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Diffstat (limited to 'tensorflow/docs_src/guide/estimators.md')
| -rw-r--r-- | tensorflow/docs_src/guide/estimators.md | 7 |
1 files changed, 5 insertions, 2 deletions
diff --git a/tensorflow/docs_src/guide/estimators.md b/tensorflow/docs_src/guide/estimators.md index ed9a3da284..7b54e3de29 100644 --- a/tensorflow/docs_src/guide/estimators.md +++ b/tensorflow/docs_src/guide/estimators.md @@ -13,6 +13,9 @@ You may either use the pre-made Estimators we provide or write your own custom Estimators. All Estimators--whether pre-made or custom--are classes based on the `tf.estimator.Estimator` class. +For a quick example try [Estimator tutorials]](../tutorials/estimators/linear). +To see each sub-topic in depth, see the [Estimator guides](premade_estimators). + Note: TensorFlow also includes a deprecated `Estimator` class at `tf.contrib.learn.Estimator`, which you should not use. @@ -29,14 +32,14 @@ Estimators provide the following benefits: * You can develop a state of the art model with high-level intuitive code. In short, it is generally much easier to create models with Estimators than with the low-level TensorFlow APIs. -* Estimators are themselves built on `tf.layers`, which +* Estimators are themselves built on `tf.keras.layers`, which simplifies customization. * Estimators build the graph for you. * Estimators provide a safe distributed training loop that controls how and when to: * build the graph * initialize variables - * start queues + * load data * handle exceptions * create checkpoint files and recover from failures * save summaries for TensorBoard |