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Diffstat (limited to 'tensorflow/docs_src/guide/feature_columns.md')
| -rw-r--r-- | tensorflow/docs_src/guide/feature_columns.md | 6 |
1 files changed, 3 insertions, 3 deletions
diff --git a/tensorflow/docs_src/guide/feature_columns.md b/tensorflow/docs_src/guide/feature_columns.md index b189c4334e..3ad41855e4 100644 --- a/tensorflow/docs_src/guide/feature_columns.md +++ b/tensorflow/docs_src/guide/feature_columns.md @@ -5,7 +5,7 @@ intermediaries between raw data and Estimators. Feature columns are very rich, enabling you to transform a diverse range of raw data into formats that Estimators can use, allowing easy experimentation. -In @{$premade_estimators$Premade Estimators}, we used the premade +In [Premade Estimators](../guide/premade_estimators.md), we used the premade Estimator, `tf.estimator.DNNClassifier` to train a model to predict different types of Iris flowers from four input features. That example created only numerical feature columns (of type @@ -534,7 +534,7 @@ embedding_column = tf.feature_column.embedding_column( dimension=embedding_dimensions) ``` -@{$guide/embedding$Embeddings} is a significant topic within machine +[Embeddings](../guide/embedding.md) is a significant topic within machine learning. This information was just to get you started using them as feature columns. @@ -559,7 +559,7 @@ As the following list indicates, not all Estimators permit all types of For more examples on feature columns, view the following: -* The @{$low_level_intro#feature_columns$Low Level Introduction} demonstrates how +* The [Low Level Introduction](../guide/low_level_intro.md#feature_columns) demonstrates how experiment directly with `feature_columns` using TensorFlow's low level APIs. * The [Estimator wide and deep learning tutorial](https://github.com/tensorflow/models/tree/master/official/wide_deep) solves a binary classification problem using `feature_columns` on a variety of |