Neural Net Visualizer
Build a neural network, set its inputs, and watch a real forward pass flow through every neuron.
Neural Network Visualizer
Build your own feed-forward neural network, adjust the inputs, then watch data flow layer-by-layer as the network computes its output. Every number you see is genuinely calculated — using the sigmoid activation function.
All Activation Values — Text View
Input
Hidden
Output
What is a Forward Pass?
A neural network is made of layers of neurons. Each neuron receives numbers from the previous layer, multiplies each by a weight, adds them all up, then adds a bias. That total is called the weighted sum (z).
Then the neuron applies an activation function — here we use sigmoid: σ(z) = 1 / (1 + e^−z). Sigmoid squashes any number into the range (0, 1), which helps the network produce consistent outputs.
A forward pass is when data flows from left to right through all layers until we get the final output. Click any neuron to see its exact computation!