Github user edespino commented on the issue:

    https://github.com/apache/incubator-madlib/pull/149
  
    Not sure what this is. While playing around with the module, I accidentally 
hit a `nan` condition. Here is the output of the session:
    
    ```
    madlibtest=# CREATE TABLE iris_data(
    madlibtest(#     id integer,
    madlibtest(#     attributes numeric[],
    madlibtest(#     class_text varchar,
    madlibtest(#     class integer
    madlibtest(# );
    CREATE TABLE
    madlibtest=# INSERT INTO iris_data VALUES
    madlibtest-# (1,ARRAY[5.1,3.5,1.4,0.2],'Iris-setosa',1);
    INSERT 0 1
    madlibtest=# SELECT madlib.mlp_classification(
    madlibtest(#     'iris_data',      -- Source table
    madlibtest(#     'mlp_model',      -- Destination table
    madlibtest(#     'attributes',     -- Input features
    madlibtest(#     'class_text',     -- Label
    madlibtest(#     ARRAY[5],         -- Number of units per layer
    madlibtest(#     'step_size=0.003,
    madlibtest'#     n_iterations=5000,
    madlibtest'#     tolerance=0',     -- Optimizer params
    madlibtest(#     'tanh');          -- Activation function
    WARNING:  loss: nan
    ERROR:  spiexceptions.UndefinedColumn: column "nan" does not exist
    LINE 4: ...7731e-05,-0.000170379656714,0.000110138191401,1.0,nan,1.0,1....
                                                                 ^
    QUERY:
                    SELECT madlib.internal_mlp_igd_distance(
                            (NULL)::DOUBLE PRECISION[],
                            ARRAY[ 
2.0,4.0,5.0,1.0,0.003,1.0,2.0,1.0,1.0,1.0,1.0,1.0,-0.000161677781273,2.34991541167e-05,-5.27816928274e-05,1.6207561377e-06,9.30167921009e-05,-2.2322484638e-06,-8.8803764219e-05,6.92299455941e-05,0.000174430298478,1.36067005437e-07,-0.000126402572755,0.00010846859258,-0.000131999662994,-0.000111939884066,0.000154233933791,9.62727960033e-05,7.92279598497e-05,3.55520197415e-05,4.28280053118e-05,-0.000136356574146,-3.07769363501e-05,-8.97131375537e-05,-0.000117176292437,-9.89281636713e-05,0.000234615355418,1.0,1.0,1.0,1.0,1.0,1.0,2.00514836476e-05,-9.37533114569e-05,7.58698692162e-05,-2.64470277731e-05,-0.000170379656714,0.000110138191401,1.0,nan,1.0,1.0,1.0,1.0,1.0,-0.000161677781273,2.34991541167e-05,-5.27816928274e-05,1.6207561377e-06,9.30167921009e-05,-2.2322484638e-06,-8.8803764219e-05,6.92299455941e-05,0.000174430298478,1.36067005437e-07,-0.000126402572755,0.00010846859258,-0.000131999662994,-0.000111939884066,0.000154233933791,9.62727960033e-05,7
 
.92279598497e-05,3.55520197415e-05,4.28280053118e-05,-0.000136356574146,-3.07769363501e-05,-8.97131375537e-05,-0.000117176292437,-9.89281636713e-05,0.000234615355418,1.0,1.0,1.0,1.0,1.0,1.0,2.00514836476e-05,-9.37533114569e-05,7.58698692162e-05,-2.64470277731e-05,-0.000170379656714,0.000110138191401,0.0,0.0
 ]::double precision[]) as state_dist
    
    CONTEXT:  Traceback (most recent call last):
      PL/Python function "mlp_classification", line 32, in <module>
        True
      PL/Python function "mlp_classification", line 140, in mlp
    PL/Python function "mlp_classification"
    madlibtest=#
    ```


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