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Posted to dev@madlib.apache.org by GitBox <gi...@apache.org> on 2019/06/03 17:33:21 UTC
[GitHub] [madlib] fmcquillan99 commented on issue #402: DL: Enable warm start
fmcquillan99 commented on issue #402: DL: Enable warm start
URL: https://github.com/apache/madlib/pull/402#issuecomment-498352780
(1)
train for 3 iterations
```
DROP TABLE IF EXISTS iris_model, iris_model_summary;
SELECT madlib.madlib_keras_fit('iris_train_packed', -- source table
'iris_model', -- model output table
'model_arch_library', -- model arch table
1, -- model arch id
$$ loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'] $$, -- compile_params
$$ batch_size=5, epochs=3 $$, -- fit_params
3, -- num_iterations
0, -- GPUs per host
'iris_test_packed', -- validation dataset
1 -- metrics compute frequency
);
SELECT * FROM iris_model_summary;
-[ RECORD 1 ]-------------+--------------------------------------------------------------------------
source_table | iris_train_packed
model | iris_model
dependent_varname | class_text
independent_varname | attributes
model_arch_table | model_arch_library
model_arch_id | 1
compile_params | loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy']
fit_params | batch_size=5, epochs=3
num_iterations | 3
validation_table | iris_test_packed
metrics_compute_frequency | 1
name |
description |
model_type | madlib_keras
model_size | 0.7900390625
start_training_time | 2019-06-03 17:18:58.692221
end_training_time | 2019-06-03 17:19:04.165636
metrics_elapsed_time | {2.97064995765686,4.35413503646851,5.47341108322144}
madlib_version | 1.16-dev
num_classes | 3
class_values | {Iris-setosa,Iris-versicolor,Iris-virginica}
dependent_vartype | character varying
normalizing_const | 1
metrics_type | {accuracy}
training_metrics_final | 0.833333313465
training_loss_final | 0.578077852726
training_metrics | {0.666666686534882,0.708333313465118,0.833333313465118}
training_loss | {0.75943660736084,0.655552685260773,0.578077852725983}
validation_metrics_final | 0.833333313465
validation_loss_final | 0.574034750462
validation_metrics | {0.666666686534882,0.666666686534882,0.833333313465118}
validation_loss | {0.756515920162201,0.653064906597137,0.574034750461578}
metrics_iters | {1,2,3}
```
(2)
do warm start 2 times
```
SELECT madlib.madlib_keras_fit('iris_train_packed', -- source table
'iris_model', -- model output table
'model_arch_library', -- model arch table
1, -- model arch id
$$ loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'] $$, -- compile_params
$$ batch_size=5, epochs=3 $$, -- fit_params
3, -- num_iterations
0, -- GPUs per host
'iris_test_packed', -- validation dataset
1, -- metrics compute frequency
TRUE -- warm start
);
```
from orig fit
```
training_metrics_final | 0.833333313465
training_loss_final | 0.578077852726
training_metrics | {0.666666686534882,0.708333313465118,0.833333313465118}
training_loss | {0.75943660736084,0.655552685260773,0.578077852725983}
validation_metrics_final | 0.833333313465
validation_loss_final | 0.574034750462
validation_metrics | {0.666666686534882,0.666666686534882,0.833333313465118}
validation_loss | {0.756515920162201,0.653064906597137,0.574034750461578}
metrics_iters | {1,2,3}
```
warm start 1:
```
training_metrics_final | 0.891666650772
training_loss_final | 0.430346846581
training_metrics | {0.841666638851166,0.899999976158142,0.891666650772095}
training_loss | {0.514156401157379,0.466687709093094,0.430346846580505}
validation_metrics_final | 0.966666638851
validation_loss_final | 0.421454459429
validation_metrics | {0.866666674613953,0.966666638851166,0.966666638851166}
validation_loss | {0.509902536869049,0.459847092628479,0.421454459428787}
metrics_iters | {1,2,3}
```
warm start 2:
```
training_metrics_final | 0.941666662693
training_loss_final | 0.362128138542
training_metrics | {0.933333337306976,0.941666662693024,0.941666662693024}
training_loss | {0.402873665094376,0.381914019584656,0.362128138542175}
validation_metrics_final | 1
validation_loss_final | 0.345562160015
validation_metrics | {1,1,1}
validation_loss | {0.391709893941879,0.367107719182968,0.345562160015106}
metrics_iters | {1,2,3}
```
OK, looks like both metric and loss carry on from where they left off
(3)
warm start and change model_id
```
SELECT madlib.madlib_keras_fit('iris_train_packed', -- source table
'iris_model', -- model output table
'model_arch_library', -- model arch table
2, -- model arch id
$$ loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'] $$, -- compile_params
$$ batch_size=5, epochs=3 $$, -- fit_params
3, -- num_iterations
0, -- GPUs per host
'iris_test_packed', -- validation dataset
1, -- metrics compute frequency
TRUE -- warm start
);
training_metrics_final | 0.958333313465
training_loss_final | 0.313535839319
training_metrics | {0.958333313465118,0.958333313465118,0.958333313465118}
training_loss | {0.345191985368729,0.328865617513657,0.313535839319229}
validation_metrics_final | 1
validation_loss_final | 0.2869117558
validation_metrics | {1,1,1}
validation_loss | {0.325278729200363,0.306510925292969,0.286911755800247}
metrics_iters | {1,2,3}
```
OK, carries on from where it left off
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