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Diffstat (limited to 'tensorflow/docs_src')
| -rw-r--r-- | tensorflow/docs_src/guide/premade_estimators.md | 2 | ||||
| -rw-r--r-- | tensorflow/docs_src/guide/saved_model.md | 2 |
2 files changed, 3 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/docs_src/guide/saved_model.md b/tensorflow/docs_src/guide/saved_model.md index 6c967fd882..33ab891861 100644 --- a/tensorflow/docs_src/guide/saved_model.md +++ b/tensorflow/docs_src/guide/saved_model.md @@ -2,7 +2,7 @@ The `tf.train.Saver` class provides methods to save and restore models. The `tf.saved_model.simple_save` function is an easy way to build a -`tf.saved_model` suitable for serving. [Estimators](./estimators) +`tf.saved_model` suitable for serving. [Estimators](../guide/estimators.md) automatically save and restore variables in the `model_dir`. ## Save and restore variables |