Neural Network Visualizer

What layer types can I add to my neural network visualization?

The visualizer supports all common layer types including Dense/Fully Connected, Convolutional (Conv2D), Pooling (MaxPool, AvgPool), Dropout, BatchNormalization, LSTM/GRU, Input, and Output layers. Each layer displays its parameters and can be customized with activation functions.

How do I create a neural network architecture?

Simply drag layer types from the toolbox onto the canvas. Click and drag from output nodes to input nodes to create connections. Double-click any layer to edit its parameters like neurons, filters, or activation functions. The tool automatically calculates total parameters.

Does the Neural Network Visualizer work on mobile devices?

Yes, the visualizer is fully responsive and works on all modern browsers including mobile devices. On tablets, you can use touch gestures for dragging and connecting layers. For the best experience on mobile, we recommend landscape orientation.

Can I export my neural network design to actual code?

Currently, you can export your architecture as a high-resolution PNG or SVG image for documentation. The tool also generates a JSON representation of your model structure. Code generation for TensorFlow/PyTorch is planned for future updates.

How can I visualize pre-trained model architectures?

You can import common architectures like VGG, ResNet, or BERT using the template menu. Additionally, you can paste a JSON model configuration to recreate existing architectures. This helps in understanding and modifying established model designs.