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]]}
...