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Orhan Kislal updated MADLIB-1482: --------------------------------- Fix Version/s: v1.22.0 (was: v1.20.0) > DL: metrics compute frequency should be >=1 only > ------------------------------------------------ > > Key: MADLIB-1482 > URL: https://issues.apache.org/jira/browse/MADLIB-1482 > Project: Apache MADlib > Issue Type: Bug > Components: Deep Learning > Reporter: Frank McQuillan > Priority: Minor > Fix For: v1.22.0 > > > metrics_compute_frequency should be >= 1 only. Currently it allows neg > numbers: > {code} > SELECT madlib.madlib_keras_fit('balanced2_train_packed', -- source table > 'model1', -- model output table > 'model_arch_library', -- model arch table > 1, -- model arch id > $$ loss='categorical_crossentropy', > optimizer='adam', metrics=['accuracy'] $$, -- compile_params > $$ batch_size=64, epochs=1 $$, -- fit_params > 10, -- num_iterations > FALSE, -- use GPUs > 'balanced2_test_packed', -- validation > dataset > -3, -- metrics compute > frequency > FALSE, -- warm start > 'Frank', -- name > 'Network test run' -- description > ); > {code} > produces > {code} > SELECT * FROM model1_summary; > -[ RECORD 1 > ]-------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- > source_table | balanced2_train_packed > model | model1 > dependent_varname | {y} > independent_varname | {feature_vector} > model_arch_table | model_arch_library > model_id | 1 > compile_params | loss='categorical_crossentropy', > optimizer='adam', metrics=['accuracy'] > fit_params | batch_size=64, epochs=1 > num_iterations | 10 > validation_table | balanced2_test_packed > object_table | > metrics_compute_frequency | -3 > name | Frank > description | Network test run > model_type | madlib_keras > model_size | 5.9853515625 > start_training_time | 2021-03-12 20:24:27.74585 > end_training_time | 2021-03-12 20:24:30.012898 > metrics_elapsed_time | > {1.13839101791382,1.57564496994019,2.02039790153503,2.26697182655334} > madlib_version | 1.18.0-dev > num_classes | {23} > dependent_vartype | {text} > normalizing_const | 1 > metrics_type | {accuracy} > loss_type | categorical_crossentropy > training_metrics_final | 0.529687523841858 > training_loss_final | 470.371795654297 > training_metrics | > {0.283894240856171,0.344591349363327,0.489182680845261,0.529687523841858} > training_loss | > {3195.37939453125,1194.63610839844,508.576507568359,470.371795654297} > validation_metrics_final | 0.52836537361145 > validation_loss_final | 11892.33203125 > validation_metrics | > {0.289903849363327,0.35432693362236,0.482692301273346,0.52836537361145} > validation_loss | > {35025.0390625,23250.08984375,12720.3359375,11892.33203125} > metrics_iters | {3,6,9,10} > y_class_values | > {class01,class02,class03,class04,class05,class06,class07,class08,class09,class10,class11,class12,class13,class14,class15,class16,class17,class18,class19,class20,class21,class22,normal} > {code} > Also if you make it 0 you get this cryptic error > {code} > InternalError: (psycopg2.errors.InternalError_) ZeroDivisionError: integer > division or modulo by zero (plpython.c:5038) > CONTEXT: Traceback (most recent call last): > PL/Python function "madlib_keras_fit", line 23, in <module> > madlib_keras.fit(**globals()) > PL/Python function "madlib_keras_fit", line 42, in wrapper > PL/Python function "madlib_keras_fit", line 273, in fit > PL/Python function "madlib_keras_fit", line 542, in > should_compute_metrics_this_iter > PL/Python function "madlib_keras_fit" > [SQL: SELECT madlib.madlib_keras_fit('balanced2_train_packed', -- source > table > 'model1', -- model output table > 'model_arch_library', -- model arch table > 1, -- model arch id > $$ loss='categorical_crossentropy', > optimizer='adam', metrics=['accuracy'] $$, -- compile_params > $$ batch_size=64, epochs=1 $$, -- fit_params > 10, -- num_iterations > FALSE, -- use GPUs > 'balanced2_test_packed', -- validation > dataset > 0, -- metrics compute > frequency > FALSE, -- warm start > 'Frank', -- name > 'Network test run' -- description > );] > {code} > fix in: > {code} > fit() > fit_multiple() > autoML() > {code} -- This message was sent by Atlassian Jira (v8.20.10#820010)