Neural networks are sized in one of two ways: the number of neurons, or the number of trainable parameters. When given the following two neural networks:

We can either say that this 2-layer network has \(4 + 2 = 6\) neurons (donâ€™t include the inputs), or that the network has \(3 * 4 + 4 * 2 = 20\) weights and \(4 + 2 = 6\) biases for a total of 26 learnable parameters.

In this 3-layer network, we see \(4 + 4 + 1 = 9\) neurons or \(3 * 4 + 4 * 4 + 4 * 1 = 32\) weights and \(4 + 4 + 1 = 9\) biases, for a total of 41 learnable parameters.