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Diffstat (limited to 'tensorflow/docs_src/tutorials/representation/word2vec.md')
| -rw-r--r-- | tensorflow/docs_src/tutorials/representation/word2vec.md | 4 |
1 files changed, 2 insertions, 2 deletions
diff --git a/tensorflow/docs_src/tutorials/representation/word2vec.md b/tensorflow/docs_src/tutorials/representation/word2vec.md index 7964650e19..df0d3176b6 100644 --- a/tensorflow/docs_src/tutorials/representation/word2vec.md +++ b/tensorflow/docs_src/tutorials/representation/word2vec.md @@ -383,13 +383,13 @@ compromised speed because we use Python for reading and feeding data items -- each of which require very little work on the TensorFlow back-end. If you find your model is seriously bottlenecked on input data, you may want to implement a custom data reader for your problem, as described in -@{$new_data_formats$New Data Formats}. For the case of Skip-Gram +[New Data Formats](../../extend/new_data_formats.md). For the case of Skip-Gram modeling, we've actually already done this for you as an example in [models/tutorials/embedding/word2vec.py](https://github.com/tensorflow/models/tree/master/tutorials/embedding/word2vec.py). If your model is no longer I/O bound but you want still more performance, you can take things further by writing your own TensorFlow Ops, as described in -@{$adding_an_op$Adding a New Op}. Again we've provided an +[Adding a New Op](../../extend/adding_an_op.md). Again we've provided an example of this for the Skip-Gram case [models/tutorials/embedding/word2vec_optimized.py](https://github.com/tensorflow/models/tree/master/tutorials/embedding/word2vec_optimized.py). Feel free to benchmark these against each other to measure performance |