TensorFlow Playground — Network controls

Dev ToolsSee it liveplayground.tensorflow.org

A neural-network playground exposes data, features, hidden layers and output in one view.

TensorFlow Playground — Network controls — captured interface — Viewport capture · opening screen

Public demo · 2026-09-05Open full image ↗

AI interfaces

Why this pattern works

Make the relationship between input data, model structure and output visible on one canvas.

Design notes

Surface: A neural-network playground exposes data, features, hidden layers and output in one view.
Navigation: A top control strip holds playback and training parameters.
Layout: A left-to-right pipeline connects dataset controls, feature nodes, hidden layers and an output chart.
Density: High information density with aligned controls and a visual network.
Color: White, teal, blue and orange.
Type: Small labels and numerical controls, grouped under uppercase section headings.
Pattern: Make the relationship between input data, model structure and output visible on one canvas.
States: Built-in synthetic dataset before training, with the epoch counter at zero.
Don't: Adapt the layout to your own product and supported capabilities; write your own examples and keep important actions clearly labeled.

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Tech stack

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What should the tool respect?

Preview & edit prompt

Prompt text

# My project
[Describe the product, audience, and screen you want to improve]

Stack: Use the existing project stack
Task: Implement the interface and verify the result in the existing project.

## Tool guidance (Any AI tool)
Use available project tools to inspect, implement, and test. State any unavailable capabilities.

# Design reference: TensorFlow Playground — Network controls
Source: https://playground.tensorflow.org/#activation=tanh&batchSize=10&dataset=circle&regDataset=reg-plane&learningRate=0.03&regularizationRate=0&noise=0&networkShape=4,2&seed=0.47142&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=classification&initZero=false&hideText=false
Swipefile reference: https://swipefile.design/ref/ai-tensorflow-playground/
Captured: 2026-09-05. This is a dated reference, not a claim about the current live product.
Capture scope: one viewport. Do not infer unseen page sections or a complete user flow.

## Objective
Adapt the interaction and layout patterns below to my project. Use my own content and identity. Do not reproduce logos, proprietary copy, or brand assets.

## Reference notes
- surface: A neural-network playground exposes data, features, hidden layers and output in one view.
- nav: A top control strip holds playback and training parameters.
- layout: A left-to-right pipeline connects dataset controls, feature nodes, hidden layers and an output chart.
- density: High information density with aligned controls and a visual network.
- color: White, teal, blue and orange.
- typography: Small labels and numerical controls, grouped under uppercase section headings.
- pattern: Make the relationship between input data, model structure and output visible on one canvas.
- states: Built-in synthetic dataset before training, with the epoch counter at zero.
- dont: Adapt the layout to your own product and supported capabilities; write your own examples and keep important actions clearly labeled.

Useful for: Make the relationship between input data, model structure and output visible on one canvas.

## Acceptance criteria
- Existing project conventions, design tokens, working functionality, and unrelated changes are preserved.
- Two or three reference principles clearly support the user's task; conflicts are resolved in favor of usability and the existing product.
- Reusable components and semantic HTML express the interface. Available licensed assets are used; important UI text is never rendered as a bitmap.
- Every visible control has a defined behavior. Loading, empty, error, success, disabled, and long-content states are specified where relevant. Demo data is clearly labeled.
- Navigation and content adapt at 375px, 768px, and 1440px without horizontal page overflow. Keyboard use, visible focus, labeled inputs, at least 44px touch targets, readable contrast, and reduced motion are supported.
- Verification distinguishes rendered behavior from proposals and identifies anything still requiring a backend or external service.

## Quality bar
Clear hierarchy, consistent spacing, restrained surfaces, and purposeful motion. Avoid decorative gradients, excessive cards, placeholder buttons, and invented metrics unless the project calls for them. Do not claim a feature is connected or tested without evidence.

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## Objective
Adapt the interaction and layout patterns below to my project. Use my own content and identity. Do not reproduce logos, proprietary copy, or brand assets.

Useful for: Make the relationship between input data, model structure and output visible on one canvas.

TensorFlow Playground — Network controls

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