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Fix the tpu related broken link especially for imagenet_to_gcs.py #18126

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Mar 30, 2018
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Fix the tpu related broken link especially for imagenet_to_gcs.py #18126

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2 changes: 1 addition & 1 deletion tensorflow/contrib/tpu/tpu_estimator.md
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Expand Up @@ -172,7 +172,7 @@ It is always recommended to port a small, simple model first to make sure that
you are familiar with the basic concepts of `TPUEstimator` and test end-to-end
behavior. Once your simple model runs, gradually add more functionality.
In addition, there are several sample models, available at
[github.com/tensorflow/tpu-demos](https://github.com/tensorflow/tpu-demos).
[github.com/tensorflow/tpu](https://github.com/tensorflow/tpu).

To convert your code from the vanilla `Estimator` class to use TPUs, change the
following (note some of the details may change over time):
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10 changes: 5 additions & 5 deletions tensorflow/docs_src/programmers_guide/using_tpu.md
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Expand Up @@ -11,7 +11,7 @@ This doc is aimed at users who:
using an existing model.
* Have, perhaps, skimmed the code of an example TPU model
[[1]](https://github.com/tensorflow/models/blob/master/official/mnist/mnist_tpu.py)
[[2]](https://github.com/tensorflow/tpu-demos/tree/master/cloud_tpu/models).
[[2]](https://github.com/tensorflow/tpu/tree/master/models).
* Are interested in porting an existing `Estimator` model to
run on Cloud TPUs

Expand Down Expand Up @@ -289,7 +289,7 @@ If shape inference has failed, but the shape is known it is possible to
impose the correct shape using `tf.set_shape()`.

In the example below the shape
inference algorithm fails, but it is corrected using `set_shape`:
inference algorithm fails, but it is correctly using `set_shape`:

```
>>> x = tf.zeros(tf.constant([1,2,3])+1)
Expand Down Expand Up @@ -372,10 +372,10 @@ in bytes. A minimum of a few MB (`buffer_size=8*1024*1024`) is recommended so
that data is available when needed.

The TPU-demos repo includes
[a script](https://github.com/tensorflow/tpu-demos/blob/master/cloud_tpu/datasets/imagenet_to_gcs.py)
[a script](https://github.com/tensorflow/tpu/blob/master/tools/datasets/imagenet_to_gcs.py)
for downloading the imagenet dataset and converting it to an appropriate format.
This together with the imagenet
[models](https://github.com/tensorflow/tpu-demos/tree/master/cloud_tpu/models)
[models](https://github.com/tensorflow/tpu/tree/master/models)
included in the repo demonstrate all of these best-practices.


Expand All @@ -388,7 +388,7 @@ For details on how to actually set up and run a Cloud TPU see:
This document is by no means exhaustive. The best source of more detail on how
to make a Cloud TPU compatible model are the example models published in:

* The [TPU Demos Repository.](https://github.com/tensorflow/tpu-demos/)
* The [TPU Demos Repository.](https://github.com/tensorflow/tpu)

For more information about tuning TensorFlow code for performance see:

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