Deep Learning using Rectified Linear Units (ReLU)
This is an incremental addition to studies exploring alternative classification functions for deep learning.
The paper tackles the problem of using rectified linear units (ReLU) as a classification function in deep neural networks instead of the conventional Softmax, and the result is a method that thresholds raw scores to provide class predictions via argmax.
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 $θ$ 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 $f(x)$.