fmcquillan99 edited a comment on pull request #518:
URL: https://github.com/apache/madlib/pull/518#issuecomment-698586366
(4)
`hyperopt` is not learning with cifar10 in the tests I have done so far with
`rand`
```
SELECT madlib.madlib_keras_automl('cifar10_train_packed',
'automl_cifar10_output',
'model_arch_library',
'automl_cifar10_mst_table',
ARRAY[1,2,3],
$${'loss': ['categorical_crossentropy'],
'optimizer_params_list': [
{'optimizer': ['Adam'],'lr':
[0.0001, 0.01, 'log']},
{'optimizer': ['RMSprop'],'lr':
[0.0001, 0.01, 'log'], 'decay': [1.0e-7, 1.0e-5, 'log']},
{'optimizer': ['SGD'],'lr':
[0.0001, 0.01, 'log'], 'momentum': [0.9, 0.99,'log']}],
'metrics':['accuracy']}$$,
$${'batch_size': [64,128], 'epochs':
[1]}$$,
'hyperopt',
'num_models=3, num_iters=5,
algorithm=rand',
NULL, -- random state
NULL, -- object table
FALSE, -- use GPUs
'cifar10_val_packed', -- validation table
1, -- metrics compute
freq
NULL, -- name
NULL); -- descr
```
produces no-learning result with labels:
```
INFO: ***Evaluating 3 newly suggested model configurations***
CONTEXT: PL/Python function "madlib_keras_automl"
INFO:
Time for training in iteration 1: 659.287541866 sec
DETAIL:
Training set after iteration 1:
mst_key=3: metric=0.10000000149, loss=nan
mst_key=1: metric=0.0988999977708, loss=14.5240154266
mst_key=2: metric=0.10000000149, loss=nan
Validation set after iteration 1:
mst_key=3: metric=0.10000000149, loss=nan
mst_key=1: metric=0.0989999994636, loss=14.5224027634
mst_key=2: metric=0.10000000149, loss=nan
CONTEXT: PL/Python function "madlib_keras_automl"
INFO:
Time for training in iteration 2: 663.107329845 sec
DETAIL:
Training set after iteration 2:
mst_key=3: metric=0.10000000149, loss=nan
mst_key=1: metric=0.0989999994636, loss=14.5224027634
mst_key=2: metric=0.10000000149, loss=nan
Validation set after iteration 2:
mst_key=3: metric=0.10000000149, loss=nan
mst_key=1: metric=0.0989999994636, loss=14.5224027634
mst_key=2: metric=0.10000000149, loss=nan
CONTEXT: PL/Python function "madlib_keras_automl"
etc.
```
When I run `hyperband` with same models/hyperparams, it does learn:
```
SELECT madlib.madlib_keras_automl('cifar10_train_packed',
'automl_cifar10_output',
'model_arch_library',
'automl_cifar10_mst_table',
ARRAY[1,2,3],
$${'loss': ['categorical_crossentropy'],
'optimizer_params_list': [
{'optimizer': ['Adam'],'lr':
[0.0001, 0.01, 'log']},
{'optimizer': ['RMSprop'],'lr':
[0.0001, 0.01, 'log'], 'decay': [1.0e-7, 1.0e-5, 'log']},
{'optimizer': ['SGD'],'lr':
[0.0001, 0.01, 'log'], 'momentum': [0.9, 0.99,'log']}],
'metrics':['accuracy']}$$,
$${'batch_size': [64,128], 'epochs':
[1]}$$,
'hyperband',
'R=6, eta=3, skip_last=0',
NULL, -- random state
NULL, -- object table
FALSE, -- use GPUs
'cifar10_val_packed', -- validation table
1, -- metrics compute
freq
NULL, -- name
NULL); -- descr
```
produces
```
PL/Python function "madlib_keras_automl"
INFO:
Time for training in iteration 1: 655.647205114 sec
DETAIL:
Training set after iteration 1:
mst_key=3: metric=0.34889999032, loss=1.83796536922
mst_key=1: metric=0.468120008707, loss=1.481554389
mst_key=2: metric=0.475380003452, loss=1.63904440403
Validation set after iteration 1:
mst_key=3: metric=0.355800002813, loss=1.83082151413
mst_key=1: metric=0.470499992371, loss=1.47972774506
mst_key=2: metric=0.470200002193, loss=1.66988003254
CONTEXT: PL/Python function "madlib_keras_automl"
etc.
```
Also `hyperopt` with `tpe` is not learning cifar10
```
SELECT madlib.madlib_keras_automl('cifar10_train_packed',
'automl_cifar10_output',
'model_arch_library',
'automl_cifar10_mst_table',
ARRAY[1,2,3],
$${'loss': ['categorical_crossentropy'],
'optimizer_params_list': [
{'optimizer': ['Adam'],'lr':
[0.0001, 0.01, 'log']},
{'optimizer': ['RMSprop'],'lr':
[0.0001, 0.01, 'log'], 'decay': [1.0e-7, 1.0e-5, 'log']},
{'optimizer': ['SGD'],'lr':
[0.0001, 0.01, 'log'], 'momentum': [0.9, 0.99,'log']}],
'metrics':['accuracy']}$$,
$${'batch_size': [64,128], 'epochs':
[1]}$$,
'hyperopt',
'num_models=3, num_iters=5, algorithm=tpe',
NULL, -- random state
NULL, -- object table
FALSE, -- use GPUs
'cifar10_val_packed', -- validation table
1, -- metrics compute
freq
NULL, -- name
NULL); -- descr
```
produces
```
CONTEXT: PL/Python function "madlib_keras_automl"
INFO:
Time for training in iteration 1: 648.608314991 sec
DETAIL:
Training set after iteration 1:
mst_key=3: metric=0.10000000149, loss=nan
mst_key=1: metric=0.0999400019646, loss=14.5072526932
mst_key=2: metric=0.10000000149, loss=14.5062847137
Validation set after iteration 1:
mst_key=3: metric=0.10000000149, loss=nan
mst_key=1: metric=0.10000000149, loss=14.5062856674
mst_key=2: metric=0.10000000149, loss=14.5062847137
CONTEXT: PL/Python function "madlib_keras_automl"
```
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