<p>Your input is strange as it is an binary-code. I don't know whether the model will work well.</p>

<p>First of all, you need to add start and end marks for your input and output which indicates the boundaries. Then design regional module of each time step, including how to use hidden state. You could try simple GRU/LSTM networks as following.</p>

<p><a href="https://i.stack.imgur.com/WPPvb.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/WPPvb.png" alt="enter image description here"></a></p>

<p>For details, you could try <strong>Encoder</strong></p>

<p><a href="https://i.stack.imgur.com/0ZQMk.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/0ZQMk.png" alt="enter image description here"></a></p>

<p>and <strong>Decoder</strong></p>

<p><a href="https://i.stack.imgur.com/as2bM.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/as2bM.png" alt="enter image description here"></a></p>

<p>In addition, you could take a look at <strong>Attention mechanism</strong> in paper <a href="https://arxiv.org/pdf/1409.0473.pdf" rel="nofollow noreferrer">Neural Machine Translation by Jointly Learning to Align and Translate</a>. And the structure is as following.</p>

<p><a href="https://i.stack.imgur.com/xaf2o.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/xaf2o.png" alt="enter image description here"></a></p>

<p>For details</p>

<p><a href="https://i.stack.imgur.com/zz0Q6.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/zz0Q6.png" alt="enter image description here"></a></p>

<p>Though you are using Keras, I think it will be helpful to read PyTorch codes as it is straightforward and easy to understand. The tutorial given in <a href="http://pytorch.org/tutorials/intermediate/seq2seq_translation_tutorial.html" rel="nofollow noreferrer">PyTorch tutorial</a></p>
LSTM for vector to character sequence translation
<p>I'm looking to build a sequence-to-sequence model that takes in a 2048-long vector of 1s and 0s (ex. [1,0,1,0,0,1,0,0,0,1,...,1] ) as my input and translating it to my known output of (a variable length) 1-20 long characters (ex. GBNMIRN, ILCEQZG, or FPSRABBRF).</p>

<p>My goal is to create a model that can take in a new 2048-long vector of 1s and 0s and predict what the output sequence will look like.</p>

<p>I've looked at some github repositories like <a href="https://github.com/llSourcell/seq2seq_model_live/blob/master/2-seq2seq-advanced.ipynb%20" rel="nofollow noreferrer">this</a> and <a href="https://github.com/hans/ipython-notebooks/blob/master/tf/TF%20tutorial.ipynb" rel="nofollow noreferrer">this</a>.</p>

<p>but I'm not sure how to implement it with my problem. Are there any projects that have done something similar to this/how could I implement this with the seq2seq models or LSTMs currently out there? (python implementations)</p>

<p>I am using the keras library in python. </p>