public List<Map<String, INDArray>> output(MultiDataSetIterator iterator, String... outputs){
        Preconditions.checkState(trainingConfig != null, "Training config has not been set");

        List<String> reqVars;

        if(outputs != null){
            reqVars = Arrays.asList(outputs);
        } else {
            reqVars = outputs();
        }

        List<Map<String, INDArray>> predictions = new ArrayList<>();

        if(!iterator.hasNext() && iterator.resetSupported())
            iterator.reset();

        while(iterator.hasNext()){
            MultiDataSet ds = iterator.next();
            Map<String,INDArray> placeholderMap = toPlaceholderMap(ds);

            predictions.add(exec(placeholderMap, reqVars));
        }

        return predictions;
    }
public List < Map < String , INDArray > > output ( MultiDataSetIterator iterator , String ... outputs ) { Preconditions . checkState ( trainingConfig != null , "Training config has not been set" ) ; List < String > reqVars ; if ( outputs != null ) { reqVars = Arrays . asList ( outputs ) ; } else { reqVars = outputs ( ) ; } List < Map < String , INDArray > > predictions = new ArrayList < > ( ) ; if ( ! iterator . hasNext ( ) && iterator . resetSupported ( ) ) iterator . reset ( ) ; while ( iterator . hasNext ( ) ) { MultiDataSet ds = iterator . next ( ) ; Map < String , INDArray > placeholderMap = toPlaceholderMap ( ds ) ; predictions . add ( exec ( placeholderMap , reqVars ) ) ; } return predictions ; }
Perform inference.<br>
<br>
<b>Example: classifier inference</b><br>
Predictions variable name: "softmaxOutput"<br>
Evaluations to perform: {@link org.nd4j.evaluation.classification.Evaluation}<br>
Data: single output MultiDataSets<br>
Code:<br>
<pre>
{@code
MultiDataSetIterator data = ...
sameDiff.output(iterator, "softmaxOutput);
}
</pre>

@param iterator  The iterator - the source of the data for inference
@param outputs   The set of outputs to report.  If null, defaults to all outputs of this SameDiff.