title: Is Seq2Seq the right model for my data?
body: <p>I'm trying to train a model to predict design patterns from web pages. I'm using coordinates of bounding rects given a bunch of element groupings. Patterns look like this:</p>
  
  <pre><code> [[elementId, width, height, x, y]]
  </code></pre>
  
  <p>so my target would be the <code>[[x,y]]</code> given <code>[[elementId, width, height]]</code>.</p>
  
  <p>Concretely:</p>
  
  <pre><code> [[5, 1.0, 1.0], [4, 1.0, 1.0], [2, 175.0, 65.0], [2, 1.0, 1.0], [4, 1.0, 1.0]]
   -&gt;
   [[0.0, 0.0], [0.0, 10.0], [3.0, 0.0], [0.0, 68.0], [0.0, 10.0]]
  
  
   [[2, 14.0, 14.0], [2, 14.0, 14.0], [2, 14.0, 14.0]]  
   -&gt;
   [[0.0, 3.0], [0.0, 3.0], [0.0, 3.0]]
  </code></pre>
  
  <p>Patterns vary in size so I've padded them with <code>[[0,0,0]]</code>. I currently have about 15k of them, but can get more.</p>
  
  <p>I was told that seq2seq with attention is the right model for this job. I've started with <a href="https://machinelearningmastery.com/develop-encoder-decoder-model-sequence-sequence-prediction-keras/" rel="nofollow noreferrer">https://machinelearningmastery.com/develop-encoder-decoder-model-sequence-sequence-prediction-keras/</a> and achieved horrendous results.</p>
  
  <p>Every seq2seq example i can find (searching for keras or pytorch) is used for translation which is categorical and I'm struggling to find a good regression based example.</p>
  
  <p>So my question is:</p>
  
  <p>1) Is this the right model (encoder/decoder LSTM) for what i'm trying to do?</p>
  
  <p>2) Is there any examples if so?</p>
  
  <p>Thanks so much in advance. I don't expect anyone to solve my problem, but any help would be much appreciated!</p>