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Repository: martinjm97/ENNUI
Branch: master
Commit: 49beaee37579
Files: 45
Total size: 463.4 KB
Directory structure:
gitextract_rmz9kxio/
├── .gitignore
├── LICENSE.txt
├── README.md
├── build_prod.sh
├── index.html
├── npm-shrinkwrap.json
├── package.json
├── robots.txt
├── src/
│ ├── model/
│ │ ├── build_network.ts
│ │ ├── code_generation.ts
│ │ ├── data.ts
│ │ ├── export_model.ts
│ │ ├── graphs.ts
│ │ ├── julia_skeleton.ts
│ │ ├── mnist_model.ts
│ │ ├── params_object.ts
│ │ ├── python_skeleton.ts
│ │ └── save_state_url.ts
│ └── ui/
│ ├── app.ts
│ ├── error.ts
│ ├── model_templates.ts
│ ├── shapes/
│ │ ├── activation.ts
│ │ ├── activationlayer.ts
│ │ ├── draggable.ts
│ │ ├── layer.ts
│ │ ├── layers/
│ │ │ ├── add.ts
│ │ │ ├── batchnorm.ts
│ │ │ ├── concatenate.ts
│ │ │ ├── convolutional.ts
│ │ │ ├── dense.ts
│ │ │ ├── dropout.ts
│ │ │ ├── flatten.ts
│ │ │ ├── input.ts
│ │ │ ├── maxpooling.ts
│ │ │ └── output.ts
│ │ ├── shape.ts
│ │ ├── textbox.ts
│ │ ├── wire.ts
│ │ └── wireguide.ts
│ ├── style.scss
│ ├── utils.ts
│ └── window.ts
├── tsconfig.json
├── tslint.json
└── webpack.config.js
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FILE CONTENTS
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FILE: .gitignore
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__pycache__
*.pyc
src/ui/style.css
built/*
node_modules/
*.log
dist/*
src/model/test.py
prod/*
.vscode/
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FILE: LICENSE.txt
================================================
Copyright 2020 Jesse Michel, Stefan Grosser, Zachary Holbrook, and Rikhav Shah
Permission is hereby granted, free of charge, to any person obtaining a copy of this software
and associated documentation files (the "Software"), to deal in the Software without restriction,
including without limitation the rights to use, copy, modify, merge, publish, distribute,
sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or
substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING
BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM,
DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
================================================
FILE: README.md
================================================
# ENNUI
ENNUI is an Elegant Neural Network User Interface that allows users to:
- Build neural network architectures with a drag and drop interface.
- Train those networks on the browser.
- Visualize the training process.
- Export to Python.
Thanks to Gil Strang, it's hosted at [https://math.mit.edu/ENNUI/](https://math.mit.edu/ENNUI/)
<img src="resources/ennui-resnet-train.png" width="600px"/>
# Installation
After cloning the repo, to install dependencies, run:
`npm install`
To Build the app, run:
`npm run build`
For Continuous Build and Integration, run:
`npm run watch`
To use chrome with localhost and the CIFAR-10 Dataset, make sure to close
all chrome windows and run the following command to launch chrome:
'path/to/chrome.exe --allow-file-access-from-files'
# Code Structure
ENNUI is frontend-only. The implementation is structed in two parts:
- `ENNUI/src/ui` is home to the traditional frontend components such as the styling, the button logic, and the draggable components (e.g. the layers and activations).
- `ENNUI/src/model` is the "backend" implementation supporting core functionality such as building neural networks, doing the code generation, and saving the state in the browser.
================================================
FILE: build_prod.sh
================================================
# Build
npm run build
# Create the folder if it doesn't exist
folder="prod"
if [ ! -d "$folder" ]; then
mkdir $folder
fi
# Copy index.html
cp ./index.html $folder
# Fix the paths to style.css and bundle.js
sed -i 's/src\/ui\///' $folder/index.html
sed -i 's/dist\///' $folder/index.html
# Copy style.css
uglifycss ./src/ui/style.css > $folder/style.css
# Copy favicon
cp ./favicon.ico $folder
# Uglify and copy bundle.js
uglifyjs --compress --mangle --wrap -- dist/bundle.js > $folder/bundle.js
cp -R ./dist/ $folder/
================================================
FILE: index.html
================================================
<meta charset="utf-8"/>
<html>
<head>
<meta charset="utf-8">
<meta name="description" content="Welcome to ENNUI - An elegant neural network user interface which allows you to easily design, train, and visualize neural networks.">
<title>ENNUI ~ Elegant Neural Network User Interface ~</title>
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<script type="text/javascript" async
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<!-- JSON-LD markup generated by Google Structured Data Markup Helper. -->
<script type="application/ld+json">
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"author" : [ {
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"name" : "Hendrik Strobelt"
}, {
"@type" : "Person",
"name" : "Rikhav Shah"
} ]
}
</script>
<!-- Global site tag (gtag.js) - Google Analytics -->
<script async src="https://www.googletagmanager.com/gtag/js?id=UA-133726432-1"></script>
<script>
window.dataLayer = window.dataLayer || [];
function gtag(){dataLayer.push(arguments);}
gtag('js', new Date());
gtag('config', 'UA-133726432-1');
</script>
<link rel="icon" type="image/x-icon" sizes="16x16" href="favicon.ico">
<link rel='stylesheet' href='src/ui/style.css'>
<script src='dist/bundle.js'></script>
</head>
<body>
<h1 style="display:none">ENNUI ~ Elegant Neural Network User Interface ~</h1>
<p style="display:none">ENNUI helps people learn about deep learning by building, training, and visualizing deep neural networks on the browser. It has an easy to use drag-and-drop interface. When you're ready to start coding you can export the network to produce code in Python or Julia! </p>
<h6 style="display:none">About ENNUI</h6>
<p style="display:none">
ENNUI provides several tools for all stages of deep learning development. The canvas gives space to design neural network architectures with a drag-and-drop interface. This design is easily sharable with friends and coworkers by exporting to a link. <br/>
Not only can you design neural networks, you can train them on several datasets: MNIST, CIFAR-10, and more! During training, you can track your network loss and accuracies in the Progress tab, as well as view of confusion matrix. <br/>
Once training is complete, ENNUI provides a suite of neural network visualization tools to better understand your architecture. <br/>
ENNUI is constantly updated with new features, so be sure to keep following!
</p>
<div id = 'main'>
<!-- The leftmost strip to select tabs -->
<div id = 'tabselector'>
<div id = 'blanktab' class='top_neighbor_tab-selected'> </div>
<div title = 'Network' class = 'tab-selected option tab-option' id = 'network' data-optionValue = 'network'>
<svg class = 'icon' xmlns="http://www.w3.org/2000/svg" width="24" height="24" viewBox="0 0 24 24"><path fill="none" d="M0 0h24v24H0V0z"/><path d="M11.99 18.54l-7.37-5.73L3 14.07l9 7 9-7-1.63-1.27zM12 16l7.36-5.73L21 9l-9-7-9 7 1.63 1.27L12 16zm0-11.47L17.74 9 12 13.47 6.26 9 12 4.53z"/></svg>
</div>
<div title = 'Progress' class = 'option tab-option bottom_neighbor_tab-selected' id = 'progress' data-optionValue = 'progress'>
<svg class = 'icon' xmlns="http://www.w3.org/2000/svg" width="24" height="24" viewBox="0 0 24 24"><path fill="none" d="M0 0h24v24H0V0z"/><path d="M13.5 13.48l-4-4L2 16.99l1.5 1.5 6-6.01 4 4L22 6.92l-1.41-1.41z"/></svg>
</div>
<div title = 'Visualization' class = 'option tab-option' id = 'visualization' data-optionValue = 'visualization'>
<svg class = 'icon' xmlns="http://www.w3.org/2000/svg" width="24" height="24" viewBox="0 0 24 24"><path fill="none" d="M0 0h24v24H0z"/><path d="M11 9h2v2h-2V9zm-2 2h2v2H9v-2zm4 0h2v2h-2v-2zm2-2h2v2h-2V9zM7 9h2v2H7V9zm12-6H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 18H7v-2h2v2zm4 0h-2v-2h2v2zm4 0h-2v-2h2v2zm2-7h-2v2h2v2h-2v-2h-2v2h-2v-2h-2v2H9v-2H7v2H5v-2h2v-2H5V5h14v6z"/></svg>
</div>
<div id = 'middleblanktab' > </div>
<div title = 'Education' class = 'option tab-option' id = 'education' data-optionValue = 'education'>
<svg class = 'icon' xmlns="http://www.w3.org/2000/svg" width="24" height="24" viewBox="0 0 24 24"><path fill="none" d="M0 0h24v24H0V0z"/><path d="M12 3L1 9l4 2.18v6L12 21l7-3.82v-6l2-1.09V17h2V9L12 3zm6.82 6L12 12.72 5.18 9 12 5.28 18.82 9zM17 15.99l-5 2.73-5-2.73v-3.72L12 15l5-2.73v3.72z"/></svg>
</div>
<div id = 'bottomblanktab' > </div>
</div>
<!-- The left panel (menu) -->
<div id = 'menu'>
<div id = 'networkMenu'>
<div id = 'layers' class = 'category'>
<div class = 'categoryTitle' data-expanded = 'true'>
<div class='expander'>
<svg height="24px" width="24px">
<path d="M16.59 8.59L12 13.17 7.41 8.59 6 10l6 6 6-6z" style="fill:#FFFFFF;"></path>
</svg>
</div>
<div class='categoryTitleText'>
Layers
</div>
</div>
<div class = 'option select-option' data-optionValue = 'dense'> Dense </div>
<div class = 'option select-option' data-optionValue = 'conv2D'> Convolution </div>
<div class = 'option select-option' data-optionValue = 'maxPooling2D'> Max Pooling </div>
<div class = 'option-dropdown'>
<div style="float:left">More</div>
<div style="float:right">〉</div>
<div class='dropdown-content left'>
<div title = 'Modify batches of data during training to make them more similar, resulting in faster convergence and better results.'
class = 'option select-option' data-optionValue = 'batchnorm'> Batch Normalization</div>
<div title = 'Ignore a different random portion of the weights each batch for better generalization and faster training.'
class = 'option select-option' data-optionValue = 'dropout'> Dropout</div>
<div title = 'Flatten a list of 2D images into a 1D feature vector.'
class = 'option select-option' data-optionValue = 'flatten'> Flatten</div>
<div title = 'Concatenate two or more inputs that are all 1D or all 2D.'
class = 'option select-option' data-optionValue = 'concatenate'> Concatenate</div>
<div title = 'Add two or more inputs together.'
class = 'option select-option last-dropdown' data-optionValue = 'add'> Add</div>
</div>
</div>
</div>
<div id = 'activations' class = 'category'>
<div class = 'categoryTitle' data-expanded = 'true'>
<div class='expander'>
<svg height="24px" width="24px">
<path d="M16.59 8.59L12 13.17 7.41 8.59 6 10l6 6 6-6z" style="fill:#FFFFFF;"></path>
</svg>
</div>
<div class='categoryTitleText'>
Activations
</div>
</div>
<div class = 'option select-option' data-optionValue = 'relu'> ReLU </div>
<div class = 'option select-option' data-optionValue = 'sigmoid'> Sigmoid </div>
<div class = 'option select-option' data-optionValue = 'tanh'> Tanh </div>
</div>
<div id = 'templates' class = 'bottomCategory'>
<div class = 'categoryTitle' data-expanded = 'true'>
<div class='expander'>
<svg height="24px" width="24px">
<path d="M16.59 8.59L12 13.17 7.41 8.59 6 10l6 6 6-6z" style="fill:#FFFFFF;"></path>
</svg>
</div>
<div class='categoryTitleText'>
Templates
</div>
</div>
<div class = 'option select-option' data-optionValue = 'blank'> Blank </div>
<div class = 'option select-option' data-optionValue = 'default'> Default </div>
<div class = 'option select-option' data-optionValue = 'resnet'> ResNet </div>
</div>
</div>
<div id = 'progressMenu' style="display: none">
<div id = 'optimizers' class = 'category'>
<div class = 'categoryTitle' data-expanded = 'true'>
<div class='expander'>
<svg height="24px" width="24px">
<path d="M16.59 8.59L12 13.17 7.41 8.59 6 10l6 6 6-6z" style="fill:#FFFFFF;"></path>
</svg>
</div>
<div class='categoryTitleText'>
Optimizers
</div>
</div>
<div id = "defaultOptimizer" class = 'option select-option selected' id = 'sgd' data-optionValue = 'sgd'> SGD </div>
<div id = 'rmsprop' class = 'option select-option' data-optionValue = 'rmsprop'> RMSprop </div>
<div id = 'adagrad' class = 'option select-option' data-optionValue = 'adagrad'> Adagrad </div>
<div id = 'adam' class = 'option select-option' data-optionValue = 'adam'> Adam </div>
</div>
<div id = 'losses' class = 'category'>
<div class = 'categoryTitle' data-expanded = 'true'>
<div class='expander'>
<svg height="24px" width="24px">
<path d="M16.59 8.59L12 13.17 7.41 8.59 6 10l6 6 6-6z" style="fill:#FFFFFF;"></path>
</svg>
</div>
<div class='categoryTitleText'>
Losses
</div>
</div>
<div id = 'defaultLoss' class = 'option select-option selected' data-optionValue = 'categoricalCrossentropy'>CrossEntropy</div>
<div id = 'hinge' class = 'option select-option' data-optionValue = 'hinge'> Hinge </div>
<div id = 'meanSquaredError' class = 'option select-option' data-optionValue = 'meanSquaredError'> MSE </div>
<div id = 'meanAbsoluteError' class = 'option select-option' data-optionValue = 'meanAbsoluteError'> MAE </div>
</div>
</div>
<div id = 'visualizationMenu' style="display: none">
<div id = 'classes' class = 'category'>
<div class = 'categoryTitle' data-expanded = 'true'>
<div class='expander'>
<svg height="24px" width="24px">
<path d="M16.59 8.59L12 13.17 7.41 8.59 6 10l6 6 6-6z" style="fill:#FFFFFF;"></path>
</svg>
</div>
<div class='categoryTitleText'>
Classes
</div>
</div>
<div class = 'option select-option selected' data-optionValue = 'all'> All </div>
<div class = 'option select-option' data-optionValue = '0'> 0 </div>
<div class = 'option select-option' data-optionValue = '1'> 1 </div>
<div class = 'option select-option' data-optionValue = '2'> 2 </div>
<div class = 'option select-option' data-optionValue = '3'> 3 </div>
<div class = 'option select-option' data-optionValue = '4'> 4 </div>
<div class = 'option select-option' data-optionValue = '5'> 5 </div>
<div class = 'option select-option' data-optionValue = '6'> 6 </div>
<div class = 'option select-option' data-optionValue = '7'> 7 </div>
<div class = 'option select-option' data-optionValue = '8'> 8 </div>
<div class = 'option select-option' data-optionValue = '9'> 9 </div>
</div>
</div>
<div id = 'educationMenu' style="display: none">
<div id = 'educationLayers' class = 'category'>
<div class = 'categoryTitle' data-expanded = 'true'>
<div class='expander'>
<svg height="24px" width="24px">
<path d="M16.59 8.59L12 13.17 7.41 8.59 6 10l6 6 6-6z" style="fill:#FFFFFF;"></path>
</svg>
</div>
<div class='categoryTitleText'>
Articles
</div>
</div>
<div class = 'option select-option education-option' data-optionValue = 'Overview'> Overview </div>
<div class = 'option select-option education-option' data-optionValue = 'Overfitting'> Overfitting</div>
<div class = 'option select-option education-option' data-optionValue = 'ResNets'> ResNets </div>
</div>
<div id = 'educationStory' class = 'category'>
<div class = 'categoryTitle' data-expanded = 'true'>
<div class='expander'>
<svg height="24px" width="24px">
<path d="M16.59 8.59L12 13.17 7.41 8.59 6 10l6 6 6-6z" style="fill:#FFFFFF;"></path>
</svg>
</div>
<div class='categoryTitleText'>
Layers
</div>
</div>
<div class = 'option select-option education-option' data-optionValue = 'Concatenate'> Concatenate</div>
<div class = 'option select-option education-option' data-optionValue = 'Convolution'> Convolution </div>
<div class = 'option select-option education-option' data-optionValue = 'Dropout'> Dropout</div>
<div class = 'option select-option education-option' data-optionValue = 'Flatten'> Flatten</div>
</div>
</div>
</div>
<!-- The middle canvas -->
<div id = 'middle'>
<div id = 'networkTab'>
<svg id = 'svg'> </svg>
</div>
<div id = 'progressTab' style="display: none">
<div id="loss-canvas"></div>
<div id="accuracy-canvas"></div>
<div id="confusion-matrix-canvas"></div>
</div>
<div id = 'visualizationTab' style="display: none">
<div id='visulaization'></div>
<div id='images'></div>
</div>
<div id = 'informationOverlay'>
<div id='information'>Welcome to ENNUI
<div id="informationBody">~ an elegant neural network user interface ~</div>
<div class="informationRow">
<div class="informationColumn">
Start Building <br></br>
<svg class = 'icon' xmlns="http://www.w3.org/2000/svg" width="30%" max-height="30%" viewBox="0 0 24 24"><path fill="none" d="M0 0h24v24H0V0z"/><path d="M11.99 18.54l-7.37-5.73L3 14.07l9 7 9-7-1.63-1.27zM12 16l7.36-5.73L21 9l-9-7-9 7 1.63 1.27L12 16zm0-11.47L17.74 9 12 13.47 6.26 9 12 4.53z"/></svg>
</div>
<div class="informationBlankColumn"></div>
<div id="informationEducation" class="informationColumn">
Explore Deep Learning <br></br>
<svg class = 'icon' xmlns="http://www.w3.org/2000/svg" width="30%" max-height="30%" viewBox="0 0 24 24"><path fill="none" d="M0 0h24v24H0V0z"/><path d="M12 3L1 9l4 2.18v6L12 21l7-3.82v-6l2-1.09V17h2V9L12 3zm6.82 6L12 12.72 5.18 9 12 5.28 18.82 9zM17 15.99l-5 2.73-5-2.73v-3.72L12 15l5-2.73v3.72z"/></svg>
</div>
</div>
<div id = 'acknowledgements'>
Developed by (<a class="overlayLinks" href="mailto:ennui-devs@mit.edu">ennui-devs@mit.edu</a>) <br>
Jesse Michel, Zack Holbrook, Stefan Grosser, Rikhav Shah <br>
with advising from <a class="overlayLinks" href="http://hendrik.strobelt.com/" target="_blank">Hendrik Strobelt</a> and <a class="overlayLinks" href="http://www-math.mit.edu/~gs/" target="_blank">Gilbert Strang</a>.<br>
First prototyped at <a class="overlayLinks" href="https://devpost.com/software/ennui" target="_blank">HackMIT</a>.
Open-sourced on <a class="overlayLinks" href="https://github.com/martinjm97/ENNUI" target="_blank">GitHub</a>.
</div>
</div>
</div>
<div id = 'educationTab' style="display: none">
<div id="educationOverview">
<div class="educationTitle" style="padding-top: 0px"> Learn About Deep Neural Networks </div>
<div class="educationSection"> Introduction to ENNUI </div>
<div class="educationContent">
If you are new to ENNUI, a good place to start is to watch
the quick tutorial below:
</div>
<!--<iframe class="educationVideo" src="https://www.youtube.com/embed/m0YnwAtPbb8" frameborder="0" allow="accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe> -->
<div style="text-align: center; font-size: 25px;"><a href="https://www.youtube.com/watch?v=m0YnwAtPbb8" target="blank">ENNUI Tutorial</a></div>
<div class="educationSection"> Foundations of Deep Learning </div>
<div class="educationContent">
If you are new to machine learning, the lecture video below is an excellent introduction.
This lecture is from the 18.065 class from Spring of 2019. If you want to learn more check out the <a href="http://math.mit.edu/~gs/learningfromdata/">learning from data textbook</a>.
</div>
<div style="text-align: center; font-size: 25px;"><a href="https://video.odl.mit.edu/videos/9101a72a7d994d53800d1398fd885b88/embed/?start=339" target="blank">Gilbert Strang: Deep Learning</a></div>
<!-- <iframe class="educationVideo" src="https://video.odl.mit.edu/videos/9101a72a7d994d53800d1398fd885b88/embed/?start=339" scrolling="no" frameborder="0" allowfullscreen></iframe> -->
</div>
<div id="educationConvolution">
<div class="educationTitle"> Convolutional Nets </div>
<div class="educationAuthor">by <i>Gilbert Strang</i></div>
<div class="educationContent">
<p>The words <strong>weight sharing</strong> give the key idea of CNNs. The weight matrix <span class="math inline">\(A\)</span> that connects one layer to the next layer has only a small number of independent entries. So optimizing those weights is much faster than for a fully-connected (dense) architecture.</p>
<p>In a one-dimensional problem, suppose the inputs (layer zero) are given by a vector <span class="math inline">\(v = (v_1,...,v_n)\)</span>. A convolution will multiply <span class="math inline">\(v\)</span> by a weight matrix <span class="math inline">\(A\)</span> with constant diagonals. Then the same set of weights (say 3 weights) is repeated all along the layer :</p>
<p><span class="math display">\[A=
\begin{bmatrix}
a_{-1} & a_0 & a_1 & & \\
& a_{-1} & a_0 & a_1 & \\
& & a_{-1} & a_0 & a_1 \\
\end{bmatrix}\]</span> This <span class="math inline">\(A\)</span> has <span class="math inline">\(n = 5\)</span> inputs and <span class="math inline">\(m = 3\)</span> outputs. A is <strong>shift-invariant</strong>: a convolution = filter = Toeplitz matrix. Convolutions are especially important for images with many pixels. The <span class="math inline">\(3\)</span> independent weights <span class="math inline">\(a_{-1}, a_0, a_1\)</span> might change to <span class="math inline">\(3 \times 3=9\)</span> weights in 2D. Those 9 numbers are <span class="math inline">\(a_{ij}\)</span> for <span class="math inline">\(i = -1,0,1\)</span> and <span class="math inline">\(j = -1,0,1\)</span>. An input with its 8 neighbors (in a <span class="math inline">\(3 \times 3\)</span> square) is multiplied by <span class="math inline">\(a_{00}\)</span> and its <span class="math inline">\(8\)</span> neighbors–that sum of 9 terms gives one output in <span class="math inline">\(Av\)</span>. As usual, a bias vector <span class="math inline">\(b\)</span> is added and each component of <span class="math inline">\(Av + b\)</span> is activated (or not) by a function like ReLU : the new layer contains <span class="math inline">\(\textrm{ReLU}(Av + b)\)</span>.</p>
<p>This 2D matrix <span class="math inline">\(A\)</span> is not so easy to display. You should see that a <span class="math inline">\(3 \times 3\)</span> square around an input vector <span class="math inline">\(v\)</span> of size <span class="math inline">\(n \times n\)</span> will produce an output <span class="math inline">\(Av\)</span> of size <span class="math inline">\((n - 2) \times (n - 2)\)</span>, in the same way that 5 inputs gave 3 outputs in 1D. Notice that we only had <span class="math inline">\(3 \times 3 = 9\)</span> (or maybe <span class="math inline">\(5 \times 5 = 25\)</span>) independent weights in 2D, because the convolution not only <strong>shares weights</strong> but it is <strong>local</strong>.</p>
</div>
</div>
<div id="educationResNets">
<div class="educationTitle"> Residual Networks (ResNets) </div>
<div class="educationAuthor">by <i>Zack Holbrook</i> and <i>Jesse Michel</i></div>
<div class="educationContent">
<p>In 2015, a research team at Microsoft won the <a href="http://www.image-net.org/challenges/LSVRC/">ImageNet Large Scale Visual Recognition Challenge</a> with record performance using a ResNet. Since 2015, variants of ResNets have dominated the competition, exceeding human performance in the given task. They have become a popular choice of architecture for a broad variety of image recognition tasks and are relatively easy to implement and train.</p>
<div class="educationSection">ResNet Architecture</div>
<p>ResNets are a type of convolutional neural network (CNN) that have <strong>identity shortcuts</strong>, which are paths through the network that skip layers, thereby creating shortcuts through the network. We provide a typical example of a ResNet below:
<img class="educationImage" src="dist/resnet.png" alt="Resnet image" width="50%">
<div class="modelLink">
<a class="modelLink" target="_newtab" href="http://math.mit.edu/ennui/#%7B%22graph%22:%5B%7B%22layer_name%22:%22Input%22,%22children_ids%22:%5B5,9%5D,%22parent_ids%22:%5B%5D,%22params%22:%7B%22dataset%22:%22mnist%22%7D,%22id%22:0,%22xPosition%22:100,%22yPosition%22:377%7D,%7B%22layer_name%22:%22Conv2D%22,%22children_ids%22:%5B6%5D,%22parent_ids%22:%5B0%5D,%22params%22:%7B%22filters%22:16,%22kernelSize%22:%5B3,3%5D,%22strides%22:%5B1,1%5D,%22activation%22:%22relu%22%7D,%22id%22:5,%22xPosition%22:169,%22yPosition%22:280%7D,%7B%22layer_name%22:%22Add%22,%22children_ids%22:%5B7,10%5D,%22parent_ids%22:%5B0,6%5D,%22params%22:%7B%22activation%22:%22relu%22%7D,%22id%22:9,%22xPosition%22:276,%22yPosition%22:411%7D,%7B%22layer_name%22:%22Conv2D%22,%22children_ids%22:%5B9%5D,%22parent_ids%22:%5B5%5D,%22params%22:%7B%22filters%22:16,%22kernelSize%22:%5B3,3%5D,%22strides%22:%5B1,1%5D%7D,%22id%22:6,%22xPosition%22:294,%22yPosition%22:280%7D,%7B%22layer_name%22:%22Conv2D%22,%22children_ids%22:%5B8%5D,%22parent_ids%22:%5B9%5D,%22params%22:%7B%22filters%22:16,%22kernelSize%22:%5B3,3%5D,%22strides%22:%5B1,1%5D,%22activation%22:%22relu%22%7D,%22id%22:7,%22xPosition%22:414,%22yPosition%22:280%7D,%7B%22layer_name%22:%22Add%22,%22children_ids%22:%5B11%5D,%22parent_ids%22:%5B9,8%5D,%22params%22:%7B%22activation%22:%22relu%22%7D,%22id%22:10,%22xPosition%22:521,%22yPosition%22:412%7D,%7B%22layer_name%22:%22Conv2D%22,%22children_ids%22:%5B10%5D,%22parent_ids%22:%5B7%5D,%22params%22:%7B%22filters%22:16,%22kernelSize%22:%5B3,3%5D,%22strides%22:%5B1,1%5D%7D,%22id%22:8,%22xPosition%22:541,%22yPosition%22:280%7D,%7B%22layer_name%22:%22Flatten%22,%22children_ids%22:%5B12%5D,%22parent_ids%22:%5B10%5D,%22params%22:%7B%7D,%22id%22:11,%22xPosition%22:708,%22yPosition%22:463%7D,%7B%22layer_name%22:%22Dense%22,%22children_ids%22:%5B13%5D,%22parent_ids%22:%5B11%5D,%22params%22:%7B%22units%22:32,%22activation%22:%22relu%22%7D,%22id%22:12,%22xPosition%22:702,%22yPosition%22:434%7D,%7B%22layer_name%22:%22Dropout%22,%22children_ids%22:%5B1%5D,%22parent_ids%22:%5B12%5D,%22params%22:%7B%22rate%22:0.5%7D,%22id%22:13,%22xPosition%22:778,%22yPosition%22:365%7D,%7B%22layer_name%22:%22Output%22,%22children_ids%22:%5B%5D,%22parent_ids%22:%5B13%5D,%22params%22:%7B%7D,%22id%22:1,%22xPosition%22:900,%22yPosition%22:377%7D%5D,%22hyperparameters%22:%7B%22learningRate%22:0.01,%22batchSize%22:64,%22optimizer_id%22:%22defaultOptimizer%22,%22epochs%22:6,%22loss_id%22:%22defaultLoss%22%7D%7D">
Model Link
</a>
</div>
Identity shortcuts mean that the learned parameters are residuals. Mathematically, if <span class="math inline">\(R(x)\)</span> is a sequence of convolutional layers with ReLUs, known as a <strong>residual block</strong>, for example, let <span class="math display">\[R(x) = \textrm{Conv}(\textrm{ReLU}(\textrm{Conv}(x))).\]</span> Then the output of the residual block will be <span class="math inline">\(R(x) + x\)</span>, where <span class="math inline">\(x\)</span> is the identity pass-through. If the neural network is trying to approximate some function <span class="math inline">\(F(x)\)</span>, then a perfect residual block <span class="math inline">\(R^*(x)\)</span> will be such that <span class="math inline">\(R^*(x) = F(x) - x\)</span>, which is exactly a residual after subtracting the input image.</p>
<div class="educationSection">Advantages of ResNets</div>
<p>The amazing property of ResNets is its ability to scale well, leading to deeper neural networks that still train well. As networks get larger, a number of problems arise.</p>
<p>Large networks tend to train slowly, but the <strong>weight sharing</strong> of CNNs mean that each residual block has relatively few parameters to train. Large networks also tend to have the problem of the <strong>vanishing gradient</strong> – where weight updates from gradient descent diminish to the point where the network does not improve even with more training time. The identity shortcuts in ResNets give a path for the gradients to flow, avoiding the problem of the vanishing gradient.</p>
</div>
</div>
<div id="educationFlatten">
<div class="educationTitle"> Flatten Layers </div>
<div class="educationAuthor">by <i>Zack Holbrook</i> and <i>Jesse Michel</i></div>
<div class="educationContent">
<p>The flatten layer takes in a multidimensional input and produces a single dimensional output. For example, the CIFAR dataset is a collection of images which is 3-dimensional since it is a collection of 2-dimensional images that are 32x32 pixels and has 3 color channels (Red, Green, Blue). A Flatten layer could take data from this dataset as an input, and would produce a 1 dimensional vector of size 32*32*3 = 3072.</p>
</div>
</div>
<div id="educationConcatenate">
<div class="educationTitle"> Concatenate Layers </div>
<div class="educationAuthor">by <i>Zack Holbrook</i> and <i>Jesse Michel</i></div>
<div class="educationContent">
<p>A concatenate layer takes two or more layers and concatenates the outputs into a single output by stacking the inputs. For example, it would concatenate two vectors of size 10 into a vector of size 20 by stacking one on top of the other.</p>
</div>
</div>
<div id="educationDropout">
<div class="educationTitle"> Dropout Layers </div>
<div class="educationAuthor">by <i>Stefan Grosser</i> and <i>Jesse Michel</i></div>
<div class="educationContent">
<p>Dropout layers ignore a random fraction of the incoming units during training time. For example, if the rate is 0.1, then on each forward pass the dropout layer will randomly select 10% of the weights and assign them to 0. Adding a dropout layer with a rate of 0 will make no change to the network, while a rate of 1 will have the dropout layer output 0.</p>
<p>Dropout is commonly used to prevent <strong>overfitting</strong> (for more information, see our write-up on the topic). One may think of dropout as having the network learn a collection of weak classifiers that during testing come together to create a more powerful classifier. For those who are familiar with this terminology, it is boosting using an ensemble model. It also has the convenient property of speeding up training since fewer weights are required in each forward pass.</p>
</div>
</div>
<div id="educationOverfitting">
<div class="educationTitle"> Overfitting </div>
<div class="educationAuthor">by <i>Stefan Grosser</i> and <i>Jesse Michel</i></div>
<div class="educationContent">
<p>A neural network sometimes learns too well. It identifies trends specific only to the training data and thus fails to <strong>generalize</strong>. This problem of fitting the training data too closely is called <strong>overfitting</strong>. The following figure shows the decision boundary – the curve that determines the prediction of the classifier – in the cases of underfitting, fitting well (normal), and overfitting.</p>
<img class="educationImage" src="dist/overfitti_ng.png" alt="Possible decision boundaries " />
<div class="modelLink">
<a class="modelLink" target="_newtab" href="http://mlwiki.org/index.php/Overfitting">
Source: ML Wiki
</a>
</div>
<p>When a classifier overfits, it performs far better on the training data than on the test data. Therefore, the training accuracy will be much higher than the validation accuracy and the training loss will be much lower than the validation loss. We have provided an example of this below. </p>
<img class="educationImage" style="float: left; max-width: 50%;" src="dist/loss_overfit.png" title="fig:" alt="Visualization of overfitting during training" />
<img class="educationImage" style="float: right; max-width: 50%;" src="dist/accuracy_overfit.png" title="fig:" alt="Visualization of overfitting during training" />
<div style="margin-top:10px;">
<!-- TODO: padding above -->
The architecture used for this example is shown below.
</div>
<div class="figure">
<img class="educationImage" style="max-width: 50%;" src="dist/overfitting_network.png" alt="Network architecture" >
<div class="modelLink">
<a class="modelLink" target="_newtab" href="https://math.mit.edu/ennui/#%7B%22graph%22:%5B%7B%22layer_name%22:%22Input%22,%22children_ids%22:%5B2%5D,%22parent_ids%22:%5B%5D,%22params%22:%7B%22dataset%22:%22cifar%22%7D,%22id%22:0,%22xPosition%22:100,%22yPosition%22:399%7D,%7B%22layer_name%22:%22Conv2D%22,%22children_ids%22:%5B3%5D,%22parent_ids%22:%5B0%5D,%22params%22:%7B%22filters%22:16,%22kernelSize%22:%5B3,3%5D,%22strides%22:%5B1,1%5D,%22kernelRegularizer%22:%22none%22,%22regScale%22:0.1,%22activation%22:%22relu%22%7D,%22id%22:2,%22xPosition%22:261,%22yPosition%22:453%7D,%7B%22layer_name%22:%22Flatten%22,%22children_ids%22:%5B1%5D,%22parent_ids%22:%5B2%5D,%22params%22:%7B%7D,%22id%22:3,%22xPosition%22:585,%22yPosition%22:484%7D,%7B%22layer_name%22:%22Output%22,%22children_ids%22:%5B%5D,%22parent_ids%22:%5B3%5D,%22params%22:%7B%7D,%22id%22:1,%22xPosition%22:900,%22yPosition%22:399%7D%5D,%22hyperparameters%22:%7B%22learningRate%22:0.1,%22batchSize%22:64,%22optimizer_id%22:%22defaultOptimizer%22,%22epochs%22:15,%22loss_id%22:%22defaultLoss%22%7D%7D">
Model Link
</a>
</div>
</div>
<br/><br/>
<p>Overfitting exemplifies why cross validation is so important; without a validation set, we would not be able to identify that the model will fail to generalize.</p>
<p>So, how can we combat overfitting and make sure that a model finds generalizable traits?</p>
<div class="educationSection">Regularization</div>
<p>One way to prevent overfitting is regularization, which takes the form of adding in a new term that guides the model towards a simpler solution. Recall that in classification problems we begin with pairs of inputs and their classifications <span class="math display">\[(x_1, y_1), (x_2, y_2), \dots, (x_n, y_n).\]</span> From this, we want to find a function <span class="math inline">\(f\)</span> that will accurately predict the classes of new samples of data. So if our original problem was <span class="math display">\[\min_f \sum_{i=1}^{n} C(f(x_i), y_i),\]</span> where <span class="math inline">\(C\)</span> calculates the cost of predicting <span class="math inline">\(f(x_i)\)</span> when the true value is <span class="math inline">\(y_i\)</span>, then the regularized loss will be <span class="math display">\[\min_f \sum_{i=1}^{n} C(f(x_i), y_i) + \lambda R(f),\]</span> where <span class="math inline">\(R(f)\)</span> is the regularization term that is defined to be larger when <span class="math inline">\(f\)</span> is more complex and <span class="math inline"> \(\lambda>0\) </span> is a tunable parameter controlling the amount of regularization. There are various definitions of how complex a layer is, but in our case we will say that a layer with a lower <span class="math inline">\(L2\)</span>-norm is less complex. Formally, we define <span class="math inline">\(L2\)</span>-norm for a matrix <span class="math inline">\(A\)</span> as <span class="math display">\[\text{norm}(A) = \sqrt{\sum_i \sum_j a_{ij}^2}.\]</span> For example, given matrix <span class="math display">\[A =
\begin{bmatrix}
1 & 2 \\
0 & -2
\end{bmatrix},\]</span> the L2-norm is <span class="math display">\[||A||_2 = \sqrt{1^2 + 2^2 + 0^2 + (-2)^2} = 3.\]</span></p>
<p>There are several reasons why penalizing an increased <span class="math inline">\(L2\)</span>-norm is a reasonable action. If we assume that a classifier is going to overfit, then adding in the penalty <span class="math inline">\(\lambda R(f)\)</span> will guide the decision boundary away from that state. This can be seen as adding “wiggle room" to the classifier. Moreover, this penalty of a high <span class="math inline">\(L2\)</span>-norm is a way to encourage discarding useless information. The penalty term drives layer weights to be smaller, and the closer a layer weight gets to zero, the smaller its impact is as a feature.</p>
<p>This notion of complexity leads to <span class="math inline">\(L1\)</span>- and <span class="math inline">\(L2\)</span>-norms becoming forms of regularization. In the case of <span class="math inline">\(L2\)</span>-regularization, we can add <span class="math inline">\(\lambda ||W||_2\)</span> to our loss function for a given layer <span class="math inline">\(W.\)</span> There are other ways to regularize, but for now let us take a look at a different approach. </p>
<!-- TODO: Maybe explain L1-regularization in the future -->
<div class="educationSection">Dropout</div>
<p>Another way to fight overfitting is a technique known as dropout. Dropout layers ignore a random fraction of the input units during training time (see our explanation on dropout layers for more information). There are two intuitions for why dropout helps prevent overfitting. Dropout can be seen a type of ensemble learning -- taking a collection of weak (underfit) classifiers and combing their classifications in some way, e.g., taking the majority class. For each batch, a new section of the network is trained as a weak classifier. During validation, the whole network is used, effectively combining all of the classifiers to provide a single result. Another view is that over many runs, dropout forces all parts of a network architecture to be used. Therefore, no one feature of the training set will be too influential, avoiding the network from focusing on artifacts that are only specific to the training set.</p>
<div class="educationSection">Conclusion</div>
<p>Overfitting hinders the performance of classifiers on unseen data. Regularization and dropout are two widely-used and easy-to-implement approaches for combatting overfitting. Combining these methods with cross validation makes it far easier to build more generalizable models.</p>
</div>
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gitextract_rmz9kxio/ ├── .gitignore ├── LICENSE.txt ├── README.md ├── build_prod.sh ├── index.html ├── npm-shrinkwrap.json ├── package.json ├── robots.txt ├── src/ │ ├── model/ │ │ ├── build_network.ts │ │ ├── code_generation.ts │ │ ├── data.ts │ │ ├── export_model.ts │ │ ├── graphs.ts │ │ ├── julia_skeleton.ts │ │ ├── mnist_model.ts │ │ ├── params_object.ts │ │ ├── python_skeleton.ts │ │ └── save_state_url.ts │ └── ui/ │ ├── app.ts │ ├── error.ts │ ├── model_templates.ts │ ├── shapes/ │ │ ├── activation.ts │ │ ├── activationlayer.ts │ │ ├── draggable.ts │ │ ├── layer.ts │ │ ├── layers/ │ │ │ ├── add.ts │ │ │ ├── batchnorm.ts │ │ │ ├── concatenate.ts │ │ │ ├── convolutional.ts │ │ │ ├── dense.ts │ │ │ ├── dropout.ts │ │ │ ├── flatten.ts │ │ │ ├── input.ts │ │ │ ├── maxpooling.ts │ │ │ └── output.ts │ │ ├── shape.ts │ │ ├── textbox.ts │ │ ├── wire.ts │ │ └── wireguide.ts │ ├── style.scss │ ├── utils.ts │ └── window.ts ├── tsconfig.json ├── tslint.json └── webpack.config.js
SYMBOL INDEX (282 symbols across 33 files)
FILE: src/model/build_network.ts
function buildNetworkDAG (line 11) | function buildNetworkDAG(input: Input): tf.LayersModel {
function networkDAG (line 20) | function networkDAG(toposorted: Layer[]): tf.LayersModel {
function cloneNetwork (line 32) | function cloneNetwork(input: Input, newInput: Input): void {
function topologicalSort (line 83) | function topologicalSort(input: Input, showErrors: boolean = true): Laye...
function generateTfjsModel (line 144) | function generateTfjsModel(sorted: Layer[]): tf.LayersModel {
FILE: src/model/code_generation.ts
function generatePython (line 10) | function generatePython(sorted: Layer[]): string {
function generateJulia (line 36) | function generateJulia(sorted: Layer[]): string {
FILE: src/model/data.ts
constant NUM_DATASET_ELEMENTS (line 7) | const NUM_DATASET_ELEMENTS = 65000;
constant NUM_TRAIN_ELEMENTS (line 9) | const NUM_TRAIN_ELEMENTS = 55000;
constant MNIST_IMAGES_SPRITE_PATH (line 12) | const MNIST_IMAGES_SPRITE_PATH =
constant MNIST_LABELS_PATH (line 14) | const MNIST_LABELS_PATH =
method getTrainData (line 47) | public getTrainData(numExamples: number = 15000): {xs: Tensor<tf.Rank.R4...
method getTestData (line 70) | public getTestData(numExamples: number = 1500): {xs: Tensor<tf.Rank.R4>,...
method getTestDataWithLabel (line 92) | public async getTestDataWithLabel(numExamples: number,
method toggleLoadingOverlay (line 110) | protected toggleLoadingOverlay(): void {
class Cifar10Data (line 124) | class Cifar10Data extends ImageData {
method Instance (line 126) | public static get Instance(): ImageData {
method load (line 143) | public async load(): Promise<void> {
class MnistData (line 171) | class MnistData extends ImageData {
method Instance (line 173) | public static get Instance(): ImageData {
method load (line 187) | public async load(): Promise<void> {
function changeDataset (line 262) | function changeDataset(newDataset: string): void {
FILE: src/model/export_model.ts
type ISerializedNetwork (line 23) | interface ISerializedNetwork {
function hasPathToOutput (line 32) | function hasPathToOutput(svgData: IDraggableData): boolean {
function graphToJson (line 55) | function graphToJson(svgData: IDraggableData): ISerializedNetwork {
function setHyperparameterData (line 83) | function setHyperparameterData(): IHyperparameterData {
function stateFromJson (line 122) | function stateFromJson(svgData: IDraggableData, serializedNet: ISerializ...
function setHyperparams (line 134) | function setHyperparams(hyperparamData: IHyperparameterData): void {
function graphFromJson (line 151) | function graphFromJson(svgData: IDraggableData, layersJson: ILayerJson[]...
function createActivationInstanceFromName (line 184) | function createActivationInstanceFromName(svgData: IDraggableData,
function createLayerInstanceFromName (line 211) | function createLayerInstanceFromName(svgData: IDraggableData, lj: ILayer...
function download (line 260) | function download(content: string, filename: string): void {
FILE: src/model/graphs.ts
constant GRAPH_FONT_SIZE (line 7) | const GRAPH_FONT_SIZE: number = 14;
constant NUM_CLASSES (line 8) | const NUM_CLASSES: number = 10;
function showPredictions (line 15) | async function showPredictions(): Promise<void> {
function showConfusionMatrix (line 63) | function showConfusionMatrix(): void {
function setupTestResults (line 82) | function setupTestResults(): void {
function showTestResults (line 109) | function showTestResults(batch: {xs: tf.Tensor<tf.Rank.R4>, labels: tf.T...
function plotLoss (line 142) | function plotLoss(batchNum: number, loss: number, set: string): void {
function renderLossPlot (line 154) | function renderLossPlot(): void {
function resetPlotValues (line 166) | function resetPlotValues(): void {
function plotAccuracy (line 173) | function plotAccuracy(epochs: number, accuracy: number, set: string): vo...
function renderAccuracyPlot (line 181) | function renderAccuracyPlot(): void {
function renderConfusionMatrix (line 195) | function renderConfusionMatrix(): void {
function canvasWidth (line 206) | function canvasWidth(): number {
function canvasHeight (line 211) | function canvasHeight(): number {
function setupPlots (line 217) | function setupPlots(): void {
function draw (line 223) | function draw(image: tf.Tensor, canvas: HTMLCanvasElement): void {
FILE: src/model/julia_skeleton.ts
function juliaSkeleton (line 4) | function juliaSkeleton(initializationCode: string, modelCode: string): s...
FILE: src/model/mnist_model.ts
function train (line 20) | async function train(): Promise<void> {
FILE: src/model/params_object.ts
type IHyperparameterData (line 5) | interface IHyperparameterData {
class NetworkParameters (line 13) | class NetworkParameters {
method isParam (line 21) | public isParam(param: string): boolean {
method getOptimizer (line 25) | public getOptimizer(): tf.Optimizer {
method getPythonLoss (line 44) | public getPythonLoss(): string {
method getPythonOptimizer (line 48) | public getPythonOptimizer(): string {
method getJuliaLoss (line 67) | public getJuliaLoss(): string {
method getJuliaOptimizer (line 86) | public getJuliaOptimizer(): string {
class Model (line 109) | class Model {
method constructor (line 114) | private constructor() {}
method Instance (line 116) | public static get Instance(): Model {
FILE: src/model/python_skeleton.ts
function pythonSkeleton (line 5) | function pythonSkeleton(modelCode: string): string {
FILE: src/model/save_state_url.ts
function storeNetworkInUrl (line 7) | function storeNetworkInUrl(state: ISerializedNetwork): string {
function loadStateIfPossible (line 15) | function loadStateIfPossible(): IDraggableData {
FILE: src/ui/app.ts
type IDraggableData (line 31) | interface IDraggableData {
function addOnClickToOptions (line 101) | function addOnClickToOptions(categoryId: string, func: (optionValue: str...
function setupOptionOnClicks (line 109) | function setupOptionOnClicks(): void {
function selectOption (line 136) | function selectOption(optionCategoryId: string, optionElement: HTMLEleme...
function createTemplate (line 143) | function createTemplate(template: string): void {
function appendItem (line 152) | function appendItem(itemType: string): void {
function setupIndividualOnClicks (line 170) | function setupIndividualOnClicks(): void {
function deleteSelected (line 218) | function deleteSelected(): void {
function trainOnClick (line 225) | async function trainOnClick(): Promise<void> {
function resizeMiddleSVG (line 255) | function resizeMiddleSVG(): void {
function toggleExpanderTriangle (line 291) | function toggleExpanderTriangle(categoryTitle: Element): void {
function makeCollapsable (line 295) | function makeCollapsable(elmt: Element): void {
function setModelHyperparameters (line 320) | function setModelHyperparameters(): void {
function tabSelected (line 349) | function tabSelected(): string {
function switchTab (line 363) | function switchTab(tabType: string): void {
FILE: src/ui/error.ts
function displayError (line 2) | function displayError(error: Error): void {
function clearError (line 9) | function clearError(): void {
FILE: src/ui/model_templates.ts
function resetWorkspace (line 17) | function resetWorkspace(svgData: IDraggableData): void {
function defaultTemplate (line 40) | function defaultTemplate(svgData: IDraggableData): void {
function blankTemplate (line 73) | function blankTemplate(svgData: IDraggableData): void {
function resnetTemplate (line 77) | function resnetTemplate(svgData: IDraggableData): void {
function complexTemplate (line 157) | function complexTemplate(svgData: IDraggableData): void {
FILE: src/ui/shapes/activation.ts
method constructor (line 13) | constructor(color: string, defaultLocation: Point) {
method select (line 24) | public select(): void {
method unselect (line 29) | public unselect(): void {
method delete (line 34) | public delete(): void {
method moveAction (line 43) | public moveAction(): void {
class Relu (line 81) | class Relu extends Activation {
method constructor (line 84) | constructor(defaultLocation: Point = Point.randomPoint(50, 50, Activat...
method getHoverText (line 96) | public getHoverText(): string { return "relu"; }
class Sigmoid (line 100) | class Sigmoid extends Activation {
method constructor (line 103) | constructor(defaultLocation: Point = Point.randomPoint(50, 50, Activat...
method getHoverText (line 114) | public getHoverText(): string { return "sigmoid"; }
class Tanh (line 118) | class Tanh extends Activation {
method constructor (line 121) | constructor(defaultLocation: Point = Point.randomPoint(50, 50, Activat...
method getHoverText (line 132) | public getHoverText(): string { return "tanh"; }
class Softmax (line 135) | class Softmax extends Activation {
method constructor (line 138) | constructor(defaultLocation: Point = Point.randomPoint(50, 50, Activat...
method getHoverText (line 150) | public getHoverText(): string { return "softmax"; }
FILE: src/ui/shapes/activationlayer.ts
method constructor (line 16) | constructor(block: Shape[], defaultLocation: Point = new Point(100, 100)) {
method moveAction (line 23) | public moveAction(): void {
method raiseGroup (line 33) | public raiseGroup(): void {
method delete (line 38) | public delete(): void {
method outerBoundingBox (line 48) | public outerBoundingBox(): {top: number, bottom: number, left: number, r...
method addActivation (line 62) | public addActivation(activation: Activation): void {
method getActivationText (line 73) | public getActivationText(): string {
method removeActivation (line 77) | public removeActivation(): void {
method toJson (line 81) | public toJson(): ILayerJson {
method generateTfjsLayer (line 89) | public generateTfjsLayer(): void {
FILE: src/ui/shapes/draggable.ts
method nodeBoundingBox (line 8) | protected static nodeBoundingBox(node: SVGGraphicsElement):
method constructor (line 37) | constructor(defaultLocation: Point = new Point(50, 100)) {
method makeDraggable (line 69) | public makeDraggable(): void {
method moveAction (line 116) | public moveAction(): void { return; }
method raise (line 119) | public raise(): void {
method raiseGroup (line 123) | public raiseGroup(): void {
method getHoverText (line 128) | public getHoverText(): string { return ""; }
method select (line 130) | public select(): void {
method unselect (line 146) | public unselect(): void {
method delete (line 154) | public delete(): void {
method center (line 160) | public center(): Point {
method outerBoundingBox (line 165) | public outerBoundingBox(): {top: number, bottom: number, left: number, r...
method getPosition (line 179) | public getPosition(): Point {
method cropPosition (line 187) | public cropPosition(): void {
method setPosition (line 201) | public setPosition(position: Point): void {
FILE: src/ui/shapes/layer.ts
type ILayerJson (line 13) | interface ILayerJson {
method getNextID (line 27) | public static getNextID(): number {
method constructor (line 61) | constructor(block: Shape[], defaultLocation: Point) {
method moveAction (line 88) | public moveAction(): void {
method raise (line 98) | public raise(): void {
method select (line 105) | public select(): void {
method unselect (line 124) | public unselect(): void {
method addChild (line 139) | public addChild(child: Layer): void {
method addParent (line 155) | public addParent(parent: Layer): void {
method delete (line 159) | public delete(): void {
method toJson (line 164) | public toJson(): ILayerJson {
method getJSONParams (line 176) | public getJSONParams(): { [key: string]: any } {
method getParams (line 197) | public getParams(): { [key: string]: any; } {
method setParams (line 218) | public setParams(params: Map<string, any>): void {
method addParentLayerBetween (line 241) | public addParentLayerBetween(layer: Layer, parent: Layer): void {
method addParentLayer (line 256) | public addParentLayer(layer: Layer): void {
method addChildLayerBetween (line 273) | public addChildLayerBetween(newChild: Layer): void {
method getTfjsLayer (line 283) | public getTfjsLayer(): tf.SymbolicTensor {
method generateTfjsLayer (line 287) | public generateTfjsLayer(): void {
method layerShape (line 302) | public layerShape(): number[] {
method initLineOfJulia (line 315) | public initLineOfJulia(): string {
method lineOfJulia (line 319) | public lineOfJulia(): string {
method hasParentType (line 327) | public hasParentType(type: any ): boolean {
method focusing (line 339) | protected focusing(): void {
method toggleFocus (line 346) | private toggleFocus(textField: any): void {
FILE: src/ui/shapes/layers/add.ts
class Add (line 6) | class Add extends ActivationLayer {
method constructor (line 11) | constructor(defaultLocation: Point = Point.randomPoint(100, 40, Activa...
method populateParamBox (line 19) | public populateParamBox(): void { return; }
method getHoverText (line 21) | public getHoverText(): string { return "Add"; }
method lineOfPython (line 23) | public lineOfPython(): string {
method initLineOfJulia (line 27) | public initLineOfJulia(): string {
method generateTfjsLayer (line 32) | public generateTfjsLayer(): void {
method clone (line 41) | public clone(): Add {
FILE: src/ui/shapes/layers/batchnorm.ts
class BatchNorm (line 8) | class BatchNorm extends ActivationLayer {
method constructor (line 15) | constructor(defaultLocation: Point = Point.randomPoint(100, 40, Activa...
method populateParamBox (line 20) | public populateParamBox(): void {
method getHoverText (line 41) | public getHoverText(): string { return "BatchNorm"; }
method lineOfPython (line 43) | public lineOfPython(): string {
method initLineOfJulia (line 48) | public initLineOfJulia(): string {
method clone (line 57) | public clone(): BatchNorm {
method generateTfjsLayer (line 66) | public generateTfjsLayer(): void {
FILE: src/ui/shapes/layers/concatenate.ts
class Concatenate (line 6) | class Concatenate extends Layer {
method constructor (line 11) | constructor(defaultLocation: Point = Point.randomPoint(100, 40, Activa...
method populateParamBox (line 17) | public populateParamBox(): void {return; }
method getHoverText (line 19) | public getHoverText(): string { return "Concatenate"; }
method lineOfPython (line 21) | public lineOfPython(): string {
method initLineOfJulia (line 25) | public initLineOfJulia(): string {
method generateTfjsLayer (line 29) | public generateTfjsLayer(): void {
method clone (line 38) | public clone(): Concatenate {
FILE: src/ui/shapes/layers/convolutional.ts
class Conv2D (line 5) | class Conv2D extends ActivationLayer {
method constructor (line 18) | constructor(defaultLocation: Point = Point.randomPoint(100, 40, Activa...
method populateParamBox (line 25) | public populateParamBox(): void {
method getHoverText (line 127) | public getHoverText(): string { return "Conv"; }
method lineOfPython (line 129) | public lineOfPython(): string {
method initLineOfJulia (line 137) | public initLineOfJulia(): string {
method clone (line 145) | public clone(): Conv2D {
method getParams (line 154) | public getParams(): { [key: string]: any } {
FILE: src/ui/shapes/layers/dense.ts
class Dense (line 5) | class Dense extends ActivationLayer {
method constructor (line 10) | constructor(defaultLocation: Point = Point.randomPoint(100, 40, Activa...
method populateParamBox (line 14) | public populateParamBox(): void {
method getHoverText (line 30) | public getHoverText(): string { return "Dense"; }
method lineOfPython (line 32) | public lineOfPython(): string {
method initLineOfJulia (line 39) | public initLineOfJulia(): string {
method clone (line 46) | public clone(): Dense {
FILE: src/ui/shapes/layers/dropout.ts
class Dropout (line 6) | class Dropout extends Layer {
method constructor (line 11) | constructor(defaultLocation: Point = Point.randomPoint(100, 40, Activa...
method populateParamBox (line 19) | public populateParamBox(): void {
method getHoverText (line 36) | public getHoverText(): string { return "Dropout"; }
method lineOfPython (line 38) | public lineOfPython(): string {
method initLineOfJulia (line 44) | public initLineOfJulia(): string {
method clone (line 49) | public clone(): Dropout {
FILE: src/ui/shapes/layers/flatten.ts
class Flatten (line 6) | class Flatten extends Layer {
method constructor (line 11) | constructor(defaultLocation: Point = Point.randomPoint(100, 40, Activa...
method populateParamBox (line 15) | public populateParamBox(): void {return; }
method getHoverText (line 17) | public getHoverText(): string { return "Flatten"; }
method lineOfPython (line 19) | public lineOfPython(): string {
method initLineOfJulia (line 23) | public initLineOfJulia(): string {
method clone (line 27) | public clone(): Flatten {
FILE: src/ui/shapes/layers/input.ts
class Input (line 7) | class Input extends Layer {
method constructor (line 15) | constructor() {
method getHoverText (line 21) | public getHoverText(): string { return "Input"; }
method delete (line 23) | public delete(): void { this.unselect(); }
method populateParamBox (line 25) | public populateParamBox(): void {
method generateTfjsLayer (line 60) | public generateTfjsLayer(): void {
method lineOfPython (line 68) | public lineOfPython(): string {
method initLineOfJulia (line 73) | public initLineOfJulia(): string {
method clone (line 77) | public clone(): Input {
FILE: src/ui/shapes/layers/maxpooling.ts
class MaxPooling2D (line 6) | class MaxPooling2D extends Layer {
method constructor (line 12) | constructor(defaultLocation: Point = Point.randomPoint(100, 40, Activa...
method populateParamBox (line 19) | public populateParamBox(): void {
method getHoverText (line 49) | public getHoverText(): string { return "Maxpool"; }
method lineOfPython (line 51) | public lineOfPython(): string {
method initLineOfJulia (line 56) | public initLineOfJulia(): string {
method clone (line 61) | public clone(): MaxPooling2D {
FILE: src/ui/shapes/layers/output.ts
class Output (line 8) | class Output extends ActivationLayer {
method constructor (line 19) | constructor() {
method getHoverText (line 26) | public getHoverText(): string { return "Output"; }
method delete (line 28) | public delete(): void { this.unselect(); }
method populateParamBox (line 30) | public populateParamBox(): void {return; }
method lineOfPython (line 32) | public lineOfPython(): string {
method initLineOfJulia (line 36) | public initLineOfJulia(): string {
method lineOfJulia (line 45) | public lineOfJulia(): string {
method clone (line 50) | public clone(): Output {
method addChild (line 56) | public addChild(_: Layer): void {
FILE: src/ui/shapes/shape.ts
method constructor (line 8) | constructor(location: Point, color: string) {
class PathShape (line 17) | class PathShape extends Shape {
method constructor (line 20) | constructor(path: string, color: string) {
method svgAppender (line 25) | public svgAppender(selection: d3.Selection<SVGGraphicsElement, {}, HTM...
class Rectangle (line 36) | class Rectangle extends Shape {
method constructor (line 40) | constructor(location: Point, width: number, height: number, color: str...
method svgAppender (line 46) | public svgAppender(selection: d3.Selection<SVGGraphicsElement, {}, HTM...
class Circle (line 58) | class Circle extends Shape {
method constructor (line 62) | constructor(location: Point, radius: number, color: string, outerShape...
method svgAppender (line 68) | public svgAppender(selection: d3.Selection<SVGGraphicsElement, {}, HTM...
class Line (line 82) | class Line extends Shape {
method constructor (line 86) | constructor(location: Point, endPoint: Point, lineWidth: number, color...
method svgAppender (line 92) | public svgAppender(selection: d3.Selection<SVGGraphicsElement, {}, HTM...
class Point (line 104) | class Point {
method randomPoint (line 106) | public static randomPoint(width: number, height: number, initial: Poin...
method constructor (line 112) | constructor(x: number, y: number) {
method distance (line 117) | public distance(other: Point): number {
method add (line 121) | public add(other: Point): Point {
method minus (line 125) | public minus(other: Point): Point {
method angleTo (line 129) | public angleTo(other: Point): number {
method midpoint (line 133) | public midpoint(other: Point): Point {
FILE: src/ui/shapes/textbox.ts
class TextBox (line 4) | class TextBox {
method constructor (line 9) | constructor() {
method show (line 30) | public show(): void {
method hide (line 35) | public hide(): void {
method raise (line 39) | public raise(): void {
method setText (line 43) | public setText(text: string): void {
method setOffset (line 48) | public setOffset(offset: Point): void {
method setPosition (line 52) | public setPosition(position: Point): void {
FILE: src/ui/shapes/wire.ts
class Wire (line 5) | class Wire {
method constructor (line 14) | constructor(source: Layer, dest: Layer) {
method raise (line 45) | public raise(): void {
method raiseGroup (line 51) | public raiseGroup(): void {
method select (line 55) | public select(): void {
method unselect (line 68) | public unselect(): void {
method delete (line 73) | public delete(): void {
method updatePosition (line 82) | public updatePosition(): void {
FILE: src/ui/shapes/wireguide.ts
class WireGuide (line 5) | class WireGuide {
method constructor (line 9) | constructor() {
method show (line 31) | public show(): void {
method hide (line 38) | public hide(): void {
method raise (line 43) | public raise(): void {
method moveToMouse (line 48) | public moveToMouse(): void {
FILE: src/ui/utils.ts
function copyTextToClipboard (line 1) | function copyTextToClipboard(text: string): void {
function parseString (line 54) | function parseString(s: string): number | number[] {
function getSvgOriginalBoundingBox (line 62) | function getSvgOriginalBoundingBox(svg: SVGSVGElement): {width: number, ...
FILE: src/ui/window.ts
type Mode (line 7) | enum Mode {
class WindowProperties (line 12) | class WindowProperties {
method constructor (line 28) | private constructor() {}
method Instance (line 30) | public static get Instance(): WindowProperties {
Condensed preview — 45 files, each showing path, character count, and a content snippet. Download the .json file or copy for the full structured content (523K chars).
[
{
"path": ".gitignore",
"chars": 104,
"preview": "__pycache__\n*.pyc\nsrc/ui/style.css\nbuilt/*\nnode_modules/\n*.log\ndist/*\nsrc/model/test.py\nprod/*\n.vscode/\n"
},
{
"path": "LICENSE.txt",
"chars": 1126,
"preview": "Copyright 2020 Jesse Michel, Stefan Grosser, Zachary Holbrook, and Rikhav Shah\r\n\r\nPermission is hereby granted, free of "
},
{
"path": "README.md",
"chars": 1242,
"preview": "# ENNUI\r\nENNUI is an Elegant Neural Network User Interface that allows users to:\r\n- Build neural network architectures w"
},
{
"path": "build_prod.sh",
"chars": 527,
"preview": "# Build\nnpm run build\n\n# Create the folder if it doesn't exist\nfolder=\"prod\"\nif [ ! -d \"$folder\" ]; then\n mkdir $folder"
},
{
"path": "index.html",
"chars": 38562,
"preview": "<meta charset=\"utf-8\"/>\n<html>\n<head>\n\t<meta charset=\"utf-8\">\n\t<meta name=\"description\" content=\"Welcome to ENNUI - An e"
},
{
"path": "npm-shrinkwrap.json",
"chars": 266881,
"preview": "{\r\n \"name\": \"ennui\",\r\n \"version\": \"0.1.0\",\r\n \"lockfileVersion\": 1,\r\n \"requires\": true,\r\n \"dependencies\": {\r\n \"@b"
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{
"path": "package.json",
"chars": 1464,
"preview": "{\n \"name\": \"ennui\",\n \"version\": \"0.1.0\",\n \"description\": \"Neural nework visualizer.\",\n \"main\": \"app.ts\",\n \"dependen"
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{
"path": "robots.txt",
"chars": 25,
"preview": "User-agent: *\nDisallow: \n"
},
{
"path": "src/model/build_network.ts",
"chars": 4906,
"preview": "import * as tf from \"@tensorflow/tfjs\";\r\n\r\nimport { displayError } from \"../ui/error\";\r\nimport { Layer } from \"../ui/sha"
},
{
"path": "src/model/code_generation.ts",
"chars": 2011,
"preview": "import { displayError } from \"../ui/error\";\nimport { ActivationLayer } from \"../ui/shapes/activationlayer\";\nimport { Lay"
},
{
"path": "src/model/data.ts",
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"preview": "// Adapted from https://github.com/tensorflow/tfjs-examples/blob/master/mnist/data.js\r\n\r\nimport * as tf from \"@tensorflo"
},
{
"path": "src/model/export_model.ts",
"chars": 9695,
"preview": "import { saveAs } from \"file-saver\";\r\nimport { IDraggableData } from \"../ui/app\";\r\nimport { displayError } from \"../ui/e"
},
{
"path": "src/model/graphs.ts",
"chars": 8660,
"preview": "import * as tf from \"@tensorflow/tfjs\";\nimport * as tfvis from \"@tensorflow/tfjs-vis\";\nimport { tabSelected } from \"../u"
},
{
"path": "src/model/julia_skeleton.ts",
"chars": 3399,
"preview": "import { setModelHyperparameters } from \"../ui/app\";\nimport { model } from \"./params_object\";\n\nexport function juliaSkel"
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{
"path": "src/model/mnist_model.ts",
"chars": 4410,
"preview": "// Adapted from https://github.com/tensorflow/tfjs-examples/tree/master/mnist\n\nimport * as tf from \"@tensorflow/tfjs\";\ni"
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"chars": 3031,
"preview": "import * as tf from \"@tensorflow/tfjs\";\nimport { displayError } from \"../ui/error\";\n\n/** All the hyperparameters for the"
},
{
"path": "src/model/python_skeleton.ts",
"chars": 2392,
"preview": "import { setModelHyperparameters } from \"../ui/app\";\r\nimport { dataset } from \"./data\";\r\nimport { model } from \"./params"
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{
"path": "src/model/save_state_url.ts",
"chars": 1881,
"preview": "import { IDraggableData } from \"../ui/app\";\nimport { defaultTemplate, resetWorkspace } from \"../ui/model_templates\";\nimp"
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{
"path": "src/ui/app.ts",
"chars": 16133,
"preview": "import * as d3 from \"d3\";\nimport { buildNetworkDAG, topologicalSort } from \"../model/build_network\";\nimport { generateJu"
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"path": "src/ui/error.ts",
"chars": 362,
"preview": "\nexport function displayError(error: Error): void {\n document.getElementById(\"error\").style.display = null;\n docum"
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{
"path": "src/ui/model_templates.ts",
"chars": 8717,
"preview": "import { IDraggableData } from \"./app\";\nimport { Activation, Relu } from \"./shapes/activation\";\nimport { ActivationLayer"
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{
"path": "src/ui/shapes/activation.ts",
"chars": 4974,
"preview": "import { windowProperties } from \"../window\";\nimport { ActivationLayer } from \"./activationlayer\";\nimport { Draggable } "
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"path": "src/ui/shapes/activationlayer.ts",
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"preview": "import { displayError } from \"../error\";\nimport { windowProperties } from \"../window\";\nimport { Activation } from \"./act"
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{
"path": "src/ui/shapes/draggable.ts",
"chars": 9206,
"preview": "import * as d3 from \"d3\";\r\nimport { getSvgOriginalBoundingBox } from \"../utils\";\r\nimport { windowProperties } from \"../w"
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"preview": "import * as tf from \"@tensorflow/tfjs\";\r\nimport * as d3 from \"d3\";\r\nimport { generateTfjsModel, topologicalSort } from \""
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"path": "src/ui/shapes/layers/add.ts",
"chars": 1632,
"preview": "import * as tf from \"@tensorflow/tfjs\";\r\nimport { displayError } from \"../../error\";\r\nimport { ActivationLayer } from \"."
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"chars": 3175,
"preview": "import * as tf from \"@tensorflow/tfjs\";\nimport { SymbolicTensor } from \"@tensorflow/tfjs\";\nimport { displayError } from "
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"preview": "import * as tf from \"@tensorflow/tfjs\";\nimport { ActivationLayer } from \"../activationlayer\";\nimport { Layer } from \"../"
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"preview": "import * as tf from \"@tensorflow/tfjs\";\nimport { ActivationLayer } from \"../activationlayer\";\nimport { PathShape, Point,"
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"preview": "import * as tf from \"@tensorflow/tfjs\";\nimport { ActivationLayer } from \"../activationlayer\";\nimport { PathShape, Point "
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"path": "src/ui/shapes/layers/maxpooling.ts",
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"preview": "import * as tf from \"@tensorflow/tfjs\";\nimport { ActivationLayer } from \"../activationlayer\";\nimport { Layer } from \"../"
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"preview": "import * as d3 from \"d3\";\r\nimport { Point } from \"./shape\";\r\n\r\nexport class TextBox {\r\n private group: d3.Selection<S"
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{
"path": "src/ui/shapes/wireguide.ts",
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"preview": "export function copyTextToClipboard(text: string): void {\n // https://stackoverflow.com/questions/400212/how-do-i-cop"
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"preview": "module.exports = {\r\n entry: \"./built/ui/app.js\",\r\n output: {\r\n filename: \"bundle.js\"\r\n },\r\n target: 'we"
}
]
About this extraction
This page contains the full source code of the martinjm97/ENNUI GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 45 files (463.4 KB), approximately 157.1k tokens, and a symbol index with 282 extracted functions, classes, methods, constants, and types. Use this with OpenClaw, Claude, ChatGPT, Cursor, Windsurf, or any other AI tool that accepts text input. You can copy the full output to your clipboard or download it as a .txt file.
Extracted by GitExtract — free GitHub repo to text converter for AI. Built by Nikandr Surkov.