When inputting the following parameters, both the neurons and the visual output of tensorflow breaks entirely, giving NaN. Tested across the latest Firefox 47, Chrome 49 and Opera 37 browsers.

http://playground.tensorflow.org/#activation=tanh&batchSize=1&dataset=spiral&regDataset=reg-plane&learningRate=1&regularizationRate=0&noise=50&networkShape=8&seed=0.42182&showTestData=false&discretize=false&percTrainData=50&x=true&y=true&xTimesY=false&xSquared=false&ySquared=false&cosX=false&sinX=false&cosY=false&sinY=false&collectStats=false&problem=regression

As I was investigating different activation methods, I found myself wanting to make more concrete comparisons between the rate at which different configurations improved the accuracy of their outputs.

The ability to hover over the line of the output loss and see the exact iteration number and respective rates would be interesting at the very least, and potentially helpful for observing differences between configurations.

After entering the command 'npm install' in the directory with playground files I got following message:

npm WARN optional Skipping failed optional dependency /chokidar/fsevents:
npm WARN notsup Not compatible with your operating system or architecture: [email protected]

Here is my environment:

uname -a

Linux minsky2 4.4.0-97-generic #120-Ubuntu SMP Tue Sep 19 17:26:02 UTC 2017 ppc64le ppc64le ppc64le GNU/Linux

lscpu

Architecture:          ppc64le
Byte Order:            Little Endian
CPU(s):                160
On-line CPU(s) list:   0-159
Thread(s) per core:    8
Core(s) per socket:    10
Socket(s):             2
NUMA node(s):          2
Model:                 1.0 (pvr 004c 0100)
Model name:            POWER8NVL (raw), altivec supported
CPU max MHz:           4023.0000
CPU min MHz:           2061.0000
Hypervisor vendor:     horizontal
Virtualization type:   full
L1d cache:             64K
L1i cache:             32K
L2 cache:              512K
L3 cache:              8192K
NUMA node0 CPU(s):     0-79
NUMA node1 CPU(s):     80-159

cat /usr/local/playground/npm-debug.log

0 info it worked if it ends with ok
1 verbose cli [ '/usr/bin/nodejs', '/usr/bin/npm', 'run', 'build' ]
2 info using [email protected]
3 info using [email protected]
4 verbose run-script [ 'prebuild', 'build', 'postbuild' ]
5 info lifecycle [email protected]~prebuild: [email protected]
6 silly lifecycle [email protected]~prebuild: no script for prebuild, continuing
7 info lifecycle [email protected]~build: [email protected]
8 verbose lifecycle [email protected]~build: unsafe-perm in lifecycle true
9 verbose lifecycle [email protected]~build: PATH: /usr/share/npm/bin/node-gyp-bin:/usr/local/playground/node_modules/.bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/snap/bin
10 verbose lifecycle [email protected]~build: CWD: /usr/local/playground
11 silly lifecycle [email protected]~build: Args: [ '-c',
11 silly lifecycle   'npm run prep && npm run build-js && npm run build-css && npm run build-html' ]
12 silly lifecycle [email protected]~build: Returned: code: 1  signal: null
13 info lifecycle [email protected]~build: Failed to exec build script
14 verbose stack Error: [email protected] build: `npm run prep && npm run build-js && npm run build-css && npm run build-html`
14 verbose stack Exit status 1
14 verbose stack     at EventEmitter.<anonymous> (/usr/share/npm/lib/utils/lifecycle.js:232:16)
14 verbose stack     at emitTwo (events.js:87:13)
14 verbose stack     at EventEmitter.emit (events.js:172:7)
14 verbose stack     at ChildProcess.<anonymous> (/usr/share/npm/lib/utils/spawn.js:24:14)
14 verbose stack     at emitTwo (events.js:87:13)
14 verbose stack     at ChildProcess.emit (events.js:172:7)
14 verbose stack     at maybeClose (internal/child_process.js:821:16)
14 verbose stack     at Process.ChildProcess._handle.onexit (internal/child_process.js:211:5)
15 verbose pkgid [email protected]
16 verbose cwd /usr/local/playground
17 error Linux 4.4.0-97-generic
18 error argv "/usr/bin/nodejs" "/usr/bin/npm" "run" "build"
19 error node v4.2.6
20 error npm  v3.5.2
21 error code ELIFECYCLE
22 error [email protected] build: `npm run prep && npm run build-js && npm run build-css && npm run build-html`
22 error Exit status 1
23 error Failed at the [email protected] build script 'npm run prep && npm run build-js && npm run build-css && npm run build-html'.
23 error Make sure you have the latest version of node.js and npm installed.
23 error If you do, this is most likely a problem with the deep-playground-prototype package,
23 error not with npm itself.
23 error Tell the author that this fails on your system:
23 error     npm run prep && npm run build-js && npm run build-css && npm run build-html
23 error You can get information on how to open an issue for this project with:
23 error     npm bugs deep-playground-prototype
23 error Or if that isn't available, you can get their info via:
23 error     npm owner ls deep-playground-prototype
23 error There is likely additional logging output above.
24 verbose exit [ 1, true ]

I have the following problem: if I want to change the weight of an edge, then I have to delete the weight written inside it. This is not a problem at first, but I wanted to try to manually change EVERY edge weight, so I can test what does it do.

You could make changing the weights of the edges easier by some methods:

1. When you click an edge, the program should select the text of the input. After that with one keypress, you can either modify the weight or rewrite it.
2. If the mouse exits the edge, and it goes to the hoverCard, then it shouldn't hide the hoverCard.
3. the input's text is removed when clicking on the edge. This is inferior to point 1, but I don't know how to do point 1.

I just followed the instruction in ReadMe. my environment is ubuntu16.04. But I got the error below:
`
typings install && copyfiles analytics.js dist && catw node_modules/material-design-lite/material.min.js node_modules/d3/d3.min.js node_modules/seedrandom/seedrandom.min.js > dist/lib.js

sh: 1: typings: not found
npm ERR! weird error 127
npm WARN This failure might be due to the use of legacy binary "node"
npm WARN For further explanations, please read
/usr/share/doc/nodejs/README.Debian

npm ERR! not ok code 0
npm ERR! weird error 1
npm WARN This failure might be due to the use of legacy binary "node"
npm WARN For further explanations, please read
/usr/share/doc/nodejs/README.Debian

npm ERR! not ok code 0
`

I don't know what's wrong. I have done the commands are below:

sudo apt-get install nodejs sudo apt-get install npm sudo ln -s /usr/bin/nodejs /usr/bin/node sudo apt-get install nodejs-legacy

please help me!

Fantastic visualization, thank you!

What are you trying to predict (i.e., what is the target)?
What are the inputs?
Is it possible to see the dataset before starting the neural network?

or is the dataset random data?

sync values to a sheet and update weights / values as the user changes the gui in realtime.

something akin to this
https://docs.google.com/spreadsheets/d/18bkheoJbmMUqdRFrviUy_TiooSjvvpDqiti7hm2EASY/edit#gid=2064454629

it may include written math formula pngs to gain visibility of what's happening under the hood.

related
https://blog.algorithmia.com/google-sheets-algorithmia-machine-learning-spreadsheets/

In the main README.md there is a link to the non-existent file CONTRIBUTIONS via the sentence:

If you'd like to contribute, be sure to review the contribution guidelines.

The link is broken. It should link to CONTRIBUTIONS.md.

Als-MacBook-Pro:playground-master almeier$ npm install
npm WARN deprecated [email protected]: Please update to minimatch 3.0.2 or higher to avoid a RegExp DoS issue
npm WARN deprecated [email protected]: use uuid module instead
npm WARN prefer global [email protected] should be installed with -g

[email protected] install /Users/almeier/Downloads/playground-master/node_modules/fsevents
node install

[fsevents] Success: "/Users/almeier/Downloads/playground-master/node_modules/fsevents/lib/binding/Release/node-v51-darwin-x64/fse.node" is installed via remote
[email protected] /Users/almeier/Downloads/playground-master
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Als-MacBook-Pro:playground-master almeier$ npm run serve

[email protected] serve /Users/almeier/Downloads/playground-master
http-server dist/

Starting up http-server, serving dist/
Available on:
http://127.0.0.1:8080
http://192.168.117.15:8080
Hit CTRL-C to stop the server
[Thu Mar 09 2017 09:18:20 GMT-0500 (EST)] "GET /" "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_3) AppleWebKit/602.4.8 (KHTML, like Gecko) Version/10.0.3 Safari/602.4.8"
[Thu Mar 09 2017 09:18:20 GMT-0500 (EST)] "GET /" Error (404): "Not found"
[Thu Mar 09 2017 09:18:20 GMT-0500 (EST)] "GET /favicon.ico" "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_3) AppleWebKit/602.4.8 (KHTML, like Gecko) Version/10.0.3 Safari/602.4.8"
[Thu Mar 09 2017 09:18:20 GMT-0500 (EST)] "GET /favicon.ico" Error (404): "Not found"
[Thu Mar 09 2017 09:18:35 GMT-0500 (EST)] "GET /" "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_3) AppleWebKit/602.4.8 (KHTML, like Gecko) Version/10.0.3 Safari/602.4.8"
[Thu Mar 09 2017 09:18:35 GMT-0500 (EST)] "GET /" Error (404): "Not found"
[Thu Mar 09 2017 09:19:49 GMT-0500 (EST)] "GET /" "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_3) AppleWebKit/602.4.8 (KHTML, like Gecko) Version/10.0.3 Safari/602.4.8"
[Thu Mar 09 2017 09:19:49 GMT-0500 (EST)] "GET /" Error (404): "Not found"
^Chttp-server stopped.

Environment:
MAC OS X 10.12.3
$ java -version
java version "1.6.0_65"
Java(TM) SE Runtime Environment (build 1.6.0_65-b14-468-11M4833)
Java HotSpot(TM) 64-Bit Server VM (build 20.65-b04-468, mixed mode)
Safari Version 10.0.3

First, Amazing work guys. I don't know how to convey my gratitude in words!

(And I feel bad for raising the first "issue" 😛 )

This is a feature request - There are various weight initialization techniques out there. I would like to know the feasibility of implementing visualization for those so that we can have get a feedback on the dependence on weight initialization for different network depths etc.

Hi,
for the (incredible) TensorFlow Playground JavaScript version http://playground.tensorflow.org/
it would be useful to include a stopping condition whenever 0.000(1) training and 0.000(1) test logloss are reached. Otherwise it will just eat all the local electrons for 10 cents per KWh [eia.gov]. Assuming 1000 users playing around each hour, mother earth will sweat heat up a microCelsius. Similar to the famous NetFlix question "Are you still watching?" one could just stop or ask, "Are you still tinkering?".

I am not making this up, its actually a Google Pledge called: Google Green. Yes I am also guilty of wasting a lot of electrons :-)

Also for the classification a confusion matrix and accuracy and errors would be useful, for regression an R^2 and RMSE or MSE instead of logloss. Once the logloss is static, one could do a timed estimate how long the parameterized solution will take, based on the local JavaScript GFLOPS estimation, hence for certain settings it would tell me: 0.0001 logloss or accuracy will be reached in 200 hours (oooops 24 bucks charged locally).

Cheers
Tobias

Looking at the code, the neural net part seems to be general to any arrangement of neurons. I think it's mainly a GUI issue to let the user draw arbitrary neurons and connections? This would be like a 'advanced' UI which you might play with after the current UI, although maybe it might not be that useful for learning purposes.

I think hitting the rewind button should keep the same weight initialization, this way a user can rewind and see the exact same process (maybe step by step) and also play with different parameters and see which ones make the net converge faster (while keeping the same weight initialization).

Maybe there should be a separate button to change the weight initialization or seed.
What do you guys think? This should be pretty easy to implement

Hello

As I understand the current playground is for visualizing convolutional neural nets.

Any idea how it can be extended to include recurrent neural nets as well. I would like to attempt this and appreciate if the authors can give a bit of direction on how it can be implemented.

Thanks.

By looking carefully I noticed that dataset thumbnail images flipped vertically.
In my opinion, the line #992 in src/playground.ts needs to be changed as follows:

context.fillRect(w * (d.x + 6) / 12, h - (h * (d.y + 6) / 12), 4, 4);

If add x1/x2 with data preprocess ,may be we can use less node to solve screw graph.
Use exponential to deal divisor is zero problem.

Wonderful !

Would it be possible to clarify the output neuron. I can imagine it's activation function is a sigmoid. What kind of loss is employed ? Would it be possible to propose different loss functions ?

It would be helpful to customize a model via GUI, and export it as TensorFlow code.

As some one who is new to ML is there any documentation explaining each option?

I actually could break it. You promised!! :(

• Freedom of creating own input data
• More standard data templates
• How formula is applied in the background, which learning rule is being used

What should I do?

Starting up http-server, serving dist/
Available on:
  http://127.0.0.1:8080
  http://192.168.1.30:8080
  http://192.168.1.11:8080
Hit CTRL-C to stop the server
[Thu Feb 16 2017 16:57:55 GMT-0600 (CST)] "GET /" "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_3) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/56.0.2924.87 Safari/537.36"
[Thu Feb 16 2017 16:57:55 GMT-0600 (CST)] "GET /" Error (404): "Not found"

This is the output of the log file when I go to the following address on my website:
http://192.168.1.30:8080

The derivative for the RELU activation function in nn.ts would seem to be incorrect.

It is defined as:
der: x => x < 0 ? 0 : 1

when it should be
der: x => x <= 0 ? 0 : 1

https://github.com/roboball/selu/blob/master/selu.py

I thought add these features are needed for all types of neuron networks, so it would be nice to add them.

In addition, maybe we could switch between feedforward and backpropagation mode?

As i was playing with the tool, i tried to roughly calculate the data points to make complete sense of what was going on. I couldnt understand why my calculations didn't add up, until i realised the bias (Visualized as a small, hard to notice, square next to the nodes) manipulates the value too.

Making it more obvious and explaining what the bias does, would help with the educational aspect.

Using default settings except:
Problem type = regression
Zero hidden layers
3 features

When training starts, the third feature will quickly move to a very tiny value.
Shortly after, the training stops:

this is the error log:

C:\Users\Arash\Desktop\AUTUMN16\Computational Intelligence\TensorFlow-Playground>npm run build

[email protected] build C:\Users\Arash\Desktop\AUTUMN16\Computational Intelligence\TensorFlow-Playground
npm run prep && npm run build-js && npm run build-css && npm run build-html

[email protected] prep C:\Users\Arash\Desktop\AUTUMN16\Computational Intelligence\TensorFlow-Playground
typings install && mkdir -p dist && cat node_modules/material-design-lite/material.min.js node_modules/d3/d3.min.js node_modules/seedrandom/seedrandom.min.js > dist/lib.js

typings WARN deprecated 9/7/2016, 5:27:44 AM: "registry:dt/d3#0.0.0+20160315011939" has been deprecated
typings ERR! message Unable to read typings for "d3". You should check the entry paths in "d3.d.ts" are up to date
typings ERR! caused by Unable to connect to "https://raw.githubusercontent.com/DefinitelyTyped/DefinitelyTyped/f3b564844485e265757d0494f8802e42e23dbc9d/d3/d3.d.ts"
typings ERR! caused by connect ECONNREFUSED 10.10.34.36:443

typings ERR! cwd C:\Users\Arash\Desktop\AUTUMN16\Computational Intelligence\TensorFlow-Playground
typings ERR! system Windows_NT 10.0.10586
typings ERR! command "C:\Program Files\nodejs\node.exe" "C:\Users\Arash\Desktop\AUTUMN16\Computational Intelligence\TensorFlow-Playground\node_modules\typings\dist\bin.js" "install"
typings ERR! node -v v4.5.0
typings ERR! typings -v 0.7.12

typings ERR! If you need help, you may report this error at:
typings ERR! https://github.com/typings/typings/issues

npm ERR! Windows_NT 10.0.10586
npm ERR! argv "C:\Program Files\nodejs\node.exe" "C:\Users\Arash\AppData\Roaming\npm\node_modules\npm\bin\npm-cli.js" "run" "prep"
npm ERR! node v4.5.0
npm ERR! npm v3.10.8
npm ERR! code ELIFECYCLE
npm ERR! [email protected] prep: typings install && mkdir -p dist && cat node_modules/material-design-lite/material.min.js node_modules/d3/d3.min.js node_modules/seedrandom/seedrandom.min.js > dist/lib.js
npm ERR! Exit status 1
npm ERR!
npm ERR! Failed at the [email protected] prep script 'typings install && mkdir -p dist && cat node_modules/material-design-lite/material.min.js node_modules/d3/d3.min.js node_modules/seedrandom/seedrandom.min.js > dist/lib.js'.
npm ERR! Make sure you have the latest version of node.js and npm installed.
npm ERR! If you do, this is most likely a problem with the deep-playground-prototype package,
npm ERR! not with npm itself.
npm ERR! Tell the author that this fails on your system:
npm ERR! typings install && mkdir -p dist && cat node_modules/material-design-lite/material.min.js node_modules/d3/d3.min.js node_modules/seedrandom/seedrandom.min.js > dist/lib.js
npm ERR! You can get information on how to open an issue for this project with:
npm ERR! npm bugs deep-playground-prototype
npm ERR! Or if that isn't available, you can get their info via:
npm ERR! npm owner ls deep-playground-prototype
npm ERR! There is likely additional logging output above.

npm ERR! Please include the following file with any support request:
npm ERR! C:\Users\Arash\Desktop\AUTUMN16\Computational Intelligence\TensorFlow-Playground\npm-debug.log

npm ERR! Windows_NT 10.0.10586
npm ERR! argv "C:\Program Files\nodejs\node.exe" "C:\Users\Arash\AppData\Roaming\npm\node_modules\npm\bin\npm-cli.js" "run" "build"
npm ERR! node v4.5.0
npm ERR! npm v3.10.8
npm ERR! code ELIFECYCLE
npm ERR! [email protected] build: npm run prep && npm run build-js && npm run build-css && npm run build-html
npm ERR! Exit status 1
npm ERR!
npm ERR! Failed at the [email protected] build script 'npm run prep && npm run build-js && npm run build-css && npm run build-html'.
npm ERR! Make sure you have the latest version of node.js and npm installed.
npm ERR! If you do, this is most likely a problem with the deep-playground-prototype package,
npm ERR! not with npm itself.
npm ERR! Tell the author that this fails on your system:
npm ERR! npm run prep && npm run build-js && npm run build-css && npm run build-html
npm ERR! You can get information on how to open an issue for this project with:
npm ERR! npm bugs deep-playground-prototype
npm ERR! Or if that isn't available, you can get their info via:
npm ERR! npm owner ls deep-playground-prototype
npm ERR! There is likely additional logging output above.

npm ERR! Please include the following file with any support request:
npm ERR! C:\Users\Arash\Desktop\AUTUMN16\Computational Intelligence\TensorFlow-Playground\npm-debug.log

I'm beginner and appreciate any kind of help.

Thank you.

Hi there,

We noticed that your repo has a high severity vulnerability:

• Regular Expression Denial of Service
• Affected package: minimatch <=3.0.1

Here is the test report for this repo.
If you’d like to fix this vulnerability, Snyk lets you generate a pull request that recommends the best upgrade path - there’s a link to fix this vulnerability on the test report.

Stay secure :-)
Snyk Community

Hey guys,
there is now a companion demo for gradient boosting you can play with:
https://arogozhnikov.github.io/2016/07/05/gradient_boosting_playground.html

There are not as many setting as in this playground, but I think this is a nice visual explanation of how gradient boosting is trained.

Enjoy!

PS. I'll close this issue tomorrow.

It has been a great tool for showing what a NN is in a visualised way, so I wonder if it is possible that this playgroud will support more network architectures like CNN and RNN with more kinds of data like images and sequential data?

Hi, let me just say that it is a beautiful visualization of neural network. Also, it would be better if you could add more than two classes as outputs.

Issue with the above problem.

The ability to enter data sets, either by using a table or some other means (eg. interactive heat map), would be pretty sweet :)

Is it possible to get the training set for datasets that are used?

We wrote a tiny neural network library that meets the demands of this educational visualization. For real-world applications, consider the TensorFlow library.

at "tiny neural network library" there is a deadlink

The following image is what I currently see. I tried it with recent versions of Firefox, Edge and IE.

I found screenshots used in tutorials which show the exponents as expected:

To me this seems like a rendering/font problem.

Hi,
most NN and DN algorithms are optimized by a grid search or combinations over possible parameters. It would be useful to show at least the top 10 best parameters obtained by grid/random search. Maybe a small table of the most common parameters would be useful. Or additionally random or Bayesian search, simply give the oblivious user some open AI recommendations (I am thinking of the spiral).
Thank you,
Tobias

I downloaded the repository as a zip file, unpacked it under my web server's root directory (I'm using Apache), then opened the index page, and it seems I can't see images (for example, dataset thumbnails) and the CSS is wrong.
I'm attaching what I see from my browser.

I tried to lower the learning rate after several hundred iterations. But it seems like it'll clear all previous results and re-initialize all the weights.

Is it possible to adjust the learning rate online?
(I can imagine this requires some works on the URI.)

Thanks

Great project!

I encountered a hangup in Safari 9.1 on a MacBook Pro Retina 15" late 2013 (2.3 GHz i7, 16 GB RAM). I saw a person in HackerNews post a similar issue and you asked for details, so here they are:

Given the spiral data set, X1, X1, sin(X1), sin(X2), 8 neurons + 4 neurons (+ 2 neuron i think), noise 25 and then starting training went fine in the beginning. After running for perhaps 10-30 seconds Safari 9.1 became unresponsive and unable to stop the script. After a minute or so of trying to reload it asked to force reload the webpage which worked.

Hello, TensorFlow developers:

This is a feature request. I would like to see how different back propagation algorithms can affect training speed. (Gradient Descent? Rprop? BFGS? I am new to neural networks...)

It would be nice to lift some of the limits:

1. Max layers
2. Max neurons per layer
3. Smaller regularization rates
4. Bigger batch sizes
5. More noise

At least lift those limits that are merely constants in the source code and can be changed easily.

It would be nice to have the classic y = f(x) problem with a line plot, it's more obvious and easier to grasp for starters.

X₁² and X₂² look like X¹² and X²² in FireFox.

This is a feature request that would involve a large change.

Often, it would be helpful to compare results of a simulation with previous ones. What would be cool is if each simulation was saved (both its input parameters and the graphical result) and you could recall them to compare the results to your current run. What would be even cooler is if you could see run results side by side.