njayaram2 commented on a change in pull request #388: DL: Add new param
metrics_compute_frequency to madlib_keras_fit()
URL: https://github.com/apache/madlib/pull/388#discussion_r283447550
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File path: src/ports/postgres/modules/deep_learning/madlib_keras.py_in
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@@ -313,6 +316,61 @@ def fit(schema_madlib, source_table,
model,model_arch_table,
#TODO add a unit test for this in a future PR
reset_cuda_env(original_cuda_env)
+def compute_loss_and_metrics(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, metrics_list, loss_list,
+ curr_iter, dataset_name):
+ """
+ Compute the loss and metric using a given model (model_state) on the
+ 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)
+ end_val = time.time()
+ plpy.info("Time for evaluation in iteration {0}: {1} sec.". format(
+ curr_iter + 1, 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(
+ table))
+ loss = evaluate_result[0]
+ metric = evaluate_result[1]
+ plpy.info("{0} set metric after iteration {1}: {2}.".
+ format(dataset_name, curr_iter + 1, metric))
+ plpy.info("{0} set loss after iteration {1}: {2}.".
+ format(dataset_name, curr_iter + 1, loss))
+ metrics_list.append(metric)
+ loss_list.append(loss)
+
+def should_compute_metrics_this_iter(
Review comment:
Will change it in the fit function too, it will be inconsistent otherwise.
For instance, all the prints we have do an `i+1` while reporting numbers, and
`compute_loss_and_metrics` also assumes zero indexed `i` value. Will instead
run the for loop in `range(1, num_iterations+1)` and make it consistent
everywhere.
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