From 17a758f7bb2aa3937da628669617e1e86c6bb449 Mon Sep 17 00:00:00 2001
From: Billy Lamberta <blamb@google.com>
Date: Tue, 5 Mar 2019 00:55:10 -0800
Subject: [PATCH] Pub lite

PiperOrigin-RevId: 236802220
---
 tensorflow/lite/g3doc/convert/cmdline_examples.md           | 6 +++---
 tensorflow/lite/g3doc/convert/python_api.md                 | 4 ++--
 tensorflow/lite/g3doc/guide/ops_select.md                   | 2 +-
 .../lite/g3doc/performance/post_training_quantization.md    | 2 +-
 4 files changed, 7 insertions(+), 7 deletions(-)

diff --git a/tensorflow/lite/g3doc/convert/cmdline_examples.md b/tensorflow/lite/g3doc/convert/cmdline_examples.md
index 067f09a5576..139a6c9b521 100644
--- a/tensorflow/lite/g3doc/convert/cmdline_examples.md
+++ b/tensorflow/lite/g3doc/convert/cmdline_examples.md
@@ -64,19 +64,19 @@ tflite_convert \
   --saved_model_dir=/tmp/saved_model
 ```
 
-[SavedModel](https://www.tensorflow.org/guide/saved_model.md#using_savedmodel_with_estimators)
+[SavedModel](https://www.tensorflow.org/guide/saved_model#using_savedmodel_with_estimators)
 has fewer required flags than frozen graphs due to access to additional data
 contained within the SavedModel. The values for `--input_arrays` and
 `--output_arrays` are an aggregated, alphabetized list of the inputs and outputs
 in the [SignatureDefs](../../serving/signature_defs.md) within
 the
-[MetaGraphDef](https://www.tensorflow.org/saved_model.md#apis_to_build_and_load_a_savedmodel)
+[MetaGraphDef](https://www.tensorflow.org/saved_model#apis_to_build_and_load_a_savedmodel)
 specified by `--saved_model_tag_set`. As with the GraphDef, the value for
 `input_shapes` is automatically determined whenever possible.
 
 There is currently no support for MetaGraphDefs without a SignatureDef or for
 MetaGraphDefs that use the [`assets/`
-directory](https://www.tensorflow.org/guide/saved_model.md#structure_of_a_savedmodel_directory).
+directory](https://www.tensorflow.org/guide/saved_model#structure_of_a_savedmodel_directory).
 
 ### Convert a tf.Keras model <a name="keras"></a>
 
diff --git a/tensorflow/lite/g3doc/convert/python_api.md b/tensorflow/lite/g3doc/convert/python_api.md
index 06c7389053a..4d2c7361c9f 100644
--- a/tensorflow/lite/g3doc/convert/python_api.md
+++ b/tensorflow/lite/g3doc/convert/python_api.md
@@ -241,8 +241,8 @@ interpreter.allocate_tensors()
 In order to run the latest version of the TensorFlow Lite Converter Python API,
 either install the nightly build with
 [pip](https://www.tensorflow.org/install/pip) (recommended) or
-[Docker](https://www.tensorflow.org/install/docker.md), or
-[build the pip package from source](https://www.tensorflow.org/install/source.md).
+[Docker](https://www.tensorflow.org/install/docker), or
+[build the pip package from source](https://www.tensorflow.org/install/source).
 
 ### Converting models from TensorFlow 1.12 <a name="pre_tensorflow_1.12"></a>
 
diff --git a/tensorflow/lite/g3doc/guide/ops_select.md b/tensorflow/lite/g3doc/guide/ops_select.md
index 21649ea62ba..51320140e1c 100644
--- a/tensorflow/lite/g3doc/guide/ops_select.md
+++ b/tensorflow/lite/g3doc/guide/ops_select.md
@@ -34,7 +34,7 @@ choice. It also discusses some [known limitations](#known-limitations), the
 
 To convert a TensorFlow model to a TensorFlow Lite model with TensorFlow ops,
 use the `target_ops` argument in the
-[TensorFlow Lite converter](../convert/index.md). The
+[TensorFlow Lite converter](../convert/). The
 following values are valid options for `target_ops`:
 
 *   `TFLITE_BUILTINS` - Converts models using TensorFlow Lite builtin ops.
diff --git a/tensorflow/lite/g3doc/performance/post_training_quantization.md b/tensorflow/lite/g3doc/performance/post_training_quantization.md
index 0aa7e5163a9..5fdf77b4011 100644
--- a/tensorflow/lite/g3doc/performance/post_training_quantization.md
+++ b/tensorflow/lite/g3doc/performance/post_training_quantization.md
@@ -3,7 +3,7 @@
 Post-training quantization is a general technique to reduce model size while also
 providing up to 3x lower latency with little degradation in model accuracy. Post-training
 quantization quantizes weights from floating point to 8-bits of precision. This technique
-is enabled as an option in the [TensorFlow Lite converter](../convert/index.md):
+is enabled as an option in the [TensorFlow Lite converter](../convert/):
 
 ```
 import tensorflow as tf