In the hidden layers of TensorFlow Playground, what does the color of the lines represent?

In TensorFlow Playground, the color of the lines connecting the neurons in the hidden layers visually indicates the weights of the connections between those neurons. Each line's color can typically vary depending on whether the weight is positive or negative, providing an intuitive understanding of how strongly two neurons are connected and in which direction the influence flows.

When the weight is positive, the line may be colored one way (often lighter shades), indicating that an increase in the output of one neuron will lead to an increase in the output of the connected neuron. Conversely, if the weight is negative, the line may be colored differently (often darker shades), suggesting that an increase in the output of one neuron will decrease the output of the connected neuron. This visualization helps users quickly grasp how changes in one part of the network can affect the others, making it a valuable educational tool for understanding neural networks.

The other options do not relate to the color of the lines: the type of neurons and activation functions pertain more to the design and behavior of individual neurons rather than their connections, while biases are typically represented differently and do not affect the color of the connection lines.