<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>

CC BY-SA 4.0
2019-12-13T08:42:54.767Z
2019-12-13T08:42:54.767Z
Clement
<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>

<natural-language-processing><recurrent-neural-networks><sequence-modeling>
Can a character-level Seq2Seq setup learn to perfectly reconstruct structured data like name strings?
ADSBJason
# 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


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## 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.


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