title: Difference between parallel and sequential Convolutions in Convolutional Neural Network
body: <p>I'm trying to implement a Convolutional Neural Networks with tensorflow for classifying text. I already found some implemented models and found especially two implementations: </p>
  
  <p><em>[I'm not allowed to post more than 2 Links, I will try to provide the sources in the comments]</em></p>
  
  <p>However they seem to differ fundamentally in their architecture. The first model uses the convolutions parallel with the input data whereas the second model uses the convolutions in a sequential way. I visualized the two models with tensorboard:</p>
  
  <p>First the <em>parallel</em> convolutions. After the convolutions the results are concated and with one fully connected layer the output is created.</p>
  
  <p><a href="https://i.stack.imgur.com/gTg4E.png" rel="nofollow noreferrer">parallel use of convolutions</a></p>
  
  <p>The <em>sequential</em> convolutions seem to be more straight forward, we use the result form the previous layer as the input for the next layer. </p>
  
  <p><a href="https://i.stack.imgur.com/kdQrn.png" rel="nofollow noreferrer">sequential use of convolutions</a></p>
  
  <p>So my question is, since both are used to classify text, where lies the difference between these two implementations and which one is more suitable for classifying text? </p>