It's also instructive to look at the intermediate results, the outputs of the two hidden nodes h 1 and h 2 . We showed in the previous paragraph that the h vector for the inputs x = [0, 0] was [0, 0]. Fig. 7.7b shows the values of the h layer for all 4 inputs. Notice that hidden representations of the two input points x = [0, 1] and x = [1, 0] (the two cases with XOR output = 1) are merged to the single point h = [1, 0]. The merger makes it easy to linearly separate the positive and negative cases of XOR. In other words, we can view the hidden layer of the network as forming a representation for the input.