keras_input_1_rn.`Feature_8` + -0.046337285533812146 * keras_input_1_rn.`Feature_9` + 0.09244136778158162 * keras_input_1_rn.`Feature_10` + 0.05374713587817731 * keras_input_1_rn.`Feature_11` + 0.1263342806220703 * keras_input_1_rn.`Feature_12` + -0.012835094681719804 * keras_input_1_rn.`Feature_13` + 0.10433992990325375 * keras_input_1_rn.`Feature_14` + -0.002923678152483933 * keras_input_1_rn.`Feature_15` + -0.07284505829884023 * keras_input_1_rn.`Feature_16` + -0.1056741040288035 * keras_input_1_rn.`Feature_17` + 0.16901251327142783 * keras_input_1_rn.`Feature_18` + 0.07869240936862834 * keras_input_1_rn.`Feature_19` + -0.03050388537817607 * keras_input_1_rn.`Feature_20` + 0.2173100723932087 * keras_input_1_rn.`Feature_21` + 0.22892757453195003 * keras_input_1_rn.`Feature_22` + -0.07693716943287751 * keras_input_1_rn.`Feature_23` + 0.00599632361295699 * keras_input_1_rn.`Feature_24` + 0.024153600868828572 * keras_input_1_rn.`Feature_25` + -0.19867666302636566 * keras_input_1_rn.`Feature_26` + 0.09823029156379864 * keras_input_1_rn.`Feature_27` + -0.01721122980667629 * keras_input_1_rn.`Feature_28` + 0.2159835616644051 * keras_input_1_rn.`Feature_29` + -0.10589350474092309 * keras_input_1_rn.`Feature_30` + 0.12000512918193165 * keras_input_1_rn.`Feature_31` + 0.06191400223271362 * keras_input_1_rn.`Feature_32` + 0.09012146553357141 * keras_input_1_rn.`Feature_33` + -0.1344259498491481 * keras_input_1_rn.`Feature_34` + 0.1272511945688265 * keras_input_1_rn.`Feature_35` + -0.16598830185691954 * keras_input_1_rn.`Feature_36` + 0.06900205777764495 * keras_input_1_rn.`Feature_37` + -0.023849408822064194 * keras_input_1_rn.`Feature_38` + -0.21324707519301195 * keras_input_1_rn.`Feature_39` + -0.06684375614345148 * keras_input_1_rn.`Feature_40` + 0.05195792770993776 * keras_input_1_rn.`Feature_41` + 0.11134205308261637 * keras_input_1_rn.`Feature_42` + 0.14680772444346304 * keras_input_1_rn.`Feature_43` + 0.08144126315933643 * keras_input_1_rn.`Feature_44` + -0.06302230101037966 * keras_input_1_rn.`Feature_45` + 0.17832624170354255 * keras_input_1_rn.`Feature_46` + 0.16703602336220227 * keras_input_1_rn.`Feature_47` + 0.017846161981832573 * keras_input_1_rn.`Feature_48` + 0.057768078750217354 * keras_input_1_rn.`Feature_49` + 0.19174664768023683 * keras_input_1_rn.`Feature_50` + -0.17238121164205655 * keras_input_1_rn.`Feature_51` + -0.0113207458881841 * keras_input_1_rn.`Feature_52` + 0.1562767935972808 * keras_input_1_rn.`Feature_53` + -0.09962454088039448 * keras_input_1_rn.`Feature_54` + 0.16342010194211784 * keras_input_1_rn.`Feature_55` + 0.02017769473463005 * keras_input_1_rn.`Feature_56` + -0.06498562342962251 * keras_input_1_rn.`Feature_57` + 0.0010992339185622094 * keras_input_1_rn.`Feature_58` + 0.03704279513706783 * keras_input_1_rn.`Feature_59` + -0.1381735547716284 * keras_input_1_rn.`Feature_60` + -0.18236463426196736 * keras_input_1_rn.`Feature_61` + 0.2147293128781124 * keras_input_1_rn.`Feature_62` + -0.09579547428050456 * keras_input_1_rn.`Feature_63` + -0.12669916123932173 * keras_input_1_rn.`Feature_64` + 0.21769310660439878 * keras_input_1_rn.`Feature_65` + 0.023077243757292528 * keras_input_1_rn.`Feature_66` + 0.25021369069140614 * keras_input_1_rn.`Feature_67` + -0.2837255920337282 * keras_input_1_rn.`Feature_68` + -0.05167718944577024 * keras_input_1_rn.`Feature_69` + -0.21420693705177313 * keras_input_1_rn.`Feature_70` + 0.165296105433481 * keras_input_1_rn.`Feature_71` + 0.01878412174621239 * keras_input_1_rn.`Feature_72` + -0.09069871100735176 * keras_input_1_rn.`Feature_73` + 0.025224808732944262 * keras_input_1_rn.`Feature_74` + 0.18674433689719458 * keras_input_1_rn.`Feature_75` + 0.04423225705257833 * keras_input_1_rn.`Feature_76` + 0.11973932839588003 * keras_input_1_rn.`Feature_77` + 0.13696138371463723 * keras_input_1_rn.`Feature_78` + -0.1360310815652303 * keras_input_1_rn.`Feature_79` + -0.1813002946905193 * keras_input_1_rn.`Feature_80` + 0.08533049158712716 * keras_input_1_rn.`Feature_81` + 0.23431087779571735 * keras_input_1_rn.`Feature_82` + 0.0027801268485341743 * keras_input_1_rn.`Feature_83` + 0.11463835597736509 * keras_input_1_rn.`Feature_84` + 0.028184590015143916 * keras_input_1_rn.`Feature_85` + 0.07859682019881695 * keras_input_1_rn.`Feature_86` + -0.08440914826736315 * keras_input_1_rn.`Feature_87` + -0.1864475082075625 * keras_input_1_rn.`Feature_88` + -0.12827278352719854 * keras_input_1_rn.`Feature_89` + 0.19531895868641933 * keras_input_1_rn.`Feature_90` + -0.17581775856424003 * keras_input_1_rn.`Feature_91` + -0.13582593025478257 * keras_input_1_rn.`Feature_92` + 0.029385301012581005 * keras_input_1_rn.`Feature_93` + -0.15104759594411474 * keras_input_1_rn.`Feature_94` + 0.09984558113797508 * keras_input_1_rn.`Feature_95` + 0.09331767186607806 * keras_input_1_rn.`Feature_96` + 0.20452910836873867 * keras_input_1_rn.`Feature_97` + -0.050784800289555385 * keras_input_1_rn.`Feature_98` + 0.026406281136217447 * keras_input_1_rn.`Feature_99` AS dot_prod_c_2, 0.0532883420283954 + -0.17185248209166368 * keras_input_1_rn.`Feature_0` + -0.21221514171999506 * keras_input_1_rn.`Feature_1` + 0.14219591109968213 * keras_input_1_rn.`Feature_2` + -0.1964074058836297 * keras_input_1_rn.`Feature_3` + -0.0780904656070732 * keras_input_1_rn.`Feature_4` + 0.12332642799355294 * keras_input_1_rn.`Feature_5` + -0.10753500404039691 * keras_input_1_rn.`Feature_6` + -0.1324172262389666 * keras_input_1_rn.`Feature_7` + 0.06628706993678643 * keras_input_1_rn.`Feature_8` + 0.08097601928560541 * keras_input_1_rn.`Feature_9` + 0.19892638255349687 * keras_input_1_rn.`Feature_10` + 0.04058707450097117 * keras_input_1_rn.`Feature_11` + -0.07081460738567368 * keras_input_1_rn.`Feature_12` + 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* keras_input_1_rn.`Feature_31` + 0.18411482570524712 * keras_input_1_rn.`Feature_32` + -0.17308970568650353 * keras_input_1_rn.`Feature_33` + 0.14359060563905485 * keras_input_1_rn.`Feature_34` + -0.09521836820207769 * keras_input_1_rn.`Feature_35` + 0.07166941298158537 * keras_input_1_rn.`Feature_36` + 0.11815620474349124 * keras_input_1_rn.`Feature_37` + 0.192145443900667 * keras_input_1_rn.`Feature_38` + -0.22738841865196927 * keras_input_1_rn.`Feature_39` + -0.23100231547426714 * keras_input_1_rn.`Feature_40` + -0.08906712615627649 * keras_input_1_rn.`Feature_41` + 0.1927423481628141 * keras_input_1_rn.`Feature_42` + 0.062320848533505016 * keras_input_1_rn.`Feature_43` + -0.22148130725546805 * keras_input_1_rn.`Feature_44` + -0.19596180121945145 * keras_input_1_rn.`Feature_45` + -0.10913784385267888 * keras_input_1_rn.`Feature_46` + -0.08479075448571786 * keras_input_1_rn.`Feature_47` + -0.08607926928543734 * keras_input_1_rn.`Feature_48` + -0.0884514336648944 * 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keras_input_1_rn.`Feature_67` + 0.027551973141405738 * keras_input_1_rn.`Feature_68` + 0.17948969683493676 * keras_input_1_rn.`Feature_69` + 0.07971621719198067 * keras_input_1_rn.`Feature_70` + -0.09891826885066757 * keras_input_1_rn.`Feature_71` + -0.05234892083077249 * keras_input_1_rn.`Feature_72` + -0.17452931814183265 * keras_input_1_rn.`Feature_73` + 0.18579547562597515 * keras_input_1_rn.`Feature_74` + 0.08505134243336862 * keras_input_1_rn.`Feature_75` + 0.1860746481435493 * keras_input_1_rn.`Feature_76` + 0.24902059995563336 * keras_input_1_rn.`Feature_77` + 0.22512613491446454 * keras_input_1_rn.`Feature_78` + -0.030944754908491447 * keras_input_1_rn.`Feature_79` + -0.1304913269732366 * keras_input_1_rn.`Feature_80` + -0.16101180486358482 * keras_input_1_rn.`Feature_81` + -0.2203160210684266 * keras_input_1_rn.`Feature_82` + 0.043491256200896666 * keras_input_1_rn.`Feature_83` + 0.18282460925192617 * keras_input_1_rn.`Feature_84` + -0.18324417037825966 * keras_input_1_rn.`Feature_85` + -0.19518784416324442 * keras_input_1_rn.`Feature_86` + -0.17464235892614258 * keras_input_1_rn.`Feature_87` + 0.07795398836858598 * keras_input_1_rn.`Feature_88` + -0.23129173754416676 * keras_input_1_rn.`Feature_89` + -0.0450883410992612 * keras_input_1_rn.`Feature_90` + 0.13682545405287191 * keras_input_1_rn.`Feature_91` + 0.17745631628817207 * keras_input_1_rn.`Feature_92` + 0.21833607348755601 * keras_input_1_rn.`Feature_93` + -0.017343047344763336 * keras_input_1_rn.`Feature_94` + -0.16698017552284658 * keras_input_1_rn.`Feature_95` + -0.22023843241144211 * keras_input_1_rn.`Feature_96` + 0.022394499274379153 * keras_input_1_rn.`Feature_97` + 0.009549593308386816 * keras_input_1_rn.`Feature_98` + -0.1984839275096453 * keras_input_1_rn.`Feature_99` AS dot_prod_c_3, -0.09867027480986512 + 0.01265128917694325 * keras_input_1_rn.`Feature_0` + -0.13677492315101558 * keras_input_1_rn.`Feature_1` + -0.13465914170153065 * keras_input_1_rn.`Feature_2` + 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keras_input_1_rn.`Feature_21` + -0.15831781011923804 * keras_input_1_rn.`Feature_22` + -0.10589921220920263 * keras_input_1_rn.`Feature_23` + -0.0014841429979246974 * keras_input_1_rn.`Feature_24` + 0.16183155911068087 * keras_input_1_rn.`Feature_25` + 0.06311591339358581 * keras_input_1_rn.`Feature_26` + -0.294799458816161 * keras_input_1_rn.`Feature_27` + -0.10778911376324021 * keras_input_1_rn.`Feature_28` + 0.049695609770139634 * keras_input_1_rn.`Feature_29` + 0.1898562796797799 * keras_input_1_rn.`Feature_30` + 0.11990247347268264 * keras_input_1_rn.`Feature_31` + 0.19581633243031946 * keras_input_1_rn.`Feature_32` + 0.12743550855680255 * keras_input_1_rn.`Feature_33` + 0.04676767126763226 * keras_input_1_rn.`Feature_34` + -0.17576253613469495 * keras_input_1_rn.`Feature_35` + -0.17553703459947206 * keras_input_1_rn.`Feature_36` + 0.08390514925513952 * keras_input_1_rn.`Feature_37` + -0.15000797220081774 * keras_input_1_rn.`Feature_38` + 0.13602089043358745 * keras_input_1_rn.`Feature_39` + -0.1635418641310522 * keras_input_1_rn.`Feature_40` + -0.0066673708342473974 * keras_input_1_rn.`Feature_41` + 0.06436921461297322 * keras_input_1_rn.`Feature_42` + -0.12284151490531926 * keras_input_1_rn.`Feature_43` + 0.009300774770626156 * keras_input_1_rn.`Feature_44` + -0.010846906952649745 * keras_input_1_rn.`Feature_45` + -0.036533425467461814 * keras_input_1_rn.`Feature_46` + 0.14452095716897806 * keras_input_1_rn.`Feature_47` + -0.10878496590745748 * keras_input_1_rn.`Feature_48` + 0.09056472680260803 * keras_input_1_rn.`Feature_49` + 0.11112188162495705 * keras_input_1_rn.`Feature_50` + 0.009147752736203989 * keras_input_1_rn.`Feature_51` + -0.20994725829979777 * keras_input_1_rn.`Feature_52` + -0.08176737290122225 * keras_input_1_rn.`Feature_53` + 0.07804473792218428 * keras_input_1_rn.`Feature_54` + -0.1357368873600076 * keras_input_1_rn.`Feature_55` + 0.10425175706541642 * keras_input_1_rn.`Feature_56` + 0.15739886985292514 * 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keras_input_1_rn.`Feature_75` + 0.04074145512935812 * keras_input_1_rn.`Feature_76` + 0.2035792683551604 * keras_input_1_rn.`Feature_77` + -0.07735116333837351 * keras_input_1_rn.`Feature_78` + -0.011554481556170672 * keras_input_1_rn.`Feature_79` + 0.17959909718099948 * keras_input_1_rn.`Feature_80` + -0.16006442571943785 * keras_input_1_rn.`Feature_81` + 0.0861549595894655 * keras_input_1_rn.`Feature_82` + 0.0337849375064886 * keras_input_1_rn.`Feature_83` + 0.20910595137587218 * keras_input_1_rn.`Feature_84` + -0.19612127173340974 * keras_input_1_rn.`Feature_85` + 0.10026348942484459 * keras_input_1_rn.`Feature_86` + -0.1591882706576485 * keras_input_1_rn.`Feature_87` + -0.06515017462090926 * keras_input_1_rn.`Feature_88` + 0.20492065104823304 * keras_input_1_rn.`Feature_89` + -0.18432228914445917 * keras_input_1_rn.`Feature_90` + -0.06678814706731885 * keras_input_1_rn.`Feature_91` + -0.02284123036872939 * keras_input_1_rn.`Feature_92` + 0.0727473576552983 * keras_input_1_rn.`Feature_93` + 0.005861341525104075 * keras_input_1_rn.`Feature_94` + 0.07844815636823302 * keras_input_1_rn.`Feature_95` + -0.24864808752457873 * keras_input_1_rn.`Feature_96` + -0.13520106746555674 * keras_input_1_rn.`Feature_97` + 0.0032954457574091304 * keras_input_1_rn.`Feature_98` + 0.08488202735795933 * keras_input_1_rn.`Feature_99` AS dot_prod_o_1, -0.05635435962803564 + -0.14514183577295414 * keras_input_1_rn.`Feature_0` + 0.0581354421779043 * keras_input_1_rn.`Feature_1` + -0.0768971446666887 * keras_input_1_rn.`Feature_2` + -0.24522612927289686 * keras_input_1_rn.`Feature_3` + -0.015522721036070839 * keras_input_1_rn.`Feature_4` + -0.1576685838493619 * keras_input_1_rn.`Feature_5` + 0.25132095806510346 * keras_input_1_rn.`Feature_6` + -0.2484906070441999 * keras_input_1_rn.`Feature_7` + -0.0065247478263938475 * keras_input_1_rn.`Feature_8` + 0.15191741946296117 * keras_input_1_rn.`Feature_9` + 0.20729686442162817 * keras_input_1_rn.`Feature_10` + -0.1818000128853885 * keras_input_1_rn.`Feature_11` + -0.030933045308506945 * keras_input_1_rn.`Feature_12` + 0.03716607880721164 * keras_input_1_rn.`Feature_13` + -0.1427893033148063 * keras_input_1_rn.`Feature_14` + 0.23988637288254938 * keras_input_1_rn.`Feature_15` + -0.0723894779227163 * keras_input_1_rn.`Feature_16` + 0.06481822268182831 * keras_input_1_rn.`Feature_17` + 0.033428940247544635 * keras_input_1_rn.`Feature_18` + 0.2013082190712567 * keras_input_1_rn.`Feature_19` + 0.02398464347382304 * keras_input_1_rn.`Feature_20` + -0.2637974715629671 * keras_input_1_rn.`Feature_21` + 0.08719802692944152 * keras_input_1_rn.`Feature_22` + -0.11551047555663067 * keras_input_1_rn.`Feature_23` + 0.1410770686125635 * keras_input_1_rn.`Feature_24` + 0.05479427123194898 * keras_input_1_rn.`Feature_25` + -0.12145963743219575 * keras_input_1_rn.`Feature_26` + -0.12150718821314946 * keras_input_1_rn.`Feature_27` + 0.025916565884498265 * keras_input_1_rn.`Feature_28` + 0.15002475032561993 * keras_input_1_rn.`Feature_29` + 0.03630475026986305 * keras_input_1_rn.`Feature_30` + -0.09600133430347974 * keras_input_1_rn.`Feature_31` + 0.19809658611215644 * keras_input_1_rn.`Feature_32` + 0.13847906508335284 * keras_input_1_rn.`Feature_33` + -0.1261043039008152 * keras_input_1_rn.`Feature_34` + 0.04393247943774319 * keras_input_1_rn.`Feature_35` + -0.055332157847419315 * keras_input_1_rn.`Feature_36` + 0.14034980095293315 * keras_input_1_rn.`Feature_37` + 0.2773319978228591 * keras_input_1_rn.`Feature_38` + 0.11830554927831691 * keras_input_1_rn.`Feature_39` + -0.1132035597497437 * keras_input_1_rn.`Feature_40` + -0.019362596254500063 * keras_input_1_rn.`Feature_41` + -0.020923989177260856 * keras_input_1_rn.`Feature_42` + 0.07694832571856247 * keras_input_1_rn.`Feature_43` + -0.06024194419845594 * keras_input_1_rn.`Feature_44` + -0.019085781075329593 * keras_input_1_rn.`Feature_45` + 0.09742232595799542 * keras_input_1_rn.`Feature_46` + 0.1598809579342385 * keras_input_1_rn.`Feature_47` + 0.0037236129600565805 * keras_input_1_rn.`Feature_48` + 0.09303545705492273 * keras_input_1_rn.`Feature_49` + 0.08651392748165597 * keras_input_1_rn.`Feature_50` + 0.03675748674242848 * keras_input_1_rn.`Feature_51` + -0.04512704051927858 * keras_input_1_rn.`Feature_52` + -0.10406463551469743 * keras_input_1_rn.`Feature_53` + 0.06180728630486593 * keras_input_1_rn.`Feature_54` + 0.1591695175643361 * keras_input_1_rn.`Feature_55` + 0.0022301991418592335 * keras_input_1_rn.`Feature_56` + 0.12286671869009756 * keras_input_1_rn.`Feature_57` + -0.05738942234578545 * keras_input_1_rn.`Feature_58` + 0.05844998864972908 * keras_input_1_rn.`Feature_59` + 0.13070190083525715 * keras_input_1_rn.`Feature_60` + -0.015464703115554382 * keras_input_1_rn.`Feature_61` + -0.11797807455669054 * keras_input_1_rn.`Feature_62` + 0.12720842343434635 * keras_input_1_rn.`Feature_63` + 0.07180037882730397 * keras_input_1_rn.`Feature_64` + -0.23533144784540375 * keras_input_1_rn.`Feature_65` + 0.07524800120626342 * keras_input_1_rn.`Feature_66` + 0.13790886545116365 * keras_input_1_rn.`Feature_67` + -0.10918145349446585 * keras_input_1_rn.`Feature_68` + -0.19997312788598795 * keras_input_1_rn.`Feature_69` + 0.05895477557952195 * keras_input_1_rn.`Feature_70` + 0.12080720007687133 * keras_input_1_rn.`Feature_71` + 0.26656260927126035 * keras_input_1_rn.`Feature_72` + 0.03821127610618916 * keras_input_1_rn.`Feature_73` + -0.13187713206349183 * keras_input_1_rn.`Feature_74` + 0.20461552586815313 * keras_input_1_rn.`Feature_75` + 0.0971497781302976 * keras_input_1_rn.`Feature_76` + -0.15413375892997264 * keras_input_1_rn.`Feature_77` + 0.1268828064892017 * keras_input_1_rn.`Feature_78` + -0.2048022209213064 * keras_input_1_rn.`Feature_79` + -0.07044150341737745 * keras_input_1_rn.`Feature_80` + 0.1532211744627825 * keras_input_1_rn.`Feature_81` + -0.0002931132605465269 * keras_input_1_rn.`Feature_82` + 0.09777103130280818 * keras_input_1_rn.`Feature_83` + -0.04024628870501176 * keras_input_1_rn.`Feature_84` + 0.14060808987388607 * keras_input_1_rn.`Feature_85` + -0.18890799676195796 * keras_input_1_rn.`Feature_86` + 0.18673828151018423 * keras_input_1_rn.`Feature_87` + 0.20587880228879124 * keras_input_1_rn.`Feature_88` + 0.13767185549686722 * keras_input_1_rn.`Feature_89` + -0.07587942694480898 * keras_input_1_rn.`Feature_90` + -0.06585293217926415 * keras_input_1_rn.`Feature_91` + -0.20602673344037814 * keras_input_1_rn.`Feature_92` + 0.1407820132869043 * keras_input_1_rn.`Feature_93` + -0.010085529375245803 * keras_input_1_rn.`Feature_94` + 0.1200329597942493 * keras_input_1_rn.`Feature_95` + -0.12921541279845883 * keras_input_1_rn.`Feature_96` + 0.20579547753901856 * keras_input_1_rn.`Feature_97` + -0.1827037088149406 * keras_input_1_rn.`Feature_98` + -0.2280819210673368 * keras_input_1_rn.`Feature_99` AS dot_prod_o_2, -0.1341299585867478 + -0.14563074409101118 * keras_input_1_rn.`Feature_0` + -0.09263593260449177 * keras_input_1_rn.`Feature_1` + 0.01823480350579738 * keras_input_1_rn.`Feature_2` + 0.20639420864275854 * keras_input_1_rn.`Feature_3` + 0.06911106904900012 * keras_input_1_rn.`Feature_4` + -0.27775585942339337 * keras_input_1_rn.`Feature_5` + -0.16465791474116567 * keras_input_1_rn.`Feature_6` + 0.10389998714489064 * keras_input_1_rn.`Feature_7` + 0.10876910415787507 * keras_input_1_rn.`Feature_8` + -0.20163773844484803 * keras_input_1_rn.`Feature_9` + 0.257814731331099 * keras_input_1_rn.`Feature_10` + 0.023720390252523066 * keras_input_1_rn.`Feature_11` + 0.19657430730094588 * keras_input_1_rn.`Feature_12` + 0.00699816371393838 * keras_input_1_rn.`Feature_13` + 0.10949818777389887 * keras_input_1_rn.`Feature_14` + -0.06670460031685908 * keras_input_1_rn.`Feature_15` + 0.13039938167796933 * keras_input_1_rn.`Feature_16` + -0.14792499357394515 * keras_input_1_rn.`Feature_17` + -0.03525408988582395 * keras_input_1_rn.`Feature_18` + 0.06894579527037253 * keras_input_1_rn.`Feature_19` + 0.02015405608503898 * keras_input_1_rn.`Feature_20` + -0.20305596167451756 * keras_input_1_rn.`Feature_21` + 0.011521715828606486 * keras_input_1_rn.`Feature_22` + -0.09182866477165495 * keras_input_1_rn.`Feature_23` + 0.1803401627117966 * keras_input_1_rn.`Feature_24` + 0.026589216091315978 * keras_input_1_rn.`Feature_25` + 0.011319225277456066 * keras_input_1_rn.`Feature_26` + 0.06198753342969507 * keras_input_1_rn.`Feature_27` + -0.16684064912402954 * keras_input_1_rn.`Feature_28` + -0.0822949033355358 * keras_input_1_rn.`Feature_29` + 0.1534976907086217 * keras_input_1_rn.`Feature_30` + -0.05219845886581141 * keras_input_1_rn.`Feature_31` + 0.0769691182646279 * keras_input_1_rn.`Feature_32` + 0.12422167366228055 * keras_input_1_rn.`Feature_33` + -0.057168113866183416 * keras_input_1_rn.`Feature_34` + -0.12231949157459501 * keras_input_1_rn.`Feature_35` + -0.03984094156869108 * keras_input_1_rn.`Feature_36` + 0.004168358500074252 * keras_input_1_rn.`Feature_37` + 0.17847894547335078 * keras_input_1_rn.`Feature_38` + 0.21070531059436223 * keras_input_1_rn.`Feature_39` + 0.02778429463504667 * keras_input_1_rn.`Feature_40` + 0.05052808396104247 * keras_input_1_rn.`Feature_41` + -0.0977247374932462 * keras_input_1_rn.`Feature_42` + -0.03223755409601317 * keras_input_1_rn.`Feature_43` + -0.047148701672635705 * keras_input_1_rn.`Feature_44` + 0.036802823526279276 * keras_input_1_rn.`Feature_45` + 0.10780771834625341 * keras_input_1_rn.`Feature_46` + 0.07431708474383934 * keras_input_1_rn.`Feature_47` + -0.08261883477404271 * keras_input_1_rn.`Feature_48` + 0.19032675403000204 * keras_input_1_rn.`Feature_49` + 0.2637005196880738 * keras_input_1_rn.`Feature_50` + 0.1205730802705196 * keras_input_1_rn.`Feature_51` + 0.10906601805039778 * keras_input_1_rn.`Feature_52` + -0.14910659797932843 * keras_input_1_rn.`Feature_53` + 0.15307906088287782 * keras_input_1_rn.`Feature_54` + 0.10057259865002462 * keras_input_1_rn.`Feature_55` + 0.062296823552759485 * keras_input_1_rn.`Feature_56` + -0.05025919614648468 * keras_input_1_rn.`Feature_57` + 0.05883704814751639 * keras_input_1_rn.`Feature_58` + -0.15228707873061736 * keras_input_1_rn.`Feature_59` + 0.18492018775468178 * keras_input_1_rn.`Feature_60` + 0.1928979872874269 * keras_input_1_rn.`Feature_61` + 0.02160374752527088 * keras_input_1_rn.`Feature_62` + 0.15833816178295715 * keras_input_1_rn.`Feature_63` + 0.21183955567234433 * keras_input_1_rn.`Feature_64` + -0.12985020436502245 * keras_input_1_rn.`Feature_65` + 0.23622903000543327 * keras_input_1_rn.`Feature_66` + 0.028839300026448935 * keras_input_1_rn.`Feature_67` + 0.009803128026590545 * keras_input_1_rn.`Feature_68` + 0.10406331865453163 * keras_input_1_rn.`Feature_69` + -0.1131457552348046 * keras_input_1_rn.`Feature_70` + -0.010539633705696405 * keras_input_1_rn.`Feature_71` + -0.14692100242980516 * keras_input_1_rn.`Feature_72` + -0.21171055522793422 * keras_input_1_rn.`Feature_73` + -0.06121186162720573 * keras_input_1_rn.`Feature_74` + -0.027344781380544914 * keras_input_1_rn.`Feature_75` + -0.03603282487036509 * keras_input_1_rn.`Feature_76` + -0.09581282538082253 * keras_input_1_rn.`Feature_77` + 0.20401643188233337 * keras_input_1_rn.`Feature_78` + -0.06444347056028857 * keras_input_1_rn.`Feature_79` + 0.041681156219609286 * keras_input_1_rn.`Feature_80` + -0.23776479817846904 * keras_input_1_rn.`Feature_81` + -0.2128888243444293 * keras_input_1_rn.`Feature_82` + -0.14966111643927021 * keras_input_1_rn.`Feature_83` + 0.2332721965775456 * keras_input_1_rn.`Feature_84` + -0.0208240032693143 * keras_input_1_rn.`Feature_85` + 0.188068311875443 * keras_input_1_rn.`Feature_86` + -0.10013654928105767 * keras_input_1_rn.`Feature_87` + -0.12348110884859509 * keras_input_1_rn.`Feature_88` + -0.17558700277890127 * keras_input_1_rn.`Feature_89` + -0.012798105795816842 * keras_input_1_rn.`Feature_90` + 0.043666991229185305 * keras_input_1_rn.`Feature_91` + 0.00896282171138752 * keras_input_1_rn.`Feature_92` + 0.16784193363535915 * keras_input_1_rn.`Feature_93` + 0.10318130890424324 * keras_input_1_rn.`Feature_94` + -0.11397717767111706 * keras_input_1_rn.`Feature_95` + -0.12965858030429214 * keras_input_1_rn.`Feature_96` + 0.11757016133028679 * keras_input_1_rn.`Feature_97` + 0.1918872500314142 * keras_input_1_rn.`Feature_98` + -0.17105484175794616 * keras_input_1_rn.`Feature_99` 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) AS `PreviousState_1`, CAST(0.0 AS DOUBLE) AS `PreviousState_2`, CAST(0.0 AS DOUBLE) AS `PreviousState_3`, CAST(0.0 AS DOUBLE) AS `PreviousState_4`, CAST(0.0 AS DOUBLE) AS `PreviousState_5`, CAST(0.0 AS DOUBLE) AS `PreviousState_6`, greatest(lstm_input_kernel_bias.dot_prod_o_1 + 0.0, 0) * ((exp(greatest(lstm_input_kernel_bias.dot_prod_i_1 + 0.0, 0) * ((exp(lstm_input_kernel_bias.dot_prod_c_1 + 0.0) - exp(-(lstm_input_kernel_bias.dot_prod_c_1 + 0.0))) / (exp(lstm_input_kernel_bias.dot_prod_c_1 + 0.0) + exp(-(lstm_input_kernel_bias.dot_prod_c_1 + 0.0)))) + greatest(lstm_input_kernel_bias.dot_prod_f_1 + 0.0, 0) * 0.0) - exp(-(greatest(lstm_input_kernel_bias.dot_prod_i_1 + 0.0, 0) * ((exp(lstm_input_kernel_bias.dot_prod_c_1 + 0.0) - exp(-(lstm_input_kernel_bias.dot_prod_c_1 + 0.0))) / (exp(lstm_input_kernel_bias.dot_prod_c_1 + 0.0) + exp(-(lstm_input_kernel_bias.dot_prod_c_1 + 0.0)))) + greatest(lstm_input_kernel_bias.dot_prod_f_1 + 0.0, 0) * 0.0))) / (exp(greatest(lstm_input_kernel_bias.dot_prod_i_1 + 0.0, 0) * ((exp(lstm_input_kernel_bias.dot_prod_c_1 + 0.0) - exp(-(lstm_input_kernel_bias.dot_prod_c_1 + 0.0))) / (exp(lstm_input_kernel_bias.dot_prod_c_1 + 0.0) + exp(-(lstm_input_kernel_bias.dot_prod_c_1 + 0.0)))) + greatest(lstm_input_kernel_bias.dot_prod_f_1 + 0.0, 0) * 0.0) + exp(-(greatest(lstm_input_kernel_bias.dot_prod_i_1 + 0.0, 0) * ((exp(lstm_input_kernel_bias.dot_prod_c_1 + 0.0) - exp(-(lstm_input_kernel_bias.dot_prod_c_1 + 0.0))) / (exp(lstm_input_kernel_bias.dot_prod_c_1 + 0.0) + exp(-(lstm_input_kernel_bias.dot_prod_c_1 + 0.0)))) + greatest(lstm_input_kernel_bias.dot_prod_f_1 + 0.0, 0) * 0.0)))) AS `State_1`, greatest(lstm_input_kernel_bias.dot_prod_o_2 + 0.0, 0) * ((exp(greatest(lstm_input_kernel_bias.dot_prod_i_2 + 0.0, 0) * ((exp(lstm_input_kernel_bias.dot_prod_c_2 + 0.0) - exp(-(lstm_input_kernel_bias.dot_prod_c_2 + 0.0))) / (exp(lstm_input_kernel_bias.dot_prod_c_2 + 0.0) + exp(-(lstm_input_kernel_bias.dot_prod_c_2 + 0.0)))) + greatest(lstm_input_kernel_bias.dot_prod_f_2 + 0.0, 0) * 0.0) - exp(-(greatest(lstm_input_kernel_bias.dot_prod_i_2 + 0.0, 0) * ((exp(lstm_input_kernel_bias.dot_prod_c_2 + 0.0) - exp(-(lstm_input_kernel_bias.dot_prod_c_2 + 0.0))) / (exp(lstm_input_kernel_bias.dot_prod_c_2 + 0.0) + exp(-(lstm_input_kernel_bias.dot_prod_c_2 + 0.0)))) + greatest(lstm_input_kernel_bias.dot_prod_f_2 + 0.0, 0) * 0.0))) / (exp(greatest(lstm_input_kernel_bias.dot_prod_i_2 + 0.0, 0) * ((exp(lstm_input_kernel_bias.dot_prod_c_2 + 0.0) - exp(-(lstm_input_kernel_bias.dot_prod_c_2 + 0.0))) / (exp(lstm_input_kernel_bias.dot_prod_c_2 + 0.0) + exp(-(lstm_input_kernel_bias.dot_prod_c_2 + 0.0)))) + greatest(lstm_input_kernel_bias.dot_prod_f_2 + 0.0, 0) * 0.0) + exp(-(greatest(lstm_input_kernel_bias.dot_prod_i_2 + 0.0, 0) * ((exp(lstm_input_kernel_bias.dot_prod_c_2 + 0.0) - exp(-(lstm_input_kernel_bias.dot_prod_c_2 + 0.0))) / (exp(lstm_input_kernel_bias.dot_prod_c_2 + 0.0) + exp(-(lstm_input_kernel_bias.dot_prod_c_2 + 0.0)))) + greatest(lstm_input_kernel_bias.dot_prod_f_2 + 0.0, 0) * 0.0)))) AS `State_2`, greatest(lstm_input_kernel_bias.dot_prod_o_3 + 0.0, 0) * ((exp(greatest(lstm_input_kernel_bias.dot_prod_i_3 + 0.0, 0) * ((exp(lstm_input_kernel_bias.dot_prod_c_3 + 0.0) - exp(-(lstm_input_kernel_bias.dot_prod_c_3 + 0.0))) / (exp(lstm_input_kernel_bias.dot_prod_c_3 + 0.0) + exp(-(lstm_input_kernel_bias.dot_prod_c_3 + 0.0)))) + greatest(lstm_input_kernel_bias.dot_prod_f_3 + 0.0, 0) * 0.0) - exp(-(greatest(lstm_input_kernel_bias.dot_prod_i_3 + 0.0, 0) * ((exp(lstm_input_kernel_bias.dot_prod_c_3 + 0.0) - exp(-(lstm_input_kernel_bias.dot_prod_c_3 + 0.0))) / (exp(lstm_input_kernel_bias.dot_prod_c_3 + 0.0) + exp(-(lstm_input_kernel_bias.dot_prod_c_3 + 0.0)))) + greatest(lstm_input_kernel_bias.dot_prod_f_3 + 0.0, 0) * 0.0))) / (exp(greatest(lstm_input_kernel_bias.dot_prod_i_3 + 0.0, 0) * ((exp(lstm_input_kernel_bias.dot_prod_c_3 + 0.0) - exp(-(lstm_input_kernel_bias.dot_prod_c_3 + 0.0))) / (exp(lstm_input_kernel_bias.dot_prod_c_3 + 0.0) + exp(-(lstm_input_kernel_bias.dot_prod_c_3 + 0.0)))) + greatest(lstm_input_kernel_bias.dot_prod_f_3 + 0.0, 0) * 0.0) + exp(-(greatest(lstm_input_kernel_bias.dot_prod_i_3 + 0.0, 0) * ((exp(lstm_input_kernel_bias.dot_prod_c_3 + 0.0) - exp(-(lstm_input_kernel_bias.dot_prod_c_3 + 0.0))) / (exp(lstm_input_kernel_bias.dot_prod_c_3 + 0.0) + exp(-(lstm_input_kernel_bias.dot_prod_c_3 + 0.0)))) + greatest(lstm_input_kernel_bias.dot_prod_f_3 + 0.0, 0) * 0.0)))) AS `State_3`, greatest(lstm_input_kernel_bias.dot_prod_i_1 + 0.0, 0) * ((exp(lstm_input_kernel_bias.dot_prod_c_1 + 0.0) - exp(-(lstm_input_kernel_bias.dot_prod_c_1 + 0.0))) / (exp(lstm_input_kernel_bias.dot_prod_c_1 + 0.0) + exp(-(lstm_input_kernel_bias.dot_prod_c_1 + 0.0)))) + greatest(lstm_input_kernel_bias.dot_prod_f_1 + 0.0, 0) * 0.0 AS `State_4`, greatest(lstm_input_kernel_bias.dot_prod_i_2 + 0.0, 0) * ((exp(lstm_input_kernel_bias.dot_prod_c_2 + 0.0) - exp(-(lstm_input_kernel_bias.dot_prod_c_2 + 0.0))) / (exp(lstm_input_kernel_bias.dot_prod_c_2 + 0.0) + exp(-(lstm_input_kernel_bias.dot_prod_c_2 + 0.0)))) + greatest(lstm_input_kernel_bias.dot_prod_f_2 + 0.0, 0) * 0.0 AS `State_5`, greatest(lstm_input_kernel_bias.dot_prod_i_3 + 0.0, 0) * ((exp(lstm_input_kernel_bias.dot_prod_c_3 + 0.0) - exp(-(lstm_input_kernel_bias.dot_prod_c_3 + 0.0))) / (exp(lstm_input_kernel_bias.dot_prod_c_3 + 0.0) + exp(-(lstm_input_kernel_bias.dot_prod_c_3 + 0.0)))) + greatest(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`, greatest(lstm_input_kernel_bias.dot_prod_o_1 + -0.10409186334013232 * rnn_cte_lstm_1.`State_1` + 0.06976254545075214 * rnn_cte_lstm_1.`State_2` + -0.10736437589139215 * rnn_cte_lstm_1.`State_3`, 0) * ((exp(greatest(lstm_input_kernel_bias.dot_prod_i_1 + 0.09160992365240425 * rnn_cte_lstm_1.`State_1` + 0.24971253146357347 * rnn_cte_lstm_1.`State_2` + -0.06855082080993892 * rnn_cte_lstm_1.`State_3`, 0) * ((exp(lstm_input_kernel_bias.dot_prod_c_1 + 0.03177660698427795 * rnn_cte_lstm_1.`State_1` + -0.028722086224731568 * rnn_cte_lstm_1.`State_2` + 0.4968708561774392 * rnn_cte_lstm_1.`State_3`) - exp(-(lstm_input_kernel_bias.dot_prod_c_1 + 0.03177660698427795 * rnn_cte_lstm_1.`State_1` + -0.028722086224731568 * rnn_cte_lstm_1.`State_2` + 0.4968708561774392 * rnn_cte_lstm_1.`State_3`))) / (exp(lstm_input_kernel_bias.dot_prod_c_1 + 0.03177660698427795 * rnn_cte_lstm_1.`State_1` + -0.028722086224731568 * rnn_cte_lstm_1.`State_2` + 0.4968708561774392 * rnn_cte_lstm_1.`State_3`) + exp(-(lstm_input_kernel_bias.dot_prod_c_1 + 0.03177660698427795 * rnn_cte_lstm_1.`State_1` + -0.028722086224731568 * rnn_cte_lstm_1.`State_2` + 0.4968708561774392 * rnn_cte_lstm_1.`State_3`)))) + greatest(lstm_input_kernel_bias.dot_prod_f_1 + -0.41132312467377813 * rnn_cte_lstm_1.`State_1` + 0.15872902889329948 * rnn_cte_lstm_1.`State_2` + -0.030968399142072722 * rnn_cte_lstm_1.`State_3`, 0) * rnn_cte_lstm_1.`State_4`) - exp(-(greatest(lstm_input_kernel_bias.dot_prod_i_1 + 0.09160992365240425 * rnn_cte_lstm_1.`State_1` + 0.24971253146357347 * rnn_cte_lstm_1.`State_2` + -0.06855082080993892 * rnn_cte_lstm_1.`State_3`, 0) * ((exp(lstm_input_kernel_bias.dot_prod_c_1 + 0.03177660698427795 * rnn_cte_lstm_1.`State_1` + -0.028722086224731568 * rnn_cte_lstm_1.`State_2` + 0.4968708561774392 * rnn_cte_lstm_1.`State_3`) - exp(-(lstm_input_kernel_bias.dot_prod_c_1 + 0.03177660698427795 * rnn_cte_lstm_1.`State_1` + -0.028722086224731568 * rnn_cte_lstm_1.`State_2` + 0.4968708561774392 * rnn_cte_lstm_1.`State_3`))) / (exp(lstm_input_kernel_bias.dot_prod_c_1 + 0.03177660698427795 * rnn_cte_lstm_1.`State_1` + -0.028722086224731568 * rnn_cte_lstm_1.`State_2` + 0.4968708561774392 * rnn_cte_lstm_1.`State_3`) + exp(-(lstm_input_kernel_bias.dot_prod_c_1 + 0.03177660698427795 * rnn_cte_lstm_1.`State_1` + -0.028722086224731568 * rnn_cte_lstm_1.`State_2` + 0.4968708561774392 * rnn_cte_lstm_1.`State_3`)))) + greatest(lstm_input_kernel_bias.dot_prod_f_1 + -0.41132312467377813 * rnn_cte_lstm_1.`State_1` + 0.15872902889329948 * rnn_cte_lstm_1.`State_2` + -0.030968399142072722 * rnn_cte_lstm_1.`State_3`, 0) * rnn_cte_lstm_1.`State_4`))) / (exp(greatest(lstm_input_kernel_bias.dot_prod_i_1 + 0.09160992365240425 * rnn_cte_lstm_1.`State_1` + 0.24971253146357347 * rnn_cte_lstm_1.`State_2` + -0.06855082080993892 * rnn_cte_lstm_1.`State_3`, 0) * ((exp(lstm_input_kernel_bias.dot_prod_c_1 + 0.03177660698427795 * rnn_cte_lstm_1.`State_1` + -0.028722086224731568 * rnn_cte_lstm_1.`State_2` + 0.4968708561774392 * rnn_cte_lstm_1.`State_3`) - exp(-(lstm_input_kernel_bias.dot_prod_c_1 + 0.03177660698427795 * rnn_cte_lstm_1.`State_1` + -0.028722086224731568 * rnn_cte_lstm_1.`State_2` + 0.4968708561774392 * rnn_cte_lstm_1.`State_3`))) / (exp(lstm_input_kernel_bias.dot_prod_c_1 + 0.03177660698427795 * rnn_cte_lstm_1.`State_1` + -0.028722086224731568 * rnn_cte_lstm_1.`State_2` + 0.4968708561774392 * rnn_cte_lstm_1.`State_3`) + exp(-(lstm_input_kernel_bias.dot_prod_c_1 + 0.03177660698427795 * rnn_cte_lstm_1.`State_1` + -0.028722086224731568 * rnn_cte_lstm_1.`State_2` + 0.4968708561774392 * rnn_cte_lstm_1.`State_3`)))) + greatest(lstm_input_kernel_bias.dot_prod_f_1 + -0.41132312467377813 * rnn_cte_lstm_1.`State_1` + 0.15872902889329948 * rnn_cte_lstm_1.`State_2` + -0.030968399142072722 * rnn_cte_lstm_1.`State_3`, 0) * rnn_cte_lstm_1.`State_4`) + exp(-(greatest(lstm_input_kernel_bias.dot_prod_i_1 + 0.09160992365240425 * rnn_cte_lstm_1.`State_1` + 0.24971253146357347 * rnn_cte_lstm_1.`State_2` + -0.06855082080993892 * rnn_cte_lstm_1.`State_3`, 0) * ((exp(lstm_input_kernel_bias.dot_prod_c_1 + 0.03177660698427795 * rnn_cte_lstm_1.`State_1` + -0.028722086224731568 * rnn_cte_lstm_1.`State_2` + 0.4968708561774392 * rnn_cte_lstm_1.`State_3`) - exp(-(lstm_input_kernel_bias.dot_prod_c_1 + 0.03177660698427795 * rnn_cte_lstm_1.`State_1` + -0.028722086224731568 * rnn_cte_lstm_1.`State_2` + 0.4968708561774392 * rnn_cte_lstm_1.`State_3`))) / (exp(lstm_input_kernel_bias.dot_prod_c_1 + 0.03177660698427795 * rnn_cte_lstm_1.`State_1` + -0.028722086224731568 * rnn_cte_lstm_1.`State_2` + 0.4968708561774392 * rnn_cte_lstm_1.`State_3`) + exp(-(lstm_input_kernel_bias.dot_prod_c_1 + 0.03177660698427795 * rnn_cte_lstm_1.`State_1` + -0.028722086224731568 * rnn_cte_lstm_1.`State_2` + 0.4968708561774392 * rnn_cte_lstm_1.`State_3`)))) + greatest(lstm_input_kernel_bias.dot_prod_f_1 + -0.41132312467377813 * rnn_cte_lstm_1.`State_1` + 0.15872902889329948 * rnn_cte_lstm_1.`State_2` + -0.030968399142072722 * rnn_cte_lstm_1.`State_3`, 0) * rnn_cte_lstm_1.`State_4`)))) AS `State_1`, greatest(lstm_input_kernel_bias.dot_prod_o_2 + 0.09010452483822186 * rnn_cte_lstm_1.`State_1` + 0.5835936354505167 * rnn_cte_lstm_1.`State_2` + -0.23781182562063413 * rnn_cte_lstm_1.`State_3`, 0) * ((exp(greatest(lstm_input_kernel_bias.dot_prod_i_2 + -0.18018617233786635 * rnn_cte_lstm_1.`State_1` + -0.3310485788422588 * rnn_cte_lstm_1.`State_2` + -0.09092443461703593 * rnn_cte_lstm_1.`State_3`, 0) * ((exp(lstm_input_kernel_bias.dot_prod_c_2 + 0.17643804311563757 * rnn_cte_lstm_1.`State_1` + 0.40765950953451074 * rnn_cte_lstm_1.`State_2` + 0.1420279512766052 * rnn_cte_lstm_1.`State_3`) - exp(-(lstm_input_kernel_bias.dot_prod_c_2 + 0.17643804311563757 * rnn_cte_lstm_1.`State_1` + 0.40765950953451074 * rnn_cte_lstm_1.`State_2` + 0.1420279512766052 * rnn_cte_lstm_1.`State_3`))) / (exp(lstm_input_kernel_bias.dot_prod_c_2 + 0.17643804311563757 * rnn_cte_lstm_1.`State_1` + 0.40765950953451074 * rnn_cte_lstm_1.`State_2` + 0.1420279512766052 * rnn_cte_lstm_1.`State_3`) + exp(-(lstm_input_kernel_bias.dot_prod_c_2 + 0.17643804311563757 * rnn_cte_lstm_1.`State_1` + 0.40765950953451074 * rnn_cte_lstm_1.`State_2` + 0.1420279512766052 * rnn_cte_lstm_1.`State_3`)))) + greatest(lstm_input_kernel_bias.dot_prod_f_2 + 0.32161574302934537 * rnn_cte_lstm_1.`State_1` + -0.13461764373831114 * rnn_cte_lstm_1.`State_2` + 0.5745805616344715 * rnn_cte_lstm_1.`State_3`, 0) * rnn_cte_lstm_1.`State_5`) - exp(-(greatest(lstm_input_kernel_bias.dot_prod_i_2 + -0.18018617233786635 * rnn_cte_lstm_1.`State_1` + -0.3310485788422588 * rnn_cte_lstm_1.`State_2` + -0.09092443461703593 * rnn_cte_lstm_1.`State_3`, 0) * ((exp(lstm_input_kernel_bias.dot_prod_c_2 + 0.17643804311563757 * rnn_cte_lstm_1.`State_1` + 0.40765950953451074 * rnn_cte_lstm_1.`State_2` + 0.1420279512766052 * rnn_cte_lstm_1.`State_3`) - exp(-(lstm_input_kernel_bias.dot_prod_c_2 + 0.17643804311563757 * rnn_cte_lstm_1.`State_1` + 0.40765950953451074 * rnn_cte_lstm_1.`State_2` + 0.1420279512766052 * rnn_cte_lstm_1.`State_3`))) / (exp(lstm_input_kernel_bias.dot_prod_c_2 + 0.17643804311563757 * rnn_cte_lstm_1.`State_1` + 0.40765950953451074 * rnn_cte_lstm_1.`State_2` + 0.1420279512766052 * rnn_cte_lstm_1.`State_3`) + exp(-(lstm_input_kernel_bias.dot_prod_c_2 + 0.17643804311563757 * rnn_cte_lstm_1.`State_1` + 0.40765950953451074 * rnn_cte_lstm_1.`State_2` + 0.1420279512766052 * rnn_cte_lstm_1.`State_3`)))) + greatest(lstm_input_kernel_bias.dot_prod_f_2 + 0.32161574302934537 * rnn_cte_lstm_1.`State_1` + -0.13461764373831114 * rnn_cte_lstm_1.`State_2` + 0.5745805616344715 * rnn_cte_lstm_1.`State_3`, 0) * rnn_cte_lstm_1.`State_5`))) / (exp(greatest(lstm_input_kernel_bias.dot_prod_i_2 + -0.18018617233786635 * rnn_cte_lstm_1.`State_1` + -0.3310485788422588 * rnn_cte_lstm_1.`State_2` + -0.09092443461703593 * rnn_cte_lstm_1.`State_3`, 0) * ((exp(lstm_input_kernel_bias.dot_prod_c_2 + 0.17643804311563757 * rnn_cte_lstm_1.`State_1` + 0.40765950953451074 * rnn_cte_lstm_1.`State_2` + 0.1420279512766052 * rnn_cte_lstm_1.`State_3`) - exp(-(lstm_input_kernel_bias.dot_prod_c_2 + 0.17643804311563757 * rnn_cte_lstm_1.`State_1` + 0.40765950953451074 * rnn_cte_lstm_1.`State_2` + 0.1420279512766052 * rnn_cte_lstm_1.`State_3`))) / (exp(lstm_input_kernel_bias.dot_prod_c_2 + 0.17643804311563757 * rnn_cte_lstm_1.`State_1` + 0.40765950953451074 * rnn_cte_lstm_1.`State_2` + 0.1420279512766052 * rnn_cte_lstm_1.`State_3`) + exp(-(lstm_input_kernel_bias.dot_prod_c_2 + 0.17643804311563757 * rnn_cte_lstm_1.`State_1` + 0.40765950953451074 * rnn_cte_lstm_1.`State_2` + 0.1420279512766052 * rnn_cte_lstm_1.`State_3`)))) + greatest(lstm_input_kernel_bias.dot_prod_f_2 + 0.32161574302934537 * rnn_cte_lstm_1.`State_1` + -0.13461764373831114 * rnn_cte_lstm_1.`State_2` + 0.5745805616344715 * rnn_cte_lstm_1.`State_3`, 0) * rnn_cte_lstm_1.`State_5`) + exp(-(greatest(lstm_input_kernel_bias.dot_prod_i_2 + -0.18018617233786635 * rnn_cte_lstm_1.`State_1` + -0.3310485788422588 * rnn_cte_lstm_1.`State_2` + -0.09092443461703593 * rnn_cte_lstm_1.`State_3`, 0) * ((exp(lstm_input_kernel_bias.dot_prod_c_2 + 0.17643804311563757 * rnn_cte_lstm_1.`State_1` + 0.40765950953451074 * rnn_cte_lstm_1.`State_2` + 0.1420279512766052 * rnn_cte_lstm_1.`State_3`) - exp(-(lstm_input_kernel_bias.dot_prod_c_2 + 0.17643804311563757 * rnn_cte_lstm_1.`State_1` + 0.40765950953451074 * rnn_cte_lstm_1.`State_2` + 0.1420279512766052 * rnn_cte_lstm_1.`State_3`))) / (exp(lstm_input_kernel_bias.dot_prod_c_2 + 0.17643804311563757 * rnn_cte_lstm_1.`State_1` + 0.40765950953451074 * rnn_cte_lstm_1.`State_2` + 0.1420279512766052 * rnn_cte_lstm_1.`State_3`) + exp(-(lstm_input_kernel_bias.dot_prod_c_2 + 0.17643804311563757 * rnn_cte_lstm_1.`State_1` + 0.40765950953451074 * rnn_cte_lstm_1.`State_2` + 0.1420279512766052 * rnn_cte_lstm_1.`State_3`)))) + greatest(lstm_input_kernel_bias.dot_prod_f_2 + 0.32161574302934537 * rnn_cte_lstm_1.`State_1` + -0.13461764373831114 * rnn_cte_lstm_1.`State_2` + 0.5745805616344715 * rnn_cte_lstm_1.`State_3`, 0) * rnn_cte_lstm_1.`State_5`)))) AS `State_2`, greatest(lstm_input_kernel_bias.dot_prod_o_3 + 0.6592687143997185 * rnn_cte_lstm_1.`State_1` + -0.3097730361696603 * rnn_cte_lstm_1.`State_2` + -0.41857244690422835 * rnn_cte_lstm_1.`State_3`, 0) * ((exp(greatest(lstm_input_kernel_bias.dot_prod_i_3 + -0.10464479815162242 * rnn_cte_lstm_1.`State_1` + 0.24685558674987718 * rnn_cte_lstm_1.`State_2` + -0.20684374306231754 * rnn_cte_lstm_1.`State_3`, 0) * ((exp(lstm_input_kernel_bias.dot_prod_c_3 + -0.08587924677854476 * rnn_cte_lstm_1.`State_1` + -0.16123651943934286 * rnn_cte_lstm_1.`State_2` + -0.1835351686447675 * rnn_cte_lstm_1.`State_3`) - exp(-(lstm_input_kernel_bias.dot_prod_c_3 + -0.08587924677854476 * rnn_cte_lstm_1.`State_1` + -0.16123651943934286 * rnn_cte_lstm_1.`State_2` + -0.1835351686447675 * rnn_cte_lstm_1.`State_3`))) / (exp(lstm_input_kernel_bias.dot_prod_c_3 + -0.08587924677854476 * rnn_cte_lstm_1.`State_1` + -0.16123651943934286 * rnn_cte_lstm_1.`State_2` + -0.1835351686447675 * rnn_cte_lstm_1.`State_3`) + exp(-(lstm_input_kernel_bias.dot_prod_c_3 + -0.08587924677854476 * rnn_cte_lstm_1.`State_1` + -0.16123651943934286 * rnn_cte_lstm_1.`State_2` + -0.1835351686447675 * rnn_cte_lstm_1.`State_3`)))) + greatest(lstm_input_kernel_bias.dot_prod_f_3 + 0.4707093426939127 * rnn_cte_lstm_1.`State_1` + 0.24649233617954075 * rnn_cte_lstm_1.`State_2` + 0.07631606488178362 * rnn_cte_lstm_1.`State_3`, 0) * rnn_cte_lstm_1.`State_6`) - exp(-(greatest(lstm_input_kernel_bias.dot_prod_i_3 + -0.10464479815162242 * rnn_cte_lstm_1.`State_1` + 0.24685558674987718 * rnn_cte_lstm_1.`State_2` + -0.20684374306231754 * rnn_cte_lstm_1.`State_3`, 0) * ((exp(lstm_input_kernel_bias.dot_prod_c_3 + -0.08587924677854476 * rnn_cte_lstm_1.`State_1` + -0.16123651943934286 * rnn_cte_lstm_1.`State_2` + -0.1835351686447675 * rnn_cte_lstm_1.`State_3`) - exp(-(lstm_input_kernel_bias.dot_prod_c_3 + -0.08587924677854476 * rnn_cte_lstm_1.`State_1` + -0.16123651943934286 * rnn_cte_lstm_1.`State_2` + -0.1835351686447675 * rnn_cte_lstm_1.`State_3`))) / (exp(lstm_input_kernel_bias.dot_prod_c_3 + -0.08587924677854476 * rnn_cte_lstm_1.`State_1` + -0.16123651943934286 * rnn_cte_lstm_1.`State_2` + -0.1835351686447675 * rnn_cte_lstm_1.`State_3`) + exp(-(lstm_input_kernel_bias.dot_prod_c_3 + -0.08587924677854476 * rnn_cte_lstm_1.`State_1` + -0.16123651943934286 * rnn_cte_lstm_1.`State_2` + -0.1835351686447675 * rnn_cte_lstm_1.`State_3`)))) + greatest(lstm_input_kernel_bias.dot_prod_f_3 + 0.4707093426939127 * rnn_cte_lstm_1.`State_1` + 0.24649233617954075 * rnn_cte_lstm_1.`State_2` + 0.07631606488178362 * rnn_cte_lstm_1.`State_3`, 0) * rnn_cte_lstm_1.`State_6`))) / (exp(greatest(lstm_input_kernel_bias.dot_prod_i_3 + -0.10464479815162242 * rnn_cte_lstm_1.`State_1` + 0.24685558674987718 * rnn_cte_lstm_1.`State_2` + -0.20684374306231754 * rnn_cte_lstm_1.`State_3`, 0) * ((exp(lstm_input_kernel_bias.dot_prod_c_3 + -0.08587924677854476 * rnn_cte_lstm_1.`State_1` + -0.16123651943934286 * rnn_cte_lstm_1.`State_2` + -0.1835351686447675 * rnn_cte_lstm_1.`State_3`) - exp(-(lstm_input_kernel_bias.dot_prod_c_3 + -0.08587924677854476 * rnn_cte_lstm_1.`State_1` + -0.16123651943934286 * rnn_cte_lstm_1.`State_2` + -0.1835351686447675 * rnn_cte_lstm_1.`State_3`))) / (exp(lstm_input_kernel_bias.dot_prod_c_3 + -0.08587924677854476 * rnn_cte_lstm_1.`State_1` + -0.16123651943934286 * rnn_cte_lstm_1.`State_2` + -0.1835351686447675 * rnn_cte_lstm_1.`State_3`) + exp(-(lstm_input_kernel_bias.dot_prod_c_3 + -0.08587924677854476 * rnn_cte_lstm_1.`State_1` + -0.16123651943934286 * rnn_cte_lstm_1.`State_2` + -0.1835351686447675 * rnn_cte_lstm_1.`State_3`)))) + greatest(lstm_input_kernel_bias.dot_prod_f_3 + 0.4707093426939127 * rnn_cte_lstm_1.`State_1` + 0.24649233617954075 * rnn_cte_lstm_1.`State_2` + 0.07631606488178362 * rnn_cte_lstm_1.`State_3`, 0) * rnn_cte_lstm_1.`State_6`) + exp(-(greatest(lstm_input_kernel_bias.dot_prod_i_3 + -0.10464479815162242 * rnn_cte_lstm_1.`State_1` + 0.24685558674987718 * rnn_cte_lstm_1.`State_2` + -0.20684374306231754 * rnn_cte_lstm_1.`State_3`, 0) * ((exp(lstm_input_kernel_bias.dot_prod_c_3 + -0.08587924677854476 * rnn_cte_lstm_1.`State_1` + -0.16123651943934286 * rnn_cte_lstm_1.`State_2` + -0.1835351686447675 * rnn_cte_lstm_1.`State_3`) - exp(-(lstm_input_kernel_bias.dot_prod_c_3 + -0.08587924677854476 * rnn_cte_lstm_1.`State_1` + -0.16123651943934286 * rnn_cte_lstm_1.`State_2` + -0.1835351686447675 * rnn_cte_lstm_1.`State_3`))) / (exp(lstm_input_kernel_bias.dot_prod_c_3 + -0.08587924677854476 * rnn_cte_lstm_1.`State_1` + -0.16123651943934286 * rnn_cte_lstm_1.`State_2` + -0.1835351686447675 * rnn_cte_lstm_1.`State_3`) + exp(-(lstm_input_kernel_bias.dot_prod_c_3 + -0.08587924677854476 * rnn_cte_lstm_1.`State_1` + -0.16123651943934286 * rnn_cte_lstm_1.`State_2` + -0.1835351686447675 * rnn_cte_lstm_1.`State_3`)))) + greatest(lstm_input_kernel_bias.dot_prod_f_3 + 0.4707093426939127 * rnn_cte_lstm_1.`State_1` + 0.24649233617954075 * rnn_cte_lstm_1.`State_2` + 0.07631606488178362 * rnn_cte_lstm_1.`State_3`, 0) * rnn_cte_lstm_1.`State_6`)))) AS `State_3`, greatest(lstm_input_kernel_bias.dot_prod_i_1 + 0.09160992365240425 * rnn_cte_lstm_1.`State_1` + 0.24971253146357347 * rnn_cte_lstm_1.`State_2` + -0.06855082080993892 * rnn_cte_lstm_1.`State_3`, 0) * ((exp(lstm_input_kernel_bias.dot_prod_c_1 + 0.03177660698427795 * rnn_cte_lstm_1.`State_1` + -0.028722086224731568 * rnn_cte_lstm_1.`State_2` + 0.4968708561774392 * rnn_cte_lstm_1.`State_3`) - exp(-(lstm_input_kernel_bias.dot_prod_c_1 + 0.03177660698427795 * rnn_cte_lstm_1.`State_1` + -0.028722086224731568 * rnn_cte_lstm_1.`State_2` + 0.4968708561774392 * rnn_cte_lstm_1.`State_3`))) / (exp(lstm_input_kernel_bias.dot_prod_c_1 + 0.03177660698427795 * rnn_cte_lstm_1.`State_1` + -0.028722086224731568 * rnn_cte_lstm_1.`State_2` + 0.4968708561774392 * rnn_cte_lstm_1.`State_3`) + exp(-(lstm_input_kernel_bias.dot_prod_c_1 + 0.03177660698427795 * rnn_cte_lstm_1.`State_1` + -0.028722086224731568 * rnn_cte_lstm_1.`State_2` + 0.4968708561774392 * rnn_cte_lstm_1.`State_3`)))) + greatest(lstm_input_kernel_bias.dot_prod_f_1 + -0.41132312467377813 * rnn_cte_lstm_1.`State_1` + 0.15872902889329948 * rnn_cte_lstm_1.`State_2` + -0.030968399142072722 * rnn_cte_lstm_1.`State_3`, 0) * rnn_cte_lstm_1.`State_4` AS `State_4`, greatest(lstm_input_kernel_bias.dot_prod_i_2 + -0.18018617233786635 * rnn_cte_lstm_1.`State_1` + -0.3310485788422588 * rnn_cte_lstm_1.`State_2` + -0.09092443461703593 * rnn_cte_lstm_1.`State_3`, 0) * ((exp(lstm_input_kernel_bias.dot_prod_c_2 + 0.17643804311563757 * rnn_cte_lstm_1.`State_1` + 0.40765950953451074 * rnn_cte_lstm_1.`State_2` + 0.1420279512766052 * rnn_cte_lstm_1.`State_3`) - exp(-(lstm_input_kernel_bias.dot_prod_c_2 + 0.17643804311563757 * rnn_cte_lstm_1.`State_1` + 0.40765950953451074 * rnn_cte_lstm_1.`State_2` + 0.1420279512766052 * rnn_cte_lstm_1.`State_3`))) / (exp(lstm_input_kernel_bias.dot_prod_c_2 + 0.17643804311563757 * rnn_cte_lstm_1.`State_1` + 0.40765950953451074 * rnn_cte_lstm_1.`State_2` + 0.1420279512766052 * rnn_cte_lstm_1.`State_3`) + exp(-(lstm_input_kernel_bias.dot_prod_c_2 + 0.17643804311563757 * rnn_cte_lstm_1.`State_1` + 0.40765950953451074 * rnn_cte_lstm_1.`State_2` + 0.1420279512766052 * rnn_cte_lstm_1.`State_3`)))) + greatest(lstm_input_kernel_bias.dot_prod_f_2 + 0.32161574302934537 * rnn_cte_lstm_1.`State_1` + -0.13461764373831114 * rnn_cte_lstm_1.`State_2` + 0.5745805616344715 * rnn_cte_lstm_1.`State_3`, 0) * rnn_cte_lstm_1.`State_5` AS `State_5`, greatest(lstm_input_kernel_bias.dot_prod_i_3 + -0.10464479815162242 * rnn_cte_lstm_1.`State_1` + 0.24685558674987718 * rnn_cte_lstm_1.`State_2` + -0.20684374306231754 * rnn_cte_lstm_1.`State_3`, 0) * ((exp(lstm_input_kernel_bias.dot_prod_c_3 + -0.08587924677854476 * rnn_cte_lstm_1.`State_1` + -0.16123651943934286 * rnn_cte_lstm_1.`State_2` + -0.1835351686447675 * rnn_cte_lstm_1.`State_3`) - exp(-(lstm_input_kernel_bias.dot_prod_c_3 + -0.08587924677854476 * rnn_cte_lstm_1.`State_1` + -0.16123651943934286 * rnn_cte_lstm_1.`State_2` + -0.1835351686447675 * rnn_cte_lstm_1.`State_3`))) / (exp(lstm_input_kernel_bias.dot_prod_c_3 + -0.08587924677854476 * rnn_cte_lstm_1.`State_1` + -0.16123651943934286 * rnn_cte_lstm_1.`State_2` + -0.1835351686447675 * rnn_cte_lstm_1.`State_3`) + exp(-(lstm_input_kernel_bias.dot_prod_c_3 + -0.08587924677854476 * rnn_cte_lstm_1.`State_1` + -0.16123651943934286 * rnn_cte_lstm_1.`State_2` + -0.1835351686447675 * rnn_cte_lstm_1.`State_3`)))) + greatest(lstm_input_kernel_bias.dot_prod_f_3 + 0.4707093426939127 * rnn_cte_lstm_1.`State_1` + 0.24649233617954075 * rnn_cte_lstm_1.`State_2` + 0.07631606488178362 * 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) AS output_1, CAST(rnn_cte_lstm_1.`State_2` AS DOUBLE) AS output_2, CAST(rnn_cte_lstm_1.`State_3` AS DOUBLE) 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`, -0.06627671499971587 + -0.3801641919165004 * lstm_1_1.output_1 + -0.3632051852500408 * lstm_1_1.output_2 + 0.7026323746868544 * lstm_1_1.output_3 AS output_1, 0.03689282876960263 + 0.7741702579410134 * lstm_1_1.output_1 + 0.2725005930594743 * lstm_1_1.output_2 + 0.5890354813857848 * lstm_1_1.output_3 AS output_2, -0.015245866079505278 + -0.3628457018247834 * lstm_1_1.output_1 + 0.45271946404895813 * lstm_1_1.output_2 + -0.8067188350815782 * lstm_1_1.output_3 AS output_3, 0.03408727049456051 + -0.2323121995863217 * lstm_1_1.output_1 + -0.2223507203102801 * lstm_1_1.output_2 + -0.5136639191477129 * lstm_1_1.output_3 AS output_4 
  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 
  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` 
  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) 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`, 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 
  FROM score_soft_max), 
  orig_cte AS 
  (SELECT layer_softmax.`KEY` AS `KEY`, NULL AS `Score_0`, NULL AS `Score_1`, NULL AS `Score_2`, NULL AS `Score_3`, 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`, NULL AS `LogProba_0`, NULL AS `LogProba_1`, NULL AS `LogProba_2`, NULL AS `LogProba_3`, CAST(NULL AS SIGNED INTEGER) AS `Decision`, NULL 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) 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.`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.`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.`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.`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.`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.`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.`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.`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.`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.`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`, 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`, arg_max_cte.`arg_max_Proba` AS `Decision`, arg_max_cte.`max_Proba` AS `DecisionProba` 
  FROM arg_max_cte