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Diffstat (limited to 'tensorflow/docs_src/guide/premade_estimators.md')
| -rw-r--r-- | tensorflow/docs_src/guide/premade_estimators.md | 18 |
1 files changed, 9 insertions, 9 deletions
diff --git a/tensorflow/docs_src/guide/premade_estimators.md b/tensorflow/docs_src/guide/premade_estimators.md index dc38f0c1d3..a1703058c3 100644 --- a/tensorflow/docs_src/guide/premade_estimators.md +++ b/tensorflow/docs_src/guide/premade_estimators.md @@ -8,7 +8,7 @@ how to solve the Iris classification problem in TensorFlow. Prior to using the sample code in this document, you'll need to do the following: -* @{$install$Install TensorFlow}. +* [Install TensorFlow](../install/index.md). * If you installed TensorFlow with virtualenv or Anaconda, activate your TensorFlow environment. * Install or upgrade pandas by issuing the following command: @@ -78,10 +78,10 @@ provides a programming stack consisting of multiple API layers: We strongly recommend writing TensorFlow programs with the following APIs: -* @{$guide/estimators$Estimators}, which represent a complete model. +* [Estimators](../guide/estimators.md), which represent a complete model. The Estimator API provides methods to train the model, to judge the model's accuracy, and to generate predictions. -* @{$guide/datasets_for_estimators}, which build a data input +* [Datasets for Estimators](../guide/datasets_for_estimators.md), which build a data input pipeline. The Dataset API has methods to load and manipulate data, and feed it into your model. The Dataset API meshes well with the Estimators API. @@ -173,14 +173,14 @@ example is an Iris Versicolor. An Estimator is TensorFlow's high-level representation of a complete model. It handles the details of initialization, logging, saving and restoring, and many other features so you can concentrate on your model. For more details see -@{$guide/estimators}. +[Estimators](../guide/estimators.md). An Estimator is any class derived from `tf.estimator.Estimator`. TensorFlow provides a collection of `tf.estimator` (for example, `LinearRegressor`) to implement common ML algorithms. Beyond those, you may write your own -@{$custom_estimators$custom Estimators}. +[custom Estimators](../guide/custom_estimators.md). We recommend using pre-made Estimators when just getting started. To write a TensorFlow program based on pre-made Estimators, you must perform the @@ -287,7 +287,7 @@ for key in train_x.keys(): ``` Feature columns can be far more sophisticated than those we're showing here. We -detail feature columns @{$feature_columns$later on} in our Getting +detail feature columns [later on](../guide/feature_columns.md) in our Getting Started guide. Now that we have the description of how we want the model to represent the raw @@ -423,8 +423,8 @@ Pre-made Estimators are an effective way to quickly create standard models. Now that you've gotten started writing TensorFlow programs, consider the following material: -* @{$checkpoints$Checkpoints} to learn how to save and restore models. -* @{$guide/datasets_for_estimators} to learn more about importing +* [Checkpoints](../guide/checkpoints.md) to learn how to save and restore models. +* [Datasets for Estimators](../guide/datasets_for_estimators.md) to learn more about importing data into your model. -* @{$custom_estimators$Creating Custom Estimators} to learn how to +* [Creating Custom Estimators](../guide/custom_estimators.md) to learn how to write your own Estimator, customized for a particular problem. |