Pamplemousse/NetworkLibrary.html at master · sitelle-signals/Pamplemousse · GitHub

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<!DOCTYPE html>
<html>

<head>
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  <title>Pamplemousse</title>
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<body>
  <script>
    $(function(){
      $("#includedContent").load("navigation.html");
    });
    $(function(){
      $("#includedSN3Library").load("SN3-Library.html");
    });
    $(function(){
      $("#includedSN2Library").load("SN2-Library.html");
    });
    $(function(){
      $("#includedSN1Library").load("SN1-Library.html");
    });
    </script>


  <div id="includedContent"></div>


  <!-- Page Content -->
  <div class="container">
      <div class="row mt-5">
        <div class="col-12 mb-5 mt-5">
          <h2>Library of Pre-Trained Networks</h2>
          <p>
            The page contains a library of pre-trained networks (i.e. weights) that can be simply loaded as models in tensorflow.
            To do this, please see the <b>Apply to Data Cube</b> example on the <a href="examples.html">examples page</a>.
            Additionally, I will report the results of each pre-trained network.
          </p>
          <h3>
            What were these networks trained on?
          </h3>
          <p>
            For each set of data in the library, we created 30,000 synthetic SITELLE spectra using the formulation described
            in the <b>Create Synthetic Data</b> example of varying resolutions. The velocity was uniformly sampled from
            a distribution ranging from -500 km/s to 500 km/s. The broadening values were uniformly sampled from a distribution
            ranging from 10 km/s to 200 km/s. Each section contains a pickle file containing the spectra used, the saved
            CNN model, an image of the reference spectrum, and statistics on the accuracy of the method on the test set.

            Click on the <i class="fas fa-download text-info"></i> icon to download the pre-trained CNN.

            If you would like a pre-trained library with different specifications, please feel
            free to contact us at <span class="text-info">carter.rhea@umontreal.ca</span>.
            <br>
            Here are the files I used to create these pre-trained networks:
            <a href="Generate.py" download>Generate.py</a> &
            <a href="Train.py" download>Train.py</a>

            <div class="alert alert-info" role="alert">
              Additional resolutions can be found in the table at the bottom of the page (coming soon).
            </div>
          </p>
      </div>
    </div>

    <ul class="nav nav-tabs">
      <li class="nav-item"><a data-toggle="tab" class="nav-link active" href="#sn3-library">SN3</a></li>
      <li class="nav-item"><a data-toggle="tab" class="nav-link" href="#sn2-library">SN2</a></li>
      <li class="nav-item"><a data-toggle="tab" class="nav-link" href="#sn1-library">SN1</a></li>
    </ul>

    <div class="tab-content">

      <div id="sn3-library" class="tab-pane container active">
        <p class="mt-2">We created pre-trained networks for the following resolutions: 2000, 2500, 3000,
          3500, 4000, 4500, 5000, and 7000</p>
        <div id="includedSN3Library"></div>
      </div>
      <div id="sn2-library" class="tab-pane container fade">
        <p class="mt-2">We created pre-trained networks for the following resolutions: 600, 700, 800, 900,
          1000, and 1800.</p>
        <div id="includedSN2Library"></div>
      </div>
      <div id="sn1-library" class="tab-pane container fade">
        <p class="mt-2">We created pre-trained networks for the following resolutions: 600, 700, 800, 900,
          1000, and 1800.</p>
        <div id="includedSN1Library"></div>
      </div>

    </div>


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