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Diffstat (limited to 'tensorflow/g3doc/tutorials/seq2seq/index.md')
| -rw-r--r-- | tensorflow/g3doc/tutorials/seq2seq/index.md | 6 |
1 files changed, 3 insertions, 3 deletions
diff --git a/tensorflow/g3doc/tutorials/seq2seq/index.md b/tensorflow/g3doc/tutorials/seq2seq/index.md index 3d64bcc91b..c1673f474d 100644 --- a/tensorflow/g3doc/tutorials/seq2seq/index.md +++ b/tensorflow/g3doc/tutorials/seq2seq/index.md @@ -58,7 +58,7 @@ translation [Sutskever et al., 2014](http://arxiv.org/abs/1409.3215) In the basic model depicted above, every input has to be encoded into a fixed-size state vector, as that is the only thing passed to the decoder. To allow the decoder more direct access to the input, an *attention* mechanism -was introduced in [Bahdanu et al., 2014](http://arxiv.org/abs/1409.0473) +was introduced in [Bahdanau et al., 2014](http://arxiv.org/abs/1409.0473) ([pdf](http://arxiv.org/pdf/1409.0473.pdf)). We will not go into the details of the attention mechanism (see the paper), suffice it to say that it allows the decoder to peek into the input at every @@ -176,8 +176,8 @@ projections are constructed by the following code in `seq2seq_model.py`. ``` First, note that we only construct a sampled softmax if the number of samples -(512 by default) is smaller that the target vocabulary size. For vocabularies -smaller than 512 it might be a better idea to just use a standard softmax loss. +(512 by default) is smaller than the target vocabulary size. For vocabularies +smaller than 512, it might be a better idea to just use a standard softmax loss. Then, as you can see, we construct an output projection. It is a pair, consisting of a weight matrix and a bias vector. If used, the rnn cell |