ONNXPredict Example with ModelInputFieldsMap | Vantage BYOM - ONNXPredict Example: With ModelInputFieldsMap - Teradata Vantage

The input model, tf_iris_softmax_onetensor_model, was created by using an input array of four float32 values and named float_input. Since this does not match one-to-one with the input table columns, you must use ModelInputFieldsMap to define the columns, order of the input tensor, and make sure they match the order used when generating the model.

select * from mldb.ONNXPredict(
    on iris_test    
    on (select * from onnx_models where model_id='tf_iris_softmax_onetensor_model') DIMENSION
    using
        Accumulate('id')
        ModelInputFieldsMap('float_input=sepal_length, sepal_width, petal_length, petal_width')
) 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]]}