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Diffstat (limited to 'tensorflow/docs_src/tutorials/representation/kernel_methods.md')
| -rw-r--r-- | tensorflow/docs_src/tutorials/representation/kernel_methods.md | 4 |
1 files changed, 2 insertions, 2 deletions
diff --git a/tensorflow/docs_src/tutorials/representation/kernel_methods.md b/tensorflow/docs_src/tutorials/representation/kernel_methods.md index 71e87f4d3e..67adc4951c 100644 --- a/tensorflow/docs_src/tutorials/representation/kernel_methods.md +++ b/tensorflow/docs_src/tutorials/representation/kernel_methods.md @@ -2,7 +2,7 @@ Note: This document uses a deprecated version of `tf.estimator`, `tf.contrib.learn.Estimator`, which has a different interface. It also uses -other `contrib` methods whose @{$version_compat#not_covered$API may not be stable}. +other `contrib` methods whose [API may not be stable](../../guide/version_compat.md#not_covered). In this tutorial, we demonstrate how combining (explicit) kernel methods with linear models can drastically increase the latters' quality of predictions @@ -52,7 +52,7 @@ In order to feed data to a `tf.contrib.learn Estimator`, it is helpful to conver it to Tensors. For this, we will use an `input function` which adds Ops to the TensorFlow graph that, when executed, create mini-batches of Tensors to be used downstream. For more background on input functions, check -@{$premade_estimators#create_input_functions$this section on input functions}. +[this section on input functions](../../guide/premade_estimators.md#create_input_functions). In this example, we will use the `tf.train.shuffle_batch` Op which, besides converting numpy arrays to Tensors, allows us to specify the batch_size and whether to randomize the input every time the input_fn Ops are executed |