Neural Network Explorer

Explore how a 2-layer neural network computes its output. Adjust weights, change activation functions, and see the forward pass propagate through the network in real time.

Network Visualization

Network Controls

Activation

Hidden Size

Input x

Input y

Forward Pass

Hidden 1

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Hidden 2

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Hidden 3

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Output

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σ(W · x + b)

How it works: Inputs x₁, x₂ are multiplied by weights, summed with biases, and passed through an activation function. Adjust sliders to see how each weight affects the output.

What's happening here? A neural network is like a series of pipes and faucets. The inputs (x₁, x₂) are water flowing in. The weights (sliders) are faucets that control how much water passes through each pipe. The hidden layer neurons are mixing chambers that combine the flows. The activation function (ReLU, Sigmoid, etc.) is like a filter that decides "only let enough water through if it's strong enough." The output is what comes out the other end. Green connections mean a weight is pushing the signal forward; red means it's pushing against it. Try sliding weights to zero — that connection shuts off completely. Change the activation and watch how the numbers change.