Difference between revisions of "Team:Florida Atlantic/Model"

(Prototype team page)
 
 
(19 intermediate revisions by the same user not shown)
Line 1: Line 1:
 
{{Florida_Atlantic}}
 
{{Florida_Atlantic}}
 +
 
<html>
 
<html>
 +
<head>
 +
<style type="text/css">
 +
body { background-color: #101337 }
 +
p {font-family: monospace;
 +
color: #FFFFFF;
 +
size: 18;}
 +
.center { text-align: center; }
 +
</style>
 +
</head>
 +
<body>
  
 +
<div class="column full_size" >
  
<div class="column full_size judges-will-not-evaluate">
+
<img src="https://static.igem.org/mediawiki/2017/archive/2/20/20170929214604%21T--Florida_Atlantic--owlbanner.png">
<h3>★  ALERT! </h3>
+
<div align='justify'>
<p>This page is used by the judges to evaluate your team for the <a href="https://2017.igem.org/Judging/Medals">medal criterion</a> or <a href="https://2017.igem.org/Judging/Awards"> award listed above</a>. </p>
+
<p> Delete this box in order to be evaluated for this medal criterion and/or award. See more information at <a href="https://2017.igem.org/Judging/Pages_for_Awards"> Instructions for Pages for awards</a>.</p>
+
</div>
+
<div class="clear"></div>
+
 
+
 
<div class="column full_size">
 
<div class="column full_size">
<h1> Modeling</h1>
+
<h3 style=" font-size:24px ; ">Model</h3>
 +
<center><h4>LSTM Machine Learning Model</h4></center>
 +
<form><fieldset>
 +
<p style=" font-size:18px ; ">The LSTM model contained two lstm layers
 +
with 300 nodes each. The first lstm layer used dropout with a dropout
 +
probability of 0.5 to avoid overfitting the training data. The third
 +
layer of the network was a fully-connected layer with 150 nodes and
 +
hyperbolic tangent activation function. The output layer contained two
 +
nodes with a softmax activation function.<p>
  
<p>Mathematical models and computer simulations provide a great way to describe the function and operation of BioBrick Parts and Devices. Synthetic Biology is an engineering discipline, and part of engineering is simulation and modeling to determine the behavior of your design before you build it. Designing and simulating can be iterated many times in a computer before moving to the lab. This award is for teams who build a model of their system and use it to inform system design or simulate expected behavior in conjunction with experiments in the wetlab.</p>
+
</fieldset>
 +
</form>
  
</div>
+
<img  style="max-width:95%;border:3px solid darkred;"src="https://static.igem.org/mediawiki/2017/e/ee/T--Florida_Atlantic--LSTMGraph.png">
<div class="clear"></div>
+
 
+
<div class="column half_size">
+
<h3> Gold Medal Criterion #3</h3>
+
<p>
+
To complete for the gold medal criterion #3, please describe your work on this page and fill out the description on your <a href="https://2017.igem.org/Judging/Judging_Form">judging form</a>. To achieve this medal criterion, you must convince the judges that your team has gained insight into your project from modeling. You may not convince the judges if your model does not have an effect on your project design or implementation.
+
</p>
+
 
+
<p>
+
Please see the <a href="https://2017.igem.org/Judging/Medals"> 2017 Medals Page</a> for more information.
+
</p>
+
</div>
+
 
+
<div class="column half_size">
+
<h3>Best Model Special Prize</h3>
+
 
+
<p>
+
To compete for the <a href="https://2017.igem.org/Judging/Awards">Best Model prize</a>, please describe your work on this page  and also fill out the description on the <a href="https://2017.igem.org/Judging/Judging_Form">judging form</a>. Please note you can compete for both the gold medal criterion #3 and the best model prize with this page.
+
<br><br>
+
You must also delete the message box on the top of this page to be eligible for the Best Model Prize.
+
</p>
+
 
+
</div>
+
<div class="clear"></div>
+
 
+
<div class="column full_size">
+
<h5> Inspiration </h5>
+
<p>
+
Here are a few examples from previous teams:
+
</p>
+
<ul>
+
<li><a href="https://2016.igem.org/Team:Manchester/Model">Manchester 2016</a></li>
+
<li><a href="https://2016.igem.org/Team:TU_Delft/Model">TU Delft 2016  </li>
+
<li><a href="https://2014.igem.org/Team:ETH_Zurich/modeling/overview">ETH Zurich 2014</a></li>
+
<li><a href="https://2014.igem.org/Team:Waterloo/Math_Book">Waterloo 2014</a></li>
+
</ul>
+
  
  

Latest revision as of 21:54, 1 November 2017

Florida_Atlantic

Model

LSTM Machine Learning Model

The LSTM model contained two lstm layers with 300 nodes each. The first lstm layer used dropout with a dropout probability of 0.5 to avoid overfitting the training data. The third layer of the network was a fully-connected layer with 150 nodes and hyperbolic tangent activation function. The output layer contained two nodes with a softmax activation function.