Coding Grandmaster · project 45 of 46 · premium, free for now

Teaching 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.

Learn

Training a sigmoid neuron uses gradient descent again. For each example, the error is the output minus the target. The sigmoid's slope is output × (1 − output), so each weight moves by learning rate × error × slope × its input, against the gradient. After many passes the outputs for the OR table settle near 0, 1, 1, 1.

Your task

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.

Stretch challenge

Print the loss every 100 epochs and watch it fall.

← A two-layer network Why XOR needs a hidden layer →

Python

Press Run to see what your program prints.

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 →44

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.

Start →46

Why 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

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Drop a ball through a Plinko board into the bin with the correct answer.

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FAQ

Teaching a neuron OR: common questions

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