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Diffstat (limited to 'tensorflow/docs_src/tutorials/images/image_recognition.md')
| -rw-r--r-- | tensorflow/docs_src/tutorials/images/image_recognition.md | 4 |
1 files changed, 2 insertions, 2 deletions
diff --git a/tensorflow/docs_src/tutorials/images/image_recognition.md b/tensorflow/docs_src/tutorials/images/image_recognition.md index 83a8d97cf0..52913b2082 100644 --- a/tensorflow/docs_src/tutorials/images/image_recognition.md +++ b/tensorflow/docs_src/tutorials/images/image_recognition.md @@ -106,7 +106,7 @@ curl -L "https://storage.googleapis.com/download.tensorflow.org/models/inception Next, we need to compile the C++ binary that includes the code to load and run the graph. If you've followed -@{$install_sources$the instructions to download the source installation of TensorFlow} +[the instructions to download the source installation of TensorFlow](../../install/install_sources.md) for your platform, you should be able to build the example by running this command from your shell terminal: @@ -448,7 +448,7 @@ and Michael Nielsen's book has a covering them. To find out more about implementing convolutional neural networks, you can jump -to the TensorFlow @{$deep_cnn$deep convolutional networks tutorial}, +to the TensorFlow [deep convolutional networks tutorial](../../tutorials/images/deep_cnn.md), or start a bit more gently with our [Estimator MNIST tutorial](../estimators/cnn.md). Finally, if you want to get up to speed on research in this area, you can read the recent work of all the papers referenced in this tutorial. |