Update documentation.

- Use absolute links instead of relative links. Relative links
break when published on website.
- Correct NNAPI abbreviation.

PiperOrigin-RevId: 215225415

1 files changed, 5 insertions, 5 deletions

diff --git a/tensorflow/contrib/lite/g3doc/performance.md b/tensorflow/contrib/lite/g3doc/performance.md
index 0ae9400068..6b7943caf8 100644
--- a/tensorflow/contrib/lite/g3doc/performance.md
+++ b/tensorflow/contrib/lite/g3doc/performance.md
@@ -7,12 +7,12 @@ Mobile and embedded devices have limited computational resources and it is impor
 Some models may be too large to run on embedded devices. Instead of large models it is better to use a slightly less precise but smaller model for embedded devices. Smaller models not only use less disk space and memory but are generally faster and more energy efficient. One example of models optimized for mobile devices are [MobileNets](https://arxiv.org/abs/1704.04861), which are optimized for mobile vision applications. Tensorflow Lite [models page](models.md) lists several other models that have been optimized specifically for mobile and embedded devices.
 
 You can retrain the listed models on your own dataset by using transfer learning. Check out our transfer learning tutorial for
-[image classification] (https://codelabs.developers.google.com/codelabs/tensorflow-for-poets/#0) and
+[image classification](https://codelabs.developers.google.com/codelabs/tensorflow-for-poets/#0) and
  [object detection](https://medium.com/tensorflow/training-and-serving-a-realtime-mobile-object-detector-in-30-minutes-with-cloud-tpus-b78971cf1193).
 
 
 ## Profile your model
-Before starting any optimization, it is a good practice to profile and benchmark your model. Tensorflow Lite [benchmarking tool](../tools/benchmark) has a built-in profiler that shows per operator profiling statistics. This can help in understanding performance bottlenecks and which operators dominate the computation time.
+Before starting any optimization, it is a good practice to profile and benchmark your model. Tensorflow Lite [benchmarking tool](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/lite/tools/benchmark) has a built-in profiler that shows per operator profiling statistics. This can help in understanding performance bottlenecks and which operators dominate the computation time.
 
 ## Profile and optimize operators in the graph
 If a particular operator appears frequently in the model and based on profiling you find the operator consuming the most amount of time, you can look into optimizing the operator.
@@ -22,7 +22,7 @@ If a particular operator appears frequently in the model and based on profiling
 If your model uses floating point weights or activations then it may be possible to reduce the size of model up to ~4x by using quantization and other model optimizations. Check out our [model optimization toolkit](https://www.tensorflow.org/performance/model_optimization) for details about optimizing your model. Fully quantized models can be remarkably power efficient as well.
 
 ## Tweak the number of threads
-Tensorflow Lite supports multi-threaded kernels for many operators. You can increase the number of threads and speed up execution of operators. Increasing the number of threads will however make your model use more resources and power. For some applications latency may be more important than energy efficiency. You can increase the number of threads by setting the number of [interpreter](../interpreter.h) threads.
+Tensorflow Lite supports multi-threaded kernels for many operators. You can increase the number of threads and speed up execution of operators. Increasing the number of threads will however make your model use more resources and power. For some applications latency may be more important than energy efficiency. You can increase the number of threads by setting the number of [interpreter](https://github.com/tensorflow/tensorflow/blob/1084594657a5d139102ac794f84d1427a710e39a/tensorflow/contrib/lite/interpreter.h#L337) threads.
 
 ## Eliminate redundant copies
 Tensorflow Lite is optimized to reduce redundant copies. The APIs allow user to [mmap a model file](https://github.com/tensorflow/tensorflow/blob/9982fd6c8831cbd2f58954f79ea71f26660393bc/tensorflow/contrib/lite/model.h#L152) and avoid copies. If your application is not careful, there can be redundant copies when feeding the input to the model and reading output from the model. Make sure to eliminate redundant copies. If you are using higher level APIs like Java API, make sure to carefully check the documentation for performance caveats. For example, the Java API is a lot faster if ByteBuffers are used as [inputs](https://github.com/tensorflow/tensorflow/blob/6305a6d83552ba6a472cd72398b60d9241467f1f/tensorflow/contrib/lite/java/src/main/java/org/tensorflow/lite/Interpreter.java#L151).
@@ -31,8 +31,8 @@ Tensorflow Lite is optimized to reduce redundant copies. The APIs allow user to
 Platform specific tools like [Android profiler](https://developer.android.com/studio/profile/android-profiler) and [Instruments](https://help.apple.com/instruments/mac/current/) provide a wealth of profiling information that can be used to debug your app. Sometimes the performance bug may be not in the model but in parts of application code that interact with the model. Make sure to familiarize yourself with platform specific profiling tools and best practices for your platform.
 
 ## Use hardware accelerators available on the device
-Tensorflow Lite is working on adding support for accelerators like GPU and provides acceleration through [NNAPI](https://developer.android.com/ndk/guides/neuralnetworks/) on Android.
-You can utilize these hardware accelerator backends to improve the speed and efficiency of your model. To enable NNAPI call [UseNNAPI](https://github.com/tensorflow/tensorflow/blob/6305a6d83552ba6a472cd72398b60d9241467f1f/tensorflow/contrib/lite/interpreter.h#L334) on the interpreter instance.
+Tensorflow Lite is working on adding support for accelerators like GPU and provides acceleration through [Neural Networks API](https://developer.android.com/ndk/guides/neuralnetworks/) on Android.
+You can utilize these hardware accelerator backends to improve the speed and efficiency of your model. To enable Neural Networks API call [UseNNAPI](https://github.com/tensorflow/tensorflow/blob/6305a6d83552ba6a472cd72398b60d9241467f1f/tensorflow/contrib/lite/interpreter.h#L334) on the interpreter instance.
 
 ## Need more help
 The Tensorflow team is happy to help diagnose and address specific performance issues you may be facing. Please file a bug on [github](https://github.com/tensorflow/tensorflow/issues) with details of the issue.