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Posted to dev@madlib.apache.org by GitBox <gi...@apache.org> on 2019/12/13 18:52:00 UTC

[GitHub] [madlib] fmcquillan99 commented on issue #463: DL: Add asymmetric cluster check for predict

fmcquillan99 commented on issue #463: DL: Add asymmetric cluster check for predict
URL: https://github.com/apache/madlib/pull/463#issuecomment-565563051
 
 
   seeing an evaluate error that I don't remember seeing before...
   ```
   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
                                                  6,                     -- num_iterations
                                                  FALSE,                 -- use gpus
                                                 'iris_test_packed',     -- validation dataset
                                                  2,                     -- metrics compute frequency
                                                  FALSE,                 -- warm start
                                                 'Sophie L.',            -- name
                                                 'Simple MLP for iris dataset'  -- description
                                                );
   
   
   InternalError: (psycopg2.InternalError) plpy.SPIError: plpy.Error: Number of elements in model weights(303) doesn't match model(193). (plpython.c:5038)  (seg0 slice1 10.128.0.41:40000 pid=32019) (plpython.c:5038)
   DETAIL:  
   Traceback (most recent call last):
     PL/Python function "internal_keras_eval_transition", line 6, in <module>
       return madlib_keras.internal_keras_eval_transition(**globals())
     PL/Python function "internal_keras_eval_transition", line 727, in internal_keras_eval_transition
     PL/Python function "internal_keras_eval_transition", line 125, in set_model_weights
     PL/Python function "internal_keras_eval_transition", line 167, in deserialize_as_nd_weights
     PL/Python function "internal_keras_eval_transition", line 96, in _assert
   PL/Python function "internal_keras_eval_transition"
   CONTEXT:  Traceback (most recent call last):
     PL/Python function "madlib_keras_fit_multiple_model", line 21, in <module>
       fit_obj = madlib_keras_fit_multiple_model.FitMultipleModel(**globals())
     PL/Python function "madlib_keras_fit_multiple_model", line 42, in wrapper
     PL/Python function "madlib_keras_fit_multiple_model", line 167, in __init__
     PL/Python function "madlib_keras_fit_multiple_model", line 174, in fit_multiple_model
     PL/Python function "madlib_keras_fit_multiple_model", line 200, in train_multiple_model
     PL/Python function "madlib_keras_fit_multiple_model", line 229, in evaluate_model
     PL/Python function "madlib_keras_fit_multiple_model", line 409, in compute_loss_and_metrics
     PL/Python function "madlib_keras_fit_multiple_model", line 691, in get_loss_metric_from_keras_eval
   PL/Python function "madlib_keras_fit_multiple_model"
    [SQL: "SELECT madlib.madlib_keras_fit_multiple_model('iris_train_packed',    -- source_table\n                                              'iris_multi_model',     -- model_output_table\n                                              'mst_table',            -- model_selection_table\n                                               6,                     -- num_iterations\n                                               FALSE,                 -- use gpus\n                                              'iris_test_packed',     -- validation dataset\n                                               2,                     -- metrics compute frequency\n                                               FALSE,                 -- warm start\n                                              'Sophie L.',            -- name\n                                              'Simple MLP for iris dataset'  -- description\n                                             );"]
   ```

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