Update readme and travis

.travis.yml

@@ -1,6 +1,7 @@
+language: node_js
+node_js:
+  - lts/*
+install:
+  - npm install
 script:
-- bash ./src/utils/deploy-to-gh-pages.sh
-env:
-  global:
-  - GITHUB_REPO: poloclub/cnn-explainer
-  - secure: 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
+  - npm run build

README.md

@@ -1,13 +1,14 @@
 # CNN Explainer
 
-CNN Explainer is an interactive visualization system designed for non-experts
-to more easily learn about convolutional neural networks (CNNs).
+An interactive visualization system designed to help non-experts learn about Convolutional Neural Networks (CNNs)
+
+[![Build Status](https://travis-ci.com/poloclub/cnn-explainer.svg?branch=master)](https://travis-ci.com/poloclub/cnn-explainer)
 
 <a href="https://youtu.be/udVN7fPvGe0" target="_blank"><img src="https://i.imgur.com/TIKlgt6.png" style="max-width:100%;"></a>
 
 ## Live Demo
 
-For a live demo, visit: http://poloclub.github.io/cnn-explainer/.
+For a live demo, visit: http://poloclub.github.io/cnn-explainer/
 
 ## Running Locally
 
@@ -32,7 +33,7 @@ Than run CNN Explainer:
 npm run dev
 ```
 
-Navigate to [localhost:5000](localhost:5000). You should CNN Explainer running in your broswer :)
+Navigate to [localhost:5000](https://localhost:5000). You should CNN Explainer running in your broswer :)
 
 To see how we trained the CNN, visit the directory [`./tiny-vgg/`](tiny-vgg).
 
@@ -56,41 +57,13 @@ We thank
 [Kantwon Rogers](https://www.kantwon.com), and the
 [Georgia Tech Visualization Lab](http://vis.gatech.edu)
 for their support and constructive feedback.
+
+## License
 
-For more information, check out our [research manuscript](https://arxiv.org/abs/2001.02004):
-
-[Z. J. Wang](https://zijie.wang/),
-[R. Turko](https://www.linkedin.com/in/robert-turko/),
-[O. Shaikh](http://oshaikh.com/),
-[H. Park](https://haekyu.com/),
-[N. Das](http://nilakshdas.com/),
-[F. Hohman](https://fredhohman.com/),
-[M. Kahng](http://minsuk.com),
-and [D. H. Chau](https://www.cc.gatech.edu/~dchau/).
-CNN Explainer: Learning Convolutional Neural Networks with Interactive Visualization.
-<i>arXiv:2001.02004 [cs],</i>
-Apr. 2020.
-
-
-## Citation
-```
-@article{wangCNNExplainerLearning2020,
-  title = {{{CNN Explainer}}: {{Learning Convolutional Neural Networks}} with {{Interactive Visualization}}},
-  shorttitle = {{{CNN Explainer}}},
-  author = {Wang, Zijie J. and Turko, Robert and Shaikh, Omar and Park, Haekyu and Das, Nilaksh and Hohman, Fred and Kahng, Minsuk and Chau, Duen Horng},
-  year = {2020},
-  month = apr,
-  archivePrefix = {arXiv},
-  eprint = {2001.02004},
-  eprinttype = {arxiv},
-  journal = {arXiv:2001.02004 [cs]},
-  keywords = {Computer Science - Artificial Intelligence,Computer Science - Human-Computer Interaction,Computer Science - Machine Learning},
-  primaryClass = {cs}
-}
-```
+The software is available under the [MIT License](https://github.com/poloclub/cnn-explainer/blob/master/LICENSE).
 
 ## Contact
 
-If you have any questions, feel free to [open an issue](https://github.com/poloclub/cnn-explainer/issues/new/choose) or contact [Jay Wang](www.zijie.wang).
+If you have any questions, feel free to [open an issue](https://github.com/poloclub/cnn-explainer/issues/new/choose) or contact [Jay Wang](https://www.zijie.wang).
 
 

tiny-vgg/README.md

@@ -1,8 +1,8 @@
 # Train a Tiny VGG
 
 This directory includes code and data to train a Tiny VGG model
-(inspired by the demo CNN in [Stanford CS231n class](http://cs231n.stanford.edu)
-on 10 every-day classes from [Tiny ImageNet](https://tiny-imagenet.herokuapp.com).
+(inspired by the demo CNN in [Stanford CS231n class](http://cs231n.stanford.edu))
+on 10 every-day classes from the [Tiny ImageNet](https://tiny-imagenet.herokuapp.com).
 
 ## Installation
 
@@ -69,17 +69,17 @@ python tiny-vgg.py
 After training, you will get two trained models in Keras format `trained_tiny_vgg.h5`
 and `trained_vgg_best.h5`. The first file is the final model after training, and
 `trained_vgg_best.h5` is the model having the best validation performance.
-You can use either one to power CNN Explainer.
+You can use either one for CNN Explainer.
 
 ## Convert Model Format
 
-Before loading the model using tensorflow.js, you want to convert the model format
+Before loading the model using *tensorflow.js*, you want to convert the model format
 from Keras `h5` format to [tensorflow.js format](https://www.tensorflow.org/js/tutorials/conversion/import_keras).
 
 ```
 ensorflowjs_converter --input_format keras trained_vgg_best.h5 ./
 ```
 
-Then you can put the output file `group1-shard1of1.bin` in `/public/data` and uses
-tensorflow.js to run the trained model.
+Then you can put the output file `group1-shard1of1.bin` in `/public/data` and use
+*tensorflow.js* to load the trained model.