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.
Browse neural network diagrams made with Figviz, or generate your own above
A classic feedforward network with four input nodes, two hidden layers, and two output nodes, all fully connected.
A deep network architecture with five hidden layers showing the encoder-decoder shape common in autoencoders.
A CNN architecture showing convolution and max-pooling blocks followed by fully connected layers and a softmax output.
An unrolled RNN across four time steps, with hidden state arrows linking each step and labeled inputs and outputs.
A single perceptron with labeled inputs, weights, summation, and activation function output.
A fully annotated network showing node counts, layer names, and activation functions at every layer, suitable for academic papers.
Start with one of these examples, then adapt the subject, labels, data, or layout for your own use.
A classic feedforward network with four input nodes, two hidden layers, and two output nodes, all fully connected.
Create a labeled feedforward neural network diagram with 4 input nodes, two hidden layers of 5 nodes each, and 2 output nodes. Draw circular nodes in vertical columns, connect every node to the next layer, and label Input Layer, Hidden Layer 1, Hidden Layer 2, and Output Layer. Classic black on white style. White background.
A deep network architecture with five hidden layers showing the encoder-decoder shape common in autoencoders.
Create a labeled deep neural network diagram with 6 input nodes, hidden layers of 8, 12, 8, and 4 nodes, and 3 output nodes. Show circular nodes in columns, draw all connections between adjacent layers, and label each layer by name. Classic textbook style. White background.
A CNN architecture showing convolution and max-pooling blocks followed by fully connected layers and a softmax output.
Create a labeled CNN architecture diagram showing an input image, two convolution layers with ReLU, two max-pooling layers, a flatten step, a fully connected layer, and a softmax output. Label each block clearly with its operation name. Colorful style. White background.
An unrolled RNN across four time steps, with hidden state arrows linking each step and labeled inputs and outputs.
Create a labeled unrolled RNN diagram across 4 time steps. At each step show an input node at the bottom, a hidden state node in the middle, and an output node at the top. Draw horizontal arrows connecting hidden states between steps and vertical arrows for input-to-hidden and hidden-to-output. Label t-1, t, t+1, t+2. Classic style. White background.
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.
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.
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