Let's see how this happens. Like the hidden layer, the output layer has a weight matrix (let's call it U), but some models don't include a bias vector b in the output layer, so we'll simplify by eliminating the bias vector in this example. The weight matrix is multiplied by its input vector (h) to produce the intermediate output z. z = Uh There are n 2 output nodes, so z ∈ R n 2 , weight matrix U has dimensionality U ∈ R n 2 ×n 1 , and element U i j is the weight from unit j in the hidden layer to unit i in the output layer.