Body: <p>I am trying to reproduce the paper <a href="https://arxiv.org/pdf/2005.13092.pdf" rel="nofollow noreferrer">Synthetic Petri Dish: A novel surrogate model for Rapid Architecture Search</a>. In the paper, the authors try to reduce the architecture of an MLP model trained on MNIST (2 layers - 100 neurons) by initializing a <strong>motif network</strong> from it, that is, 2 layers, 1 neuron each, and extracting the sigmoid function. I have been searching a lot, but I have not found the answer of how can someone extract an 'architectural motif' from a <strong>trained</strong> neural network.</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-01-13T17:04:50.553Z
Comments.Text: Hi. Welcome to AI SE. Have you already looked into https://github.com/uber-research/Synthetic-Petri-Dish? If you find the answer in this Github repo or somewhere else, feel free to write a formal answer below to your own question ;)
Comments.UserDisplayName: nbro
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-01-13T17:10:29.457Z
Comments.Text: @nbro Hello, thanks for welcoming me. Indeed I did but unfortunately they did not include the motif extraction part in their code. What they do only is display the motif training points - which they define in their paper as tuple of slope values and validation accuracy.
Comments.UserDisplayName: Perl Del Rey
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-01-13T17:12:30.590Z
Comments.Text: Maybe the best thing to do is open an issue in the issue tracker of that repo, if nobody provides an answer meanwhile.
Comments.UserDisplayName: nbro
Tags: <neural-networks><papers><neural-architecture-search>
Title: How can an "architectural motif" be extracted from a trained MLP?
UserDisplayName: Perl Del Rey
ThreadText: # How can an "architectural motif" be extracted from a trained MLP?
  
  (Asked by: username_0 on 2021-01-12)
  
  I am trying to reproduce the paper [Synthetic Petri Dish: A novel surrogate model for Rapid Architecture Search](https://arxiv.org/pdf/2005.13092.pdf). In the paper, the authors try to reduce the architecture of an MLP model trained on MNIST (2 layers - 100 neurons) by initializing a **motif network** from it, that is, 2 layers, 1 neuron each, and extracting the sigmoid function. I have been searching a lot, but I have not found the answer of how can someone extract an 'architectural motif' from a **trained** neural network.
  
  **Tags:** neural-networks, papers, neural-architecture-search
  
  > **Comments:**
  > **username_1**: Hi. Welcome to AI SE. Have you already looked into https://github.com/uber-research/Synthetic-Petri-Dish? If you find the answer in this Github repo or somewhere else, feel free to write a formal answer below to your own question ;)  
  > **username_0**: @username_1 Hello, thanks for welcoming me. Indeed I did but unfortunately they did not include the motif extraction part in their code. What they do only is display the motif training points - which they define in their paper as tuple of slope values and validation accuracy.  
  > **username_1**: Maybe the best thing to do is open an issue in the issue tracker of that repo, if nobody provides an answer meanwhile.  
  
  ---
  
  ## 0 answers
  
  