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Posted to issues@madlib.apache.org by "Frank McQuillan (JIRA)" <ji...@apache.org> on 2019/04/23 23:45:00 UTC
[jira] [Created] (MADLIB-1329) MLP warm start not working
Frank McQuillan created MADLIB-1329:
---------------------------------------
Summary: MLP warm start not working
Key: MADLIB-1329
URL: https://issues.apache.org/jira/browse/MADLIB-1329
Project: Apache MADlib
Issue Type: Bug
Components: Module: Neural Networks
Reporter: Frank McQuillan
Fix For: v1.16
MLP warm start not working when have >1 hidden layers:
{code}
madlib=# DROP TABLE IF EXISTS mlp_model, mlp_model_summary, mlp_model_standardization;
DROP TABLE
madlib=# -- Set seed so results are reproducible
madlib=# SELECT setseed(0);
-[ RECORD 1 ]
setseed |
madlib=# SELECT madlib.mlp_classification(
madlib(# 'iris_data', -- Source table
madlib(# 'mlp_model', -- Destination table
madlib(# 'attributes', -- Input features
madlib(# 'class_text', -- Label
madlib(# ARRAY[5,5,5,5], -- Number of units per layer
madlib(# 'learning_rate_init=0.003,
madlib'# n_iterations=25,
madlib'# tolerance=0', -- Optimizer params, with n_tries
madlib(# 'tanh', -- Activation function
madlib(# NULL, -- Default weight (1)
madlib(# FALSE, -- No warm start
madlib(# TRUE -- Not verbose
madlib(# );
INFO: Iteration: 1, Loss: <1.36043166072>
INFO: Iteration: 2, Loss: <1.3499381049>
INFO: Iteration: 3, Loss: <1.33443838909>
INFO: Iteration: 4, Loss: <1.29191921391>
INFO: Iteration: 5, Loss: <1.13514652999>
INFO: Iteration: 6, Loss: <0.605807639127>
INFO: Iteration: 7, Loss: <0.222521966636>
INFO: Iteration: 8, Loss: <0.119710009513>
INFO: Iteration: 9, Loss: <0.0798186326376>
INFO: Iteration: 10, Loss: <0.059110614479>
INFO: Iteration: 11, Loss: <0.0465561473767>
INFO: Iteration: 12, Loss: <0.0381870587426>
INFO: Iteration: 13, Loss: <0.0322380000338>
INFO: Iteration: 14, Loss: <0.0278080860799>
INFO: Iteration: 15, Loss: <0.0243910955926>
INFO: Iteration: 16, Loss: <0.0216814708136>
INFO: Iteration: 17, Loss: <0.019484269578>
INFO: Iteration: 18, Loss: <0.0176694972659>
INFO: Iteration: 19, Loss: <0.0161472761464>
INFO: Iteration: 20, Loss: <0.0148535627994>
INFO: Iteration: 21, Loss: <0.0137415416838>
INFO: Iteration: 22, Loss: <0.0127762321545>
INFO: Iteration: 23, Loss: <0.0119309943525>
INFO: Iteration: 24, Loss: <0.0111851991017>
-[ RECORD 1 ]------+-
mlp_classification |
madlib=# SELECT madlib.mlp_classification(
madlib(# 'iris_data', -- Source table
madlib(# 'mlp_model', -- Destination table
madlib(# 'attributes', -- Input features
madlib(# 'class_text', -- Label
madlib(# ARRAY[5,5,5,5], -- Number of units per layer
madlib(# 'learning_rate_init=0.003,
madlib'# n_iterations=25,
madlib'# tolerance=0', -- Optimizer params
madlib(# 'tanh', -- Activation function
madlib(# NULL, -- Default weight (1)
madlib(# TRUE, -- Warm start
madlib(# TRUE -- Not verbose
madlib(# );
INFO: Iteration: 1, Loss: <0.526775235884>
INFO: Iteration: 2, Loss: <0.117145401113>
INFO: Iteration: 3, Loss: <0.0602106163466>
INFO: Iteration: 4, Loss: <0.04108334178>
INFO: Iteration: 5, Loss: <0.0312666961015>
INFO: Iteration: 6, Loss: <0.0252605498585>
INFO: Iteration: 7, Loss: <0.0211986830952>
INFO: Iteration: 8, Loss: <0.01826564738>
INFO: Iteration: 9, Loss: <0.0160470744822>
INFO: Iteration: 10, Loss: <0.0143096846719>
INFO: Iteration: 11, Loss: <0.0129119612879>
INFO: Iteration: 12, Loss: <0.011763011155>
INFO: Iteration: 13, Loss: <0.0108017593072>
INFO: Iteration: 14, Loss: <0.00998563483374>
INFO: Iteration: 15, Loss: <0.0092840495142>
INFO: Iteration: 16, Loss: <0.00867445508889>
INFO: Iteration: 17, Loss: <0.00813986234697>
INFO: Iteration: 18, Loss: <0.00766722611383>
INFO: Iteration: 19, Loss: <0.00724636272143>
INFO: Iteration: 20, Loss: <0.00686920569199>
INFO: Iteration: 21, Loss: <0.00652928239318>
INFO: Iteration: 22, Loss: <0.00622133870019>
INFO: Iteration: 23, Loss: <0.00594106500617>
INFO: Iteration: 24, Loss: <0.00568489301496>
-[ RECORD 1 ]------+-
mlp_classification |
madlib=# SELECT madlib.mlp_classification(
madlib(# 'iris_data', -- Source table
madlib(# 'mlp_model', -- Destination table
madlib(# 'attributes', -- Input features
madlib(# 'class_text', -- Label
madlib(# ARRAY[5,5,5,5], -- Number of units per layer
madlib(# 'learning_rate_init=0.003,
madlib'# n_iterations=25,
madlib'# tolerance=0', -- Optimizer params
madlib(# 'tanh', -- Activation function
madlib(# NULL, -- Default weight (1)
madlib(# TRUE, -- Warm start
madlib(# TRUE -- Not verbose
madlib(# );
INFO: Iteration: 1, Loss: <0.310183199428>
INFO: Iteration: 2, Loss: <0.088393889654>
INFO: Iteration: 3, Loss: <0.0516018746862>
INFO: Iteration: 4, Loss: <0.0371242489164>
INFO: Iteration: 5, Loss: <0.0291275739389>
INFO: Iteration: 6, Loss: <0.0240085087393>
INFO: Iteration: 7, Loss: <0.0204364115942>
INFO: Iteration: 8, Loss: <0.0177966114175>
INFO: Iteration: 9, Loss: <0.0157638172685>
INFO: Iteration: 10, Loss: <0.0141490621788>
INFO: Iteration: 11, Loss: <0.0128347944511>
INFO: Iteration: 12, Loss: <0.0117439367706>
INFO: Iteration: 13, Loss: <0.0108237918575>
INFO: Iteration: 14, Loss: <0.0100370813095>
INFO: Iteration: 15, Loss: <0.00935667554548>
INFO: Iteration: 16, Loss: <0.00876235168295>
INFO: Iteration: 17, Loss: <0.00823872217701>
INFO: Iteration: 18, Loss: <0.00777386815345>
INFO: Iteration: 19, Loss: <0.00735841229808>
INFO: Iteration: 20, Loss: <0.0069848745105>
INFO: Iteration: 21, Loss: <0.00664721443072>
INFO: Iteration: 22, Loss: <0.00634050043973>
INFO: Iteration: 23, Loss: <0.00606066608629>
INFO: Iteration: 24, Loss: <0.00580432810321>
-[ RECORD 1 ]------+-
mlp_classification |
madlib=# SELECT madlib.mlp_classification(
madlib(# 'iris_data', -- Source table
madlib(# 'mlp_model', -- Destination table
madlib(# 'attributes', -- Input features
madlib(# 'class_text', -- Label
madlib(# ARRAY[5,5,5,5], -- Number of units per layer
madlib(# 'learning_rate_init=0.003,
madlib'# n_iterations=25,
madlib'# tolerance=0', -- Optimizer params
madlib(# 'tanh', -- Activation function
madlib(# NULL, -- Default weight (1)
madlib(# TRUE, -- Warm start
madlib(# TRUE -- Not verbose
madlib(# );
INFO: Iteration: 1, Loss: <0.241686849256>
INFO: Iteration: 2, Loss: <0.0789037812418>
INFO: Iteration: 3, Loss: <0.0477383278583>
INFO: Iteration: 4, Loss: <0.0349153179967>
INFO: Iteration: 5, Loss: <0.0276779503206>
INFO: Iteration: 6, Loss: <0.0229787339952>
INFO: Iteration: 7, Loss: <0.0196655648511>
INFO: Iteration: 8, Loss: <0.0171975669069>
INFO: Iteration: 9, Loss: <0.0152849520197>
INFO: Iteration: 10, Loss: <0.0137577086149>
INFO: Iteration: 11, Loss: <0.0125092120633>
INFO: Iteration: 12, Loss: <0.0114690677404>
INFO: Iteration: 13, Loss: <0.0105888625494>
INFO: Iteration: 14, Loss: <0.00983417252295>
INFO: Iteration: 15, Loss: <0.00917983203434>
INFO: Iteration: 16, Loss: <0.00860700679725>
INFO: Iteration: 17, Loss: <0.0081013143176>
INFO: Iteration: 18, Loss: <0.0076515781172>
INFO: Iteration: 19, Loss: <0.00724897914204>
INFO: Iteration: 20, Loss: <0.00688646377197>
INFO: Iteration: 21, Loss: <0.00655832207597>
INFO: Iteration: 22, Loss: <0.00625988170135>
INFO: Iteration: 23, Loss: <0.00598728196025>
INFO: Iteration: 24, Loss: <0.0057373045858>
-[ RECORD 1 ]------+-
mlp_classification |
{code}
MLP warm start does seem to work OK for 1 hidden layer:
{code}
madlib=# DROP TABLE IF EXISTS mlp_model, mlp_model_summary, mlp_model_standardization;
DROP TABLE
madlib=# -- Set seed so results are reproducible
madlib=# SELECT setseed(0);
-[ RECORD 1 ]
setseed |
madlib=# SELECT madlib.mlp_classification(
madlib(# 'iris_data', -- Source table
madlib(# 'mlp_model', -- Destination table
madlib(# 'attributes', -- Input features
madlib(# 'class_text', -- Label
madlib(# ARRAY[5], -- Number of units per layer
madlib(# 'learning_rate_init=0.003,
madlib'# n_iterations=25,
madlib'# tolerance=0', -- Optimizer params, with n_tries
madlib(# 'tanh', -- Activation function
madlib(# NULL, -- Default weight (1)
madlib(# FALSE, -- No warm start
madlib(# TRUE -- Not verbose
madlib(# );
INFO: Iteration: 1, Loss: <1.43845150739>
INFO: Iteration: 2, Loss: <0.645143112079>
INFO: Iteration: 3, Loss: <0.198510249554>
INFO: Iteration: 4, Loss: <0.10299445227>
INFO: Iteration: 5, Loss: <0.0683773141272>
INFO: Iteration: 6, Loss: <0.0508462725678>
INFO: Iteration: 7, Loss: <0.0403303687053>
INFO: Iteration: 8, Loss: <0.0333482996416>
INFO: Iteration: 9, Loss: <0.0283871624887>
INFO: Iteration: 10, Loss: <0.02468658589>
INFO: Iteration: 11, Loss: <0.0218236746963>
INFO: Iteration: 12, Loss: <0.0195449339706>
INFO: Iteration: 13, Loss: <0.0176893779887>
INFO: Iteration: 14, Loss: <0.0161499639458>
INFO: Iteration: 15, Loss: <0.0148528011871>
INFO: Iteration: 16, Loss: <0.0137452714235>
INFO: Iteration: 17, Loss: <0.0127889068292>
INFO: Iteration: 18, Loss: <0.0119549448347>
INFO: Iteration: 19, Loss: <0.011221456629>
INFO: Iteration: 20, Loss: <0.0105714364345>
INFO: Iteration: 21, Loss: <0.00999149680482>
INFO: Iteration: 22, Loss: <0.00947095724551>
INFO: Iteration: 23, Loss: <0.00900119461623>
INFO: Iteration: 24, Loss: <0.00857517170977>
-[ RECORD 1 ]------+-
mlp_classification |
madlib=# SELECT madlib.mlp_classification(
madlib(# 'iris_data', -- Source table
madlib(# 'mlp_model', -- Destination table
madlib(# 'attributes', -- Input features
madlib(# 'class_text', -- Label
madlib(# ARRAY[5], -- Number of units per layer
madlib(# 'learning_rate_init=0.003,
madlib'# n_iterations=25,
madlib'# tolerance=0', -- Optimizer params
madlib(# 'tanh', -- Activation function
madlib(# NULL, -- Default weight (1)
madlib(# TRUE, -- Warm start
madlib(# TRUE -- Not verbose
madlib(# );
INFO: Iteration: 1, Loss: <0.00783212719879>
INFO: Iteration: 2, Loss: <0.00754868929356>
INFO: Iteration: 3, Loss: <0.00724537071819>
INFO: Iteration: 4, Loss: <0.00696499503481>
INFO: Iteration: 5, Loss: <0.0067052209318>
INFO: Iteration: 6, Loss: <0.00646387227329>
INFO: Iteration: 7, Loss: <0.00623906734339>
INFO: Iteration: 8, Loss: <0.00602917134597>
INFO: Iteration: 9, Loss: <0.00583275728324>
INFO: Iteration: 10, Loss: <0.00564857402972>
INFO: Iteration: 11, Loss: <0.00547552010753>
INFO: Iteration: 12, Loss: <0.0053126220109>
INFO: Iteration: 13, Loss: <0.00515901618608>
INFO: Iteration: 14, Loss: <0.00501393396817>
INFO: Iteration: 15, Loss: <0.00487668892432>
INFO: Iteration: 16, Loss: <0.00474666616715>
INFO: Iteration: 17, Loss: <0.00462331328983>
INFO: Iteration: 18, Loss: <0.00450613264339>
INFO: Iteration: 19, Loss: <0.00439467473002>
INFO: Iteration: 20, Loss: <0.00428853252899>
INFO: Iteration: 21, Loss: <0.00418733660521>
INFO: Iteration: 22, Loss: <0.00409075087755>
INFO: Iteration: 23, Loss: <0.00399846894544>
INFO: Iteration: 24, Loss: <0.00391021088973>
-[ RECORD 1 ]------+-
mlp_classification |
madlib=# SELECT madlib.mlp_classification(
'iris_data', -- Source table
'mlp_model', -- Destination table
'attributes', -- Input features
'class_text', -- Label
ARRAY[5], -- Number of units per layer
'learning_rate_init=0.003,
n_iterations=25,
tolerance=0', -- Optimizer params
'tanh', -- Activation function
NULL, -- Default weight (1)
TRUE, -- Warm start
TRUE -- Not verbose
);
INFO: Iteration: 1, Loss: <0.00374476271638>
INFO: Iteration: 2, Loss: <0.00367708783353>
INFO: Iteration: 3, Loss: <0.00360220992802>
INFO: Iteration: 4, Loss: <0.00353024687379>
INFO: Iteration: 5, Loss: <0.00346107072758>
INFO: Iteration: 6, Loss: <0.00339452362577>
INFO: Iteration: 7, Loss: <0.00333045921955>
INFO: Iteration: 8, Loss: <0.00326874179276>
INFO: Iteration: 9, Loss: <0.00320924531599>
INFO: Iteration: 10, Loss: <0.0031518525991>
INFO: Iteration: 11, Loss: <0.00309645453113>
INFO: Iteration: 12, Loss: <0.00304294939712>
INFO: Iteration: 13, Loss: <0.00299124226312>
INFO: Iteration: 14, Loss: <0.00294124442135>
INFO: Iteration: 15, Loss: <0.00289287288902>
INFO: Iteration: 16, Loss: <0.00284604995461>
INFO: Iteration: 17, Loss: <0.00280070276667>
INFO: Iteration: 18, Loss: <0.0027567629604>
INFO: Iteration: 19, Loss: <0.0027141663181>
INFO: Iteration: 20, Loss: <0.00267285246002>
INFO: Iteration: 21, Loss: <0.00263276456234>
INFO: Iteration: 22, Loss: <0.00259384909969>
INFO: Iteration: 23, Loss: <0.00255605560962>
INFO: Iteration: 24, Loss: <0.00251933647706>
-[ RECORD 1 ]------+-
mlp_classification |
madlib=# SELECT madlib.mlp_classification(
'iris_data', -- Source table
'mlp_model', -- Destination table
'attributes', -- Input features
'class_text', -- Label
ARRAY[5], -- Number of units per layer
'learning_rate_init=0.003,
n_iterations=25,
tolerance=0', -- Optimizer params
'tanh', -- Activation function
NULL, -- Default weight (1)
TRUE, -- Warm start
TRUE -- Not verbose
);
INFO: Iteration: 1, Loss: <0.00244894389095>
INFO: Iteration: 2, Loss: <0.00241947565705>
INFO: Iteration: 3, Loss: <0.00238653134239>
INFO: Iteration: 4, Loss: <0.00235444586402>
INFO: Iteration: 5, Loss: <0.0023232030616>
INFO: Iteration: 6, Loss: <0.00229277038229>
INFO: Iteration: 7, Loss: <0.00226311685617>
INFO: Iteration: 8, Loss: <0.00223421306776>
INFO: Iteration: 9, Loss: <0.00220603106015>
INFO: Iteration: 10, Loss: <0.00217854424586>
INFO: Iteration: 11, Loss: <0.0021517273241>
INFO: Iteration: 12, Loss: <0.00212555620409>
INFO: Iteration: 13, Loss: <0.00210000793359>
INFO: Iteration: 14, Loss: <0.00207506063261>
INFO: Iteration: 15, Loss: <0.00205069343161>
INFO: Iteration: 16, Loss: <0.0020268864139>
INFO: Iteration: 17, Loss: <0.00200362056202>
INFO: Iteration: 18, Loss: <0.00198087770756>
INFO: Iteration: 19, Loss: <0.00195864048445>
INFO: Iteration: 20, Loss: <0.0019368922852>
INFO: Iteration: 21, Loss: <0.00191561721998>
INFO: Iteration: 22, Loss: <0.00189480007833>
INFO: Iteration: 23, Loss: <0.00187442629334>
INFO: Iteration: 24, Loss: <0.00185448190796>
-[ RECORD 1 ]------+-
mlp_classification |
{code}
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