diff options
| -rw-r--r-- | tensorflow/docs_src/guide/premade_estimators.md | 2 | ||||
| -rw-r--r-- | tensorflow/python/estimator/estimator.py | 6 |
2 files changed, 7 insertions, 1 deletions
diff --git a/tensorflow/docs_src/guide/premade_estimators.md b/tensorflow/docs_src/guide/premade_estimators.md index a1703058c3..9b64d51b98 100644 --- a/tensorflow/docs_src/guide/premade_estimators.md +++ b/tensorflow/docs_src/guide/premade_estimators.md @@ -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. diff --git a/tensorflow/python/estimator/estimator.py b/tensorflow/python/estimator/estimator.py index f7ee42c7f6..bcbd7b7933 100644 --- a/tensorflow/python/estimator/estimator.py +++ b/tensorflow/python/estimator/estimator.py @@ -431,7 +431,11 @@ class Estimator(object): 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`. |