  We introduce the use of rectified linear units (ReLU) as the classification
function in a deep neural network (DNN). Conventionally, ReLU is used as an
activation function in DNNs, with Softmax function as their classification
function. However, there have been several studies on using a classification
function other than Softmax, and this study is an addition to those. We
accomplish this by taking the activation of the penultimate layer $h_{n - 1}$
in a neural network, then multiply it by weight parameters $\theta$ to get the
raw scores $o_{i}$. Afterwards, we threshold the raw scores $o_{i}$ by $0$,
i.e. $f(o) = \max(0, o_{i})$, where $f(o)$ is the ReLU function. We provide
class predictions $\hat{y}$ through argmax function, i.e. argmax $f(x)$.
