Answers.Body: <p>If you give it a name, it will probably be <strong>almost</strong> perfect model, as the number of hidden units are definitely enough to store the raw data of a name. However as a neural network it will still not be perfect. There may also be serious overfitting if you do this. A Seq2Seq model have way more parameters than necessary to just "remembers" all the possible names, as names variants are not a lot. </p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2019-12-13T08:42:54.767Z
Answers.LastActivityDate: 2019-12-13T08:42:54.767Z
Answers.UserDisplayName: Clement
Body: <p>If not perfect, how well can they do? For example, if I give the Seq2Seq setup a name it did not see in the training process, can it output the same name without error?</p>
  
  <p>Example</p>
  
  
  
  <pre><code>name = "Will Smith"
  output = DecoderRNN(EncoderRNN(name))
  can_this_be_true = name == output
  </code></pre>
  
Tags: <natural-language-processing><recurrent-neural-networks><sequence-modeling>
Title: Can a character-level Seq2Seq setup learn to perfectly reconstruct structured data like name strings?
UserDisplayName: ADSBJason
ThreadText: # Can a character-level Seq2Seq setup learn to perfectly reconstruct structured data like name strings?
  
  (Asked by: username_0 on 2019-12-12)
  
  If not perfect, how well can they do? For example, if I give the Seq2Seq setup a name it did not see in the training process, can it output the same name without error?
  
  Example
  
  ```
  name = "Will Smith"
  output = DecoderRNN(EncoderRNN(name))
  can_this_be_true = name == output
  
  ```
  
  **Tags:** natural-language-processing, recurrent-neural-networks, sequence-modeling
  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_1 on 2019-12-13. Score: 1
  
  If you give it a name, it will probably be **almost** perfect model, as the number of hidden units are definitely enough to store the raw data of a name. However as a neural network it will still not be perfect. There may also be serious overfitting if you do this. A Seq2Seq model have way more parameters than necessary to just "remembers" all the possible names, as names variants are not a lot.
  
  
  ---
  
  