How do I make a neural network diagram?

Describe your architecture in the prompt box: list the layer types, node counts, and any special components. Choose a style and aspect ratio, then click Generate. Figviz produces a labeled diagram you can download. Refine the prompt to adjust any layer or label.

Is the neural network diagram generator free?

Yes. Every account starts with free credits you can use straight away, no credit card required. Buying a pay-as-you-go credit pack removes the watermark and unlocks 4K resolution exports. Credits never expire.

What architectures can Figviz draw?

Figviz can draw feedforward (fully connected) networks, deep networks with many hidden layers, convolutional neural networks (CNNs) with convolution and pooling blocks, recurrent neural networks (RNNs) in both looped and unrolled form, and single-neuron perceptron diagrams with labeled weights and activation functions.

Can I label the layers and show node counts?

Yes. Include layer names, node counts, and activation functions in your prompt (for example "Hidden Layer 1, ReLU, n=8") and Figviz will annotate each layer accordingly. This makes the diagram suitable for academic papers and technical slide decks.

Can I use the diagrams in ML papers or slide decks?

Yes. Diagrams generated with a free account include a watermark; diagrams from a paid credit account are watermark-free and can be used in your own papers, theses, slide decks, blog posts, and course materials. Download as a high-resolution PNG (up to 4K) for crisp printing.

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Neural Network Diagram Generator Neural Network Diagrams

Describe any neural network architecture and get a clean, labeled diagram in seconds. Nodes, layers, weights, and connections are all drawn and annotated so you can drop the result straight into a paper, slide deck, or course material.

Last updated: 2026-06-21

✓Feedforward, CNN, RNN and more✓Labeled layers and nodes✓Paper and slide ready✓Free to generate

Neural Network Diagram Generator

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Describe your neural network architecture

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Neural Network Diagram Examples

Browse neural network diagrams made with Figviz, or generate your own above

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Feedforward Neural Network

A classic feedforward network with four input nodes, two hidden layers, and two output nodes, all fully connected.

feedforwardfully connectedlabeled layers

Deep Neural Network

A deep network architecture with five hidden layers showing the encoder-decoder shape common in autoencoders.

deep learningautoencodermultiple layers

Convolutional Neural Network (CNN)

A CNN architecture showing convolution and max-pooling blocks followed by fully connected layers and a softmax output.

CNNconvolutionpooling

Recurrent Neural Network (RNN)

An unrolled RNN across four time steps, with hidden state arrows linking each step and labeled inputs and outputs.

RNNrecurrenttime series

Single Neuron (Perceptron)

A single perceptron with labeled inputs, weights, summation, and activation function output.

perceptronneuronweights

Labeled Neural Network with Layer Annotations

A fully annotated network showing node counts, layer names, and activation functions at every layer, suitable for academic papers.

annotatedpaper-readylayer counts

What is a neural network diagram generator?

A neural network diagram generator is a tool that turns a plain-English description of a neural network architecture into a clean visual diagram showing nodes, layers, and connections. Instead of placing shapes by hand in a drawing tool, you describe the number of layers and nodes, the type of network, and any special components, and the generator produces a ready-to-use image. Figviz uses AI to interpret the description and render the diagram, so researchers, students, and educators can illustrate complex architectures in seconds without any design experience.

How to make a neural network diagram

Choose the architecture type: feedforward, CNN, RNN, or another variant.

Specify the number of layers and the node count for each layer.

Describe any special components such as convolution blocks, pooling layers, or recurrent connections.

Select a style (Classic, Colorful, or Minimal) and the output aspect ratio.

Click Generate and review the diagram. Refine the prompt if any layer or label needs adjusting.

Download the high-resolution PNG for use in papers, slides, or teaching materials.

Common neural network architectures you can draw

Feedforward (fully connected) network: nodes arranged in vertical columns with edges between every node in adjacent layers. The simplest architecture and a good starting point for teaching.

Deep neural network: a feedforward network with many hidden layers, often shown with varying widths to illustrate encoder-decoder or bottleneck shapes.

Convolutional neural network (CNN): a sequence of convolution, activation, and pooling blocks followed by fully connected layers, commonly used for image recognition tasks.

Recurrent neural network (RNN): a network unrolled across time steps with hidden state arrows linking each step, used for sequences and time-series data.

Single neuron or perceptron: a single node with labeled input weights, a summation, and an activation function, useful for introducing the building block of neural networks.

Tips for clear neural network diagrams

Always state the node count for each layer explicitly so the diagram is not left to guesswork. For CNNs, name each block by its operation (Conv2D, MaxPool, Flatten) rather than by node count, as these layers are better shown as blocks than circles. For RNNs, specify whether you want the compact looped version or the unrolled version across time steps. Use the Colorful style for presentations where you want each layer type to stand out at a glance, and the Classic or Minimal style for academic papers where black-and-white printing is common. Add activation function labels (ReLU, Softmax) in the prompt if the diagram is intended for a technical audience.

Frequently asked questions

A neural network diagram generator is a tool that converts a text description of a neural network into a visual diagram. Figviz reads your description, draws the nodes and layers, connects them with edges, and labels everything so you have a finished diagram ready for papers or presentations without any manual drawing.

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