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| author |  A. Unique TensorFlower <nobody@tensorflow.org> | 2016-01-13 14:05:16 -0800 |
|---|---|---|
| committer |  Vijay Vasudevan <vrv@google.com> | 2016-01-13 14:05:16 -0800 |
| commit | 0c62942e2bda15fa815fff440c9f5e466bff59b5 (patch) | |
| tree | 530559d71ef1671c5a81ea2018677ae25948c9bb /tensorflow/g3doc/tutorials/mnist/download/index.md | |
| parent | 5515148be85f369484d6179b7c1baab30995b068 (diff) | |
Change visibility to public in bower.BUILD
A remote repository cannot refer to the main repository that way and a fix in a future Bazel release will break TensorFlow with the current set-up.
Error: http://ci.bazel.io/job/TensorFlow/BAZEL_VERSION=HEAD,PLATFORM_NAME=linux-x86_64/74/console
Change: 112056797
Diffstat (limited to 'tensorflow/g3doc/tutorials/mnist/download/index.md')
| -rw-r--r-- | tensorflow/g3doc/tutorials/mnist/download/index.md | 4 |
1 files changed, 2 insertions, 2 deletions
diff --git a/tensorflow/g3doc/tutorials/mnist/download/index.md b/tensorflow/g3doc/tutorials/mnist/download/index.md index e9698d6248..dcd7dfc23d 100644 --- a/tensorflow/g3doc/tutorials/mnist/download/index.md +++ b/tensorflow/g3doc/tutorials/mnist/download/index.md @@ -1,6 +1,6 @@ # MNIST Data Download -Code: [tensorflow/examples/tutorials/mnist/](https://www.tensorflow.org/code/tensorflow/examples/tutorials/mnist/) +Code: [tensorflow/examples/tutorials/mnist/](https://tensorflow.googlesource.com/tensorflow/+/master/tensorflow/examples/tutorials/mnist/) The goal of this tutorial is to show how to download the dataset files required for handwritten digit classification using the (classic) MNIST data set. @@ -11,7 +11,7 @@ This tutorial references the following files: File | Purpose --- | --- -[`input_data.py`](https://www.tensorflow.org/code/tensorflow/examples/tutorials/mnist/input_data.py) | The code to download the MNIST dataset for training and evaluation. +[`input_data.py`](https://tensorflow.googlesource.com/tensorflow/+/master/tensorflow/examples/tutorials/mnist/input_data.py) | The code to download the MNIST dataset for training and evaluation. ## Prepare the Data |