Which dataset do you want to use?

Cursor Mode


Which properties do you want to feed in?

Click to edit.
Weight/Bias is 0.2.
Error: 0.2.
This is the output from one neuron. Hover to see it larger.
The outputs are mixed with varying weights, shown by the thickness of the lines.


Test loss
Training loss
Colors shows data, neuron and weight values.

This Is Cool, Can I Repurpose It?

Please do! This app can be found at GitHub. The original source here was created with the hope that it can make neural networks a little more accessible and easier to learn. You’re free to use it in any way that follows the Apache License.

There are some controls below to enable you tailor the playground to a specific topic or lesson. Just choose which features you’d like to be visible below then save this link, or refresh the page.

What Do All the Colors Mean?

Orange and blue are used throughout the visualization in slightly different ways, but in general orange shows negative values while blue shows positive values.

The data points (represented by small circles) are initially colored orange or blue, which correspond to positive one and negative one.

In the hidden layers, the lines are colored by the weights of the connections between neurons. Blue shows a positive weight, which means the network is using that output of the neuron as given. An orange line shows that the network is assiging a negative weight.

In the output layer, the dots are colored orange or blue depending on their original values. The background color shows what the network is predicting for a particular area. The intensity of the color shows how confident that prediction is.

What Library Are You Using?

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


This page is created by Joshua Lui, based on the original work of Daniel Smilkov and Shan Carter. It is a continuation of many people’s previous work — most notably Andrej Karpathy’s convnet.js demo and Chris Olah’s articles about neural networks.