`metrics` could be any python object. If users use NNI built-in tuner/assessor, `metrics` can only have two formats: 1) a number e.g., float, int, 2) a dict object that has a key named `default` whose value is a number. This `metrics` is reported to [assessor](Builtin_Assessors.md). Usually, `metrics` could be periodically evaluated loss or accuracy.
`metrics` could be any python object. If users use NNI built-in tuner/assessor, `metrics` can only have two formats: 1) a number e.g., float, int, 2) a dict object that has a key named `default` whose value is a number. This `metrics` is reported to [assessor](BuiltinAssessors.md). Usually, `metrics` could be periodically evaluated loss or accuracy.
- Report performance of the configuration
```python
nni.report_final_result(metrics)
```
`metrics` also could be any python object. If users use NNI built-in tuner/assessor, `metrics` follows the same format rule as that in `report_intermediate_result`, the number indicates the model's performance, for example, the model's accuracy, loss etc. This `metrics` is reported to [tuner](Builtin_Tuner.md).
`metrics` also could be any python object. If users use NNI built-in tuner/assessor, `metrics` follows the same format rule as that in `report_intermediate_result`, the number indicates the model's performance, for example, the model's accuracy, loss etc. This `metrics` is reported to [tuner](BuiltinTuner.md).
### Step 3 - Enable NNI API
...
...
@@ -156,8 +156,8 @@ For more information, please refer to [HowToDebug](HowToDebug.md)
<aname="more-examples"></a>
## More Trial Examples
*[MNIST examples](mnist_examples.md)
*[Finding out best optimizer for Cifar10 classification](cifar10_examples.md)
*[How to tune Scikit-learn on NNI](sklearn_examples.md)
*[Automatic Model Architecture Search for Reading Comprehension.](SQuAD_evolution_examples.md)
*[Tuning GBDT on NNI](gbdt_example.md)
*[MNIST examples](MnistExamples.md)
*[Finding out best optimizer for Cifar10 classification](Cifar10Examples.md)
*[How to tune Scikit-learn on NNI](SklearnExamples.md)
*[Automatic Model Architecture Search for Reading Comprehension.](SquadEvolutionExamples.md)
