Coding Grandmaster · project 44 of 46 · premium, free for now
A two-layer network
Write forward(x1, x2) for a network with two hidden neurons and one output neuron, using the given weights, and print the output rounded to 3 decimal places for the four input pairs.
Learn
Stack neurons in layers: each neuron in the hidden layer reads all the inputs, and the output neuron reads the hidden layer's outputs. This forward pass is just the single-neuron calculation repeated, with the weights stored as a list of lists, one list per neuron. Hidden layers let a network represent patterns no single neuron can.
Your task
Write forward(x1, x2) for a network with two hidden neurons and one output neuron, using the given weights, and print the output rounded to 3 decimal places for the four input pairs.
Stretch challenge
Add a third hidden neuron. Which lists must change?
Press Run to see what your program prints.
More in this module
Keep going in Coding Grandmaster
A single neuron
Write sigmoid(z) and neuron(inputs, weights, bias) which returns the sigmoid of the weighted sum plus the bias. Print the neuron's output, rounded to 4 decimal places, for each input pair.
Start →45Teaching a neuron OR
Train a neuron on the OR table, starting from weights 0.1, -0.1 and bias 0, for 1,000 epochs with learning rate lr. Print the four outputs rounded to 2 decimal places.
Start →46Why XOR needs a hidden layer
Write step(z) and xor_net(a, b) using two hidden neurons, h1 = step(a + b - 0.5) and h2 = step(a + b - 1.5), and the output step(h1 - h2 - 0.5). Print the XOR table, one line per row, like 0 1 -> 1.
Start →Practise the idea
⚙️ Play Function Machine
Feed numbers into a mystery machine, plot what comes out, then crack its secret rule.
FAQ
A two-layer network: common questions
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