kaknikhil commented on a change in pull request #393: DL: Add support for
reporting various metrics in fit/evaluate
URL: https://github.com/apache/madlib/pull/393#discussion_r285699186
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File path: src/ports/postgres/modules/deep_learning/madlib_keras.py_in
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@@ -342,23 +330,24 @@ def compute_loss_and_metrics(schema_madlib, table,
dependent_varname,
given dataset (table.)
"""
start_val = time.time()
- evaluate_result = get_loss_acc_from_keras_eval(schema_madlib,
- table,
- dependent_varname,
- independent_varname,
- compile_params,
- model_arch, model_state,
- gpus_per_host,
- segments_per_host,
- seg_ids_val,
- rows_per_seg_val,
- gp_segment_id_col)
+ evaluate_result = get_loss_metric_from_keras_eval(schema_madlib,
+ table,
+ dependent_varname,
+ independent_varname,
+ compile_params,
+ model_arch,
+ model_state,
+ gpus_per_host,
+ segments_per_host,
+ seg_ids_val,
+ rows_per_seg_val,
+ gp_segment_id_col)
end_val = time.time()
plpy.info("Time for evaluation in iteration {0}: {1} sec.". format(
curr_iter, end_val - start_val))
- if len(evaluate_result) < 2:
- plpy.error('Calling evaluate on table {0} returned < 2 '
- 'metrics. Expected both loss and a metric.'.format(
+ if len(evaluate_result) not in [1, 2]:
Review comment:
Since metric can be an optional param, isn't it possible that keras.eval
only returns the loss ?
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