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Add more doc for the dict returned by an estimator's evaluate() method #21753

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Aug 23, 2018
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Add more doc for the dict returned by an estimator's evaluate() method #21753

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Add more doc for the dict returned by an estimator's evaluate() method
ageron Aug 21, 2018
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Add more doc for the dict returned by an estimator's evaluate() metho…
ageron Aug 21, 2018
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2 changes: 2 additions & 0 deletions tensorflow/docs_src/guide/premade_estimators.md
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Expand Up @@ -366,6 +366,8 @@ Running this code yields the following output (or something similar):
Test set accuracy: 0.967
```

The `eval_result` dictionary also contains the `average_loss` (mean loss per sample), the `loss` (mean loss per mini-batch) and the value of the estimator's `global_step` (the number of training iterations it underwent).

### Making predictions (inferring) from the trained model

We now have a trained model that produces good evaluation results.
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6 changes: 5 additions & 1 deletion tensorflow/python/estimator/estimator.py
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Expand Up @@ -431,7 +431,11 @@ def evaluate(self, input_fn, steps=None, hooks=None, checkpoint_path=None,
Returns:
A dict containing the evaluation metrics specified in `model_fn` keyed by
name, as well as an entry `global_step` which contains the value of the
global step for which this evaluation was performed.
global step for which this evaluation was performed. For canned
estimators, the dict contains the `loss` (mean loss per mini-batch) and
the `average_loss` (mean loss per sample). Canned classifiers also return
the `accuracy`. Canned regressors also return the `label/mean` and the
`prediction/mean`.

Raises:
ValueError: If `steps <= 0`.
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