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From GitBox <...@apache.org>
Subject [GitHub] [madlib] fmcquillan99 commented on issue #451: DL: Update evaluate and predict for multi model outputs
Date Mon, 04 Nov 2019 21:59:53 GMT
fmcquillan99 commented on issue #451: DL: Update evaluate and predict for multi model outputs
URL: https://github.com/apache/madlib/pull/451#issuecomment-549566751
 
 
   
   ------------------------------------------------------------------------------
   
   Retesting after last commits (4) (5) (6) (7) (8) (9) (10) below:
   
   (1) review after recent commit
   needed to re-run to get new results:
   
   ```
   DROP TABLE IF EXISTS iris_multi_model, iris_multi_model_summary, iris_multi_model_info;
   
   SELECT madlib.madlib_keras_fit_multiple_model('iris_train_packed',    -- source_table
                                                 'iris_multi_model',     -- model_output_table
                                                 'mst_table',            -- model_selection_table
                                                 10,                     -- num_iterations
                                                 0,                     -- use gpus
                                                 'iris_test_packed'      -- validation dataset
                                                );
   
   SELECT * FROM iris_multi_model_info ORDER BY training_metrics_final DESC;
   
   -[ RECORD 1 ]------------+-------------------------------------------------------------------------------------------------------------------------------------------------------------------
   --------------------
   mst_key                  | 2
   model_id                 | 1
   compile_params           | loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy']
   fit_params               | batch_size=8,epochs=1
   model_type               | madlib_keras
   model_size               | 0.7900390625
   metrics_elapsed_time     | {0.16501784324646,0.154597043991089,0.158796072006226,0.171799898147583,0.168801069259644,0.160946846008301,0.168446063995361,0.249520063400269,0.723811149597168,
   0.157901048660278}
   metrics_type             | {accuracy}
   training_metrics_final   | 0.975000023842
   training_loss_final      | 0.184436321259
   training_metrics         | {0.666666686534882,0.708333313465118,0.975000023841858,0.975000023841858,0.458333343267441,0.666666686534882,0.666666686534882,0.716666638851166,0.816666662693024
   ,0.975000023841858}
   training_loss            | {0.654977560043335,0.473680555820465,0.407350271940231,0.344571173191071,1.01371514797211,0.536512732505798,0.542378485202789,0.427120715379715,0.301178097724915,
   0.184436321258545}
   validation_metrics_final | 0.966666638851
   validation_loss_final    | 0.229953154922
   validation_metrics       | {0.666666686534882,0.633333325386047,0.933333337306976,0.933333337306976,0.333333343267441,0.666666686534882,0.666666686534882,0.666666686534882,0.800000011920929
   ,0.966666638851166}
   validation_loss          | {0.632323026657104,0.544658362865448,0.475189208984375,0.403949052095413,1.15320909023285,0.571010947227478,0.57290381193161,0.472318321466446,0.329961955547333,0
   .229953154921532}
   -[ RECORD 2 ]------------+-------------------------------------------------------------------------------------------------------------------------------------------------------------------
   --------------------
   mst_key                  | 6
   model_id                 | 1
   compile_params           | loss='categorical_crossentropy',optimizer='Adam(lr=0.001)',metrics=['accuracy']
   fit_params               | batch_size=8,epochs=1
   model_type               | madlib_keras
   model_size               | 0.7900390625
   metrics_elapsed_time     | {0.151521921157837,0.170912981033325,0.162657022476196,0.154775142669678,0.152937889099121,0.15539813041687,0.167464017868042,0.155567169189453,0.182074785232544,
   0.164788961410522}
   metrics_type             | {accuracy}
   training_metrics_final   | 0.933333337307
   training_loss_final      | 0.653343558311
   training_metrics         | {0.00833333376795053,0.133333340287209,0.358333319425583,0.491666674613953,0.75,0.958333313465118,0.958333313465118,0.949999988079071,0.908333361148834,0.93333333
   7306976}
   training_loss            | {1.24945497512817,1.10760903358459,1.04750990867615,0.997703850269318,0.913850665092468,0.828234732151031,0.770057380199432,0.728686928749084,0.687141954898834,0.
   653343558311462}
   validation_metrics_final | 0.833333313465
   validation_loss_final    | 0.668307900429
   validation_metrics       | {0,0.100000001490116,0.433333337306976,0.566666662693024,0.766666650772095,0.899999976158142,0.899999976158142,0.866666674613953,0.800000011920929,0.8333333134651
   18}
   validation_loss          | {1.30171895027161,1.11200094223022,1.03035044670105,1.00190925598145,0.922983944416046,0.828430116176605,0.771750569343567,0.736835598945618,0.698546707630157,0.6
   68307900428772}
   -[ RECORD 3 ]------------+-------------------------------------------------------------------------------------------------------------------------------------------------------------------
   --------------------
   mst_key                  | 8
   model_id                 | 2
   compile_params           | loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy']
   fit_params               | batch_size=8,epochs=1
   model_type               | madlib_keras
   model_size               | 1.2197265625
   metrics_elapsed_time     | {0.178833961486816,0.178119897842407,0.193637132644653,0.183725118637085,0.181838989257812,0.190093994140625,0.190335988998413,0.183039903640747,0.189705848693848
   ,0.196530103683472}
   metrics_type             | {accuracy}
   training_metrics_final   | 0.925000011921
   training_loss_final      | 0.191128313541
   training_metrics         | {0.675000011920929,0.699999988079071,0.708333313465118,0.850000023841858,0.691666662693024,0.966666638851166,0.949999988079071,0.858333349227905,0.766666650772095
   ,0.925000011920929}
   training_loss            | {1.00050187110901,0.477404177188873,0.449770420789719,0.367935240268707,0.715284705162048,0.233321651816368,0.206968143582344,0.247707515954971,0.513520300388336,
   0.191128313541412}
   validation_metrics_final | 0.899999976158
   validation_loss_final    | 0.238029867411
   validation_metrics       | {0.566666662693024,0.666666686534882,0.666666686534882,0.833333313465118,0.566666662693024,0.966666638851166,0.933333337306976,0.866666674613953,0.699999988079071
   ,0.899999976158142}
   validation_loss          | {1.10490369796753,0.515014708042145,0.473093926906586,0.429382801055908,0.964671075344086,0.282983720302582,0.234358683228493,0.304766476154327,0.506977200508118,
   0.23802986741066}
   -[ RECORD 4 ]------------+-------------------------------------------------------------------------------------------------------------------------------------------------------------------
   --------------------
   mst_key                  | 9
   model_id                 | 2
   compile_params           | loss='categorical_crossentropy', optimizer='Adam(lr=0.01)',metrics=['accuracy']
   fit_params               | batch_size=4,epochs=1
   model_type               | madlib_keras
   model_size               | 1.2197265625
   metrics_elapsed_time     | {0.179304122924805,0.177167892456055,0.197530031204224,0.186438083648682,0.183886051177979,0.179242849349976,0.261205911636353,0.180387020111084,0.190798997879028
   ,0.199844121932983}
   metrics_type             | {accuracy}
   training_metrics_final   | 0.925000011921
   training_loss_final      | 0.129962354898
   training_metrics         | {0.666666686534882,0.875,0.975000023841858,0.758333325386047,0.966666638851166,0.800000011920929,0.966666638851166,0.983333349227905,0.966666638851166,0.925000011
   920929}
   training_loss            | {0.631272912025452,0.311742216348648,0.201621904969215,0.389256983995438,0.119620576500893,0.486419379711151,0.105227634310722,0.0892100557684898,0.13771826028823
   9,0.129962354898453}
   validation_metrics_final | 1
   validation_loss_final    | 0.099272236228
   validation_metrics       | {0.666666686534882,0.833333313465118,0.966666638851166,0.733333349227905,0.899999976158142,0.733333349227905,0.899999976158142,0.966666638851166,0.866666674613953
   ,1}
   validation_loss          | {0.655599772930145,0.343398422002792,0.23225425183773,0.460039615631104,0.179262787103653,0.432839661836624,0.164745837450027,0.110677324235439,0.236034005880356,
   0.0992722362279892}
   -[ RECORD 5 ]------------+-------------------------------------------------------------------------------------------------------------------------------------------------------------------
   --------------------
   mst_key                  | 12
   model_id                 | 2
   compile_params           | loss='categorical_crossentropy',optimizer='Adam(lr=0.001)',metrics=['accuracy']
   fit_params               | batch_size=8,epochs=1
   model_type               | madlib_keras
   model_size               | 1.2197265625
   metrics_elapsed_time     | {0.179224967956543,0.178079128265381,0.202226161956787,0.190601825714111,0.179889917373657,0.182010173797607,0.223275899887085,0.186384916305542,0.1926109790802,0
   .205245018005371}
   metrics_type             | {accuracy}
   training_metrics_final   | 0.916666686535
   training_loss_final      | 0.557382702827
   training_metrics         | {0.400000005960464,0.466666668653488,0.641666650772095,0.758333325386047,0.850000023841858,0.875,0.866666674613953,0.908333361148834,0.908333361148834,0.916666686
   534882}
   training_loss            | {1.03677904605865,0.961223006248474,0.898576974868774,0.84269106388092,0.784159600734711,0.72914981842041,0.679587423801422,0.637798070907593,0.592370092868805,0.
   557382702827454}
   validation_metrics_final | 0.933333337307
   validation_loss_final    | 0.633718490601
   validation_metrics       | {0.233333334326744,0.333333343267441,0.433333337306976,0.666666686534882,0.800000011920929,0.933333337306976,1,0.933333337306976,0.966666638851166,0.9333333373069
   76}
   validation_loss          | {1.07957482337952,1.01587510108948,0.959396243095398,0.91132265329361,0.857007622718811,0.808456540107727,0.762722790241241,0.718301713466644,0.671158373355865,0.
   633718490600586}
   -[ RECORD 6 ]------------+-------------------------------------------------------------------------------------------------------------------------------------------------------------------
   --------------------
   mst_key                  | 10
   model_id                 | 2
   compile_params           | loss='categorical_crossentropy', optimizer='Adam(lr=0.01)',metrics=['accuracy']
   fit_params               | batch_size=8,epochs=1
   model_type               | madlib_keras
   model_size               | 1.2197265625
   metrics_elapsed_time     | {0.185405015945435,0.240031957626343,0.179027080535889,0.188817024230957,0.186041116714478,0.189532041549683,0.191230058670044,0.189625978469849,0.199972867965698
   ,0.184904098510742}
   metrics_type             | {accuracy}
   training_metrics_final   | 0.899999976158
   training_loss_final      | 0.202067151666
   training_metrics         | {0.691666662693024,0.675000011920929,0.691666662693024,0.875,0.899999976158142,0.983333349227905,0.983333349227905,0.908333361148834,0.933333337306976,0.899999976
   158142}
   training_loss            | {0.936435461044312,0.705836236476898,0.493957877159119,0.422138810157776,0.360373467206955,0.24227599799633,0.1996029317379,0.195797547698021,0.160537555813789,0.
   202067151665688}
   validation_metrics_final | 1
   validation_loss_final    | 0.204844340682
   validation_metrics       | {0.566666662693024,0.566666662693024,0.566666662693024,0.833333313465118,1,0.966666638851166,0.933333337306976,1,1,1}
   validation_loss          | {0.968072831630707,0.788067102432251,0.579671025276184,0.491580307483673,0.424855649471283,0.284231871366501,0.230658054351807,0.215303242206573,0.172062397003174
   ,0.20484434068203}
   -[ RECORD 7 ]------------+-------------------------------------------------------------------------------------------------------------------------------------------------------------------
   --------------------
   mst_key                  | 4
   model_id                 | 1
   compile_params           | loss='categorical_crossentropy', optimizer='Adam(lr=0.01)',metrics=['accuracy']
   fit_params               | batch_size=8,epochs=1
   model_type               | madlib_keras
   model_size               | 0.7900390625
   metrics_elapsed_time     | {0.158287048339844,0.155457973480225,0.576164960861206,0.162032127380371,0.15852689743042,0.156464099884033,0.161139965057373,0.160786867141724,0.167949914932251,
   0.656886100769043}
   metrics_type             | {accuracy}
   training_metrics_final   | 0.899999976158
   training_loss_final      | 0.169732123613
   training_metrics         | {0.666666686534882,0.666666686534882,0.816666662693024,0.991666674613953,0.741666674613953,0.899999976158142,0.925000011920929,0.741666674613953,0.983333349227905
   ,0.899999976158142}
   training_loss            | {0.76077276468277,0.5220667719841,0.420222669839859,0.327254116535187,0.341780602931976,0.252596199512482,0.206999227404594,0.435290902853012,0.150588378310204,0.
   169732123613358}
   validation_metrics_final | 1
   validation_loss_final    | 0.175337061286
   validation_metrics       | {0.666666686534882,0.666666686534882,0.666666686534882,0.933333337306976,0.666666686534882,0.833333313465118,1,0.633333325386047,0.966666638851166,1}
   validation_loss          | {0.767970025539398,0.556378245353699,0.499317765235901,0.392887800931931,0.395217329263687,0.307017415761948,0.249646931886673,0.518399477005005,0.179019212722778
   ,0.175337061285973}
   -[ RECORD 8 ]------------+-------------------------------------------------------------------------------------------------------------------------------------------------------------------
   --------------------
   mst_key                  | 5
   model_id                 | 1
   compile_params           | loss='categorical_crossentropy',optimizer='Adam(lr=0.001)',metrics=['accuracy']
   fit_params               | batch_size=4,epochs=1
   model_type               | madlib_keras
   model_size               | 0.7900390625
   metrics_elapsed_time     | {0.152910947799683,0.177506923675537,0.163512945175171,0.153976917266846,0.15569806098938,0.155301809310913,0.166845083236694,0.15528392791748,0.162101984024048,0
   .198511123657227}
   metrics_type             | {accuracy}
   training_metrics_final   | 0.875
   training_loss_final      | 0.530299961567
   training_metrics         | {0.333333343267441,0.191666662693024,0.508333325386047,0.791666686534882,0.883333325386047,0.816666662693024,0.875,0.783333361148834,0.75,0.875}
   training_loss            | {1.42261958122253,1.11098456382751,0.942022323608398,0.839245975017548,0.778925061225891,0.719573795795441,0.665915071964264,0.617532074451447,0.570941388607025,0
   .530299961566925}
   validation_metrics_final | 0.866666674614
   validation_loss_final    | 0.546338915825
   validation_metrics       | {0.333333343267441,0.16666667163372,0.400000005960464,0.833333313465118,0.899999976158142,0.833333313465118,0.899999976158142,0.833333313465118,0.699999988079071,
   0.866666674613953}
   validation_loss          | {1.58205306529999,1.19787907600403,0.984605967998505,0.874651849269867,0.803973138332367,0.732114672660828,0.681202590465546,0.62757009267807,0.578481078147888,0.
   54633891582489}
   -[ RECORD 9 ]------------+-------------------------------------------------------------------------------------------------------------------------------------------------------------------
   --------------------
   mst_key                  | 3
   model_id                 | 1
   compile_params           | loss='categorical_crossentropy', optimizer='Adam(lr=0.01)',metrics=['accuracy']
   fit_params               | batch_size=4,epochs=1
   model_type               | madlib_keras
   model_size               | 0.7900390625
   metrics_elapsed_time     | {0.155992031097412,0.153978109359741,0.572959899902344,0.162055015563965,0.155320167541504,0.156923055648804,0.163105964660645,0.161538124084473,0.168941020965576
   ,0.654891967773438}
   metrics_type             | {accuracy}
   training_metrics_final   | 0.824999988079
   training_loss_final      | 0.347870081663
   training_metrics         | {0.633333325386047,0.766666650772095,0.666666686534882,0.666666686534882,0.691666662693024,0.691666662693024,0.908333361148834,0.899999976158142,0.675000011920929
   ,0.824999988079071}
   training_loss            | {0.815440833568573,0.520094633102417,0.449681550264359,0.457792103290558,0.437296599149704,0.43956533074379,0.400028377771378,0.407060354948044,0.410939812660217,
   0.347870081663132}
   validation_metrics_final | 0.800000011921
   validation_loss_final    | 0.418093115091
   validation_metrics       | {0.600000023841858,0.666666686534882,0.666666686534882,0.666666686534882,0.566666662693024,0.566666662693024,0.833333313465118,0.866666674613953,0.666666686534882
   ,0.800000011920929}
   validation_loss          | {0.782467901706696,0.585534811019897,0.514401435852051,0.50047355890274,0.542211294174194,0.564250826835632,0.486649245023727,0.488315939903259,0.454440474510193,
   0.418093115091324}
   -[ RECORD 10 ]-----------+-------------------------------------------------------------------------------------------------------------------------------------------------------------------
   --------------------
   mst_key                  | 1
   model_id                 | 1
   compile_params           | loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy']
   fit_params               | batch_size=4,epochs=1
   model_type               | madlib_keras
   model_size               | 0.7900390625
   metrics_elapsed_time     | {0.154391050338745,0.155292987823486,0.166854858398438,0.157708883285522,0.15703010559082,0.15372896194458,0.161499977111816,0.15468692779541,0.163357973098755,0.
   166243076324463}
   metrics_type             | {accuracy}
   training_metrics_final   | 0.716666638851
   training_loss_final      | 0.498635411263
   training_metrics         | {0.666666686534882,0.666666686534882,0.816666662693024,0.991666674613953,0.824999988079071,0.933333337306976,0.824999988079071,0.975000023841858,0.816666662693024
   ,0.716666638851166}
   training_loss            | {0.597955822944641,0.43520125746727,0.371508300304413,0.0662390738725662,0.332939654588699,0.406542897224426,0.290035516023636,0.127334654331207,0.290014386177063
   ,0.498635411262512}
   validation_metrics_final | 0.699999988079
   validation_loss_final    | 0.547126591206
   validation_metrics       | {0.666666686534882,0.666666686534882,0.800000011920929,0.933333337306976,0.800000011920929,1,0.766666650772095,0.933333337306976,0.833333313465118,0.6999999880790
   71}
   validation_loss          | {0.594180822372437,0.464261531829834,0.383160322904587,0.0937399193644524,0.414926111698151,0.368867307901382,0.376850306987762,0.15509657561779,0.317675232887268
   ,0.547126591205597}
   -[ RECORD 11 ]-----------+-------------------------------------------------------------------------------------------------------------------------------------------------------------------
   --------------------
   mst_key                  | 11
   model_id                 | 2
   compile_params           | loss='categorical_crossentropy',optimizer='Adam(lr=0.001)',metrics=['accuracy']
   fit_params               | batch_size=4,epochs=1
   model_type               | madlib_keras
   model_size               | 1.2197265625
   metrics_elapsed_time     | {0.189064979553223,0.181553840637207,0.179675817489624,0.196763038635254,0.186076879501343,0.191986083984375,0.194756984710693,0.19181489944458,0.202284812927246,
   0.188621997833252}
   metrics_type             | {accuracy}
   training_metrics_final   | 0.708333313465
   training_loss_final      | 0.781156182289
   training_metrics         | {0.333333343267441,0.333333343267441,0.333333343267441,0.308333337306976,0.333333343267441,0.333333343267441,0.474999994039536,0.758333325386047,0.75,0.7083333134
   65118}
   training_loss            | {1.1848406791687,1.14267539978027,1.11269533634186,1.09739363193512,1.09032690525055,1.08066284656525,1.06443786621094,0.940568923950195,0.859182178974152,0.78115
   6182289124}
   validation_metrics_final | 0.666666686535
   validation_loss_final    | 0.827600717545
   validation_metrics       | {0.333333343267441,0.333333343267441,0.333333343267441,0.300000011920929,0.333333343267441,0.333333343267441,0.566666662693024,0.766666650772095,0.866666674613953
   ,0.666666686534882}
   validation_loss          | {1.15087306499481,1.12277579307556,1.10153806209564,1.09295105934143,1.08225655555725,1.07222008705139,1.05354011058807,0.967542886734009,0.901465713977814,0.8276
   00717544556}
   -[ RECORD 12 ]-----------+-------------------------------------------------------------------------------------------------------------------------------------------------------------------
   --------------------
   mst_key                  | 7
   model_id                 | 2
   compile_params           | loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy']
   fit_params               | batch_size=4,epochs=1
   model_type               | madlib_keras
   model_size               | 1.2197265625
   metrics_elapsed_time     | {0.178417921066284,0.60815691947937,0.202095985412598,0.178900003433228,0.18206000328064,0.181649923324585,0.191691875457764,0.182677984237671,0.183932065963745,0
   .189748048782349}
   metrics_type             | {accuracy}
   training_metrics_final   | 0.358333319426
   training_loss_final      | 1.10466384888
   training_metrics         | {0.358333319425583,0.358333319425583,0.358333319425583,0.308333337306976,0.358333319425583,0.358333319425583,0.358333319425583,0.358333319425583,0.333333343267441
   ,0.358333319425583}
   training_loss            | {1.10114789009094,1.10259783267975,1.09921455383301,1.1069358587265,1.09716868400574,1.10427033901215,1.0967606306076,1.10034203529358,1.09822237491608,1.10466384
   887695}
   validation_metrics_final | 0.233333334327
   validation_loss_final    | 1.14768576622
   validation_metrics       | {0.233333334326744,0.233333334326744,0.233333334326744,0.433333337306976,0.233333334326744,0.233333334326744,0.233333334326744,0.233333334326744,0.333333343267441
   ,0.233333334326744}
   validation_loss          | {1.14770805835724,1.11313581466675,1.09988021850586,1.10149800777435,1.10710287094116,1.10837662220001,1.11643397808075,1.14481270313263,1.10837042331696,1.147685
   76622009}
   ```
   
   (4) review after recent commit
   predict on training data
   
   ```
   DROP TABLE IF EXISTS iris_predict;
   
   SELECT madlib.madlib_keras_predict('iris_multi_model', -- model
                                      'iris_train',  -- train_table
                                      'id',         -- id column
                                      'attributes', -- independent var
                                      'iris_predict',  -- output table
                                       'response',  -- prediction type
                                       NULL,        -- use gpus
                                       2            -- mst_key to use
                                      );
   
   SELECT COUNT(*) FROM iris_predict JOIN iris_train USING (id)
   WHERE iris_predict.estimated_class_text != iris_train.class_text;
   
   -[ RECORD 1 ]
   count | 3
   ```
   
   error %
   
   ```
   SELECT round(count(*)*100/(150*0.8),2) as train_accuracy_percent from
       (select iris_train.class_text as actual, iris_predict.estimated_class_text as estimated
        from iris_predict inner join iris_train
        on iris_train.id=iris_predict.id) q
   WHERE q.actual=q.estimated;
   
    train_accuracy_percent 
   ------------------------
                     97.50
   (1 row)
   
   ```
   This now matches
   ```
   training_metrics_final   | 0.975000023842
   ```
   from the training output table.
   
   OK
   
   
   (5) review after recent commit
   predict on test data
   
   ```
   DROP TABLE IF EXISTS iris_predict;
   
   SELECT madlib.madlib_keras_predict('iris_multi_model', -- model
                                      'iris_test',  -- test_table
                                      'id',         -- id column
                                      'attributes', -- independent var
                                      'iris_predict',  -- output table
                                       'response',  -- prediction type
                                       NULL,        -- use gpus
                                       2            -- mst_key to use
                                      );
   
   SELECT COUNT(*) FROM iris_predict JOIN iris_test USING (id)
   WHERE iris_predict.estimated_class_text != iris_test.class_text;
   
    count
   -------
        1
   (1 row)
   ```
   
   as a %
   
   ```
   SELECT round(count(*)*100/(150*0.2),2) as test_accuracy_percent from
       (select iris_test.class_text as actual, iris_predict.estimated_class_text as estimated
        from iris_predict inner join iris_test
        on iris_test.id=iris_predict.id) q
   WHERE q.actual=q.estimated;
   
     test_accuracy_percent 
   -----------------------
                    96.67
   
   (1 row)
   ```
   which matches:
   ```
   validation_metrics_final | 0.966666638851
   ```
   from the training output table.
   
   OK
   
   
   (7) review after last commit
   pick wrong model
   
   ```
   DROP TABLE IF EXISTS iris_validate;
   SELECT madlib.madlib_keras_evaluate('iris_multi_model',  -- model
                                       'iris_test_packed',  -- test table
                                       'iris_validate',     -- output table
                                        NULL,               -- use gpus
                                        999                   -- mst_key to use
                                      );
   
   ERROR:  plpy.Error: madlib_keras_evaluate: mst_key 999 does not exist in the info table
(plpython.c:5038)
   CONTEXT:  Traceback (most recent call last):
     PL/Python function "madlib_keras_evaluate", line 21, in <module>
       return madlib_keras.evaluate(**globals())
     PL/Python function "madlib_keras_evaluate", line 575, in evaluate
     PL/Python function "madlib_keras_evaluate", line 244, in create_summary_view
   PL/Python function "madlib_keras_evaluate"
   ```
   which is a better message
   
   OK
   
   (8) review after last commit
   input table does not exist
   
   ```
   DROP TABLE IF EXISTS iris_validate;
   SELECT madlib.madlib_keras_evaluate('iris_multi_model999',  -- model
                                       'iris_test_packed',  -- test table
                                       'iris_validate',     -- output table
                                        NULL,               -- use gpus
                                        2                   -- mst_key to use
                                      );
   
   ERROR:  plpy.Error: madlib_keras_evaluate: Missing summary and/or info tables for iris_multi_model999
(plpython.c:5038)
   CONTEXT:  Traceback (most recent call last):
     PL/Python function "madlib_keras_evaluate", line 21, in <module>
       return madlib_keras.evaluate(**globals())
     PL/Python function "madlib_keras_evaluate", line 575, in evaluate
     PL/Python function "madlib_keras_evaluate", line 237, in create_summary_view
   PL/Python function "madlib_keras_evaluate"
   ```
   which is a better message
   
   OK
   
   (9) review after last commit:
   spot check on errors looks OK
   
   
   (10) review after last commit:
   
   ```
   SELECT * FROM iris_multi_model_summary;
   
   -[ RECORD 1 ]-------+---------------------------------------------
   source_table        | iris_train_packed
   validation_table    | iris_test_packed
   dependent_varname   | class_text
   independent_varname | attributes
   model_arch_table    | model_arch_library
   num_iterations      | 10
   start_training_time | 2019-11-04 21:46:55.109008
   end_training_time   | 2019-11-04 21:49:41.586433
   madlib_version      | 1.17-dev
   num_classes         | 3
   class_values        | {Iris-setosa,Iris-versicolor,Iris-virginica}
   dependent_vartype   | character varying
   normalizing_const   | 1
   
   ```
   
   OK

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