The tf_iris_softmax_model was created with each input variable mapped to a single input tensor. Since the names of the input tensors match column names that exist in the iris_test table you do not need to use the ModelInputFieldsMap argument and the model tensor names are automatically set to the matching column names.
select * from mldb.ONNXPredict( on (select * from iris_test) on (select * from onnx_models where model_id='tf_iris_softmax_model') DIMENSION using Accumulate('id') ) as td ; *** Query completed. 30 rows found. 2 columns returned. *** Total elapsed time was 1 second.
id json_report ---------- --------------------------------------------------------------------- 10 {"probability":[[0.9998599,1.4007483E-4,4.8622266E-11]]} 65 {"probability":[[3.912328E-4,0.9988294,7.793538E-4]]} 15 {"probability":[[0.9999999,1.7266561E-7,1.2621994E-13]]} 5 {"probability":[[0.999992,8.021199E-6,2.2980238E-12]]} 30 {"probability":[[0.99968386,3.1617365E-4,9.13303E-11]]} 20 {"probability":[[0.99999213,7.901898E-6,2.2734618E-12]]} 40 {"probability":[[0.9999683,3.1726304E-5,1.782701E-11]]} 70 {"probability":[[3.5515255E-5,0.9994023,5.62249E-4]]} 60 {"probability":[[3.0097424E-4,0.6842745,0.3154245]]} 80 {"probability":[[3.0982963E-4,0.9995832,1.070616E-4]]} 45 {"probability":[[0.9997352,2.6480303E-4,1.7095797E-11]]} 75 {"probability":[[6.3532675E-6,0.999877,1.16734125E-4]]} 120 {"probability":[[2.2099151E-8,0.023814192,0.97618586]]} 85 {"probability":[[1.0646227E-4,0.64747274,0.35242087]]} 115 {"probability":[[8.84089E-12,1.13627166E-7,0.9999999]]} 55 {"probability":[[2.3626533E-6,0.9896403,0.010357335]]} 125 {"probability":[[5.0688627E-9,0.0036252008,0.9963748]]} 90 {"probability":[[6.0868737E-5,0.9305012,0.06943793]]} 95 {"probability":[[4.43892E-5,0.98495364,0.015002]]} 100 {"probability":[[5.467016E-5,0.99671644,0.0032288947]]} 130 {"probability":[[2.5530914E-8,0.9915139,0.008486167]]} 135 {"probability":[[1.6936058E-7,0.55152786,0.44847193]]} 140 {"probability":[[4.6395296E-9,0.004772966,0.995227]]} 25 {"probability":[[0.99771667,0.0022833364,2.1570362E-11]]} 110 {"probability":[[5.827173E-11,8.144908E-5,0.9999186]]} 145 {"probability":[[1.7500341E-11,3.8031712E-6,0.9999962]]} 150 {"probability":[[2.2181743E-7,0.011317454,0.9886824]]} 35 {"probability":[[0.999882,1.17950236E-4,2.4415578E-10]]} 50 {"probability":[[0.99997914,2.0821832E-5,3.7522135E-11]]} 105 {"probability":[[3.3467944E-11,2.2144119E-5,0.9999778]]}