the resulting (rounded) softmax(z) is [0.055, 0.090, 0.006, 0.099, 0.74, 0.010] Again like the sigmoid, the input to the softmax will be the dot product between a weight vector w and an input vector x (plus a bias). But now we'll need separate weight vectors (and bias) for each of the K classes.