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Expand Up @@ -80,7 +80,7 @@ model.compile(loss='mse', optimizer='rmsprop')

![Attention Seq2seq](http://i64.tinypic.com/a2rw3d.png)

Let's not stop there either. In all the models described above, there is no allignment between the input sequence elements and the output sequence elements. But for machine translation, learning a soft allignment between the input and output sequences imporves performance.[[3]](http://arxiv.org/pdf/1409.0473v6.pdf). The Seq2seq framework includes a ready made attention model which does the same. Note that in the attention model, there is no hidden state propogation, and a bidirectional LSTM encoder is used by default. Example:
Let's not stop there either. In all the models described above, there is no allignment between the input sequence elements and the output sequence elements. But for machine translation, learning a soft allignment between the input and output sequences improves performance.[[3]](http://arxiv.org/pdf/1409.0473v6.pdf). The Seq2seq framework includes a ready made attention model which does the same. Note that in the attention model, there is no hidden state propogation, and a bidirectional LSTM encoder is used by default. Example:

```python
import seq2seq
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