# How is RELU used on convolutional layer [duplicate]

I know that when dealing with artificial neural networks, RELU yields a value based on the weighted sum of the inputs plus a bias term. However, this logic does not seem to apply to convolutional neural networks.

Looking at the ResNet architecture, the outputs of the convolutional neural nets (what I believe to be feature maps), is added to the input x, and then RELU is applied onto it. What exactly does the RELU function do in this case? Do the convolution layers output feature maps, or something else?

## marked as duplicate by Sycorax, mdewey, kjetil b halvorsen, gunes, Siong Thye GohApr 26 at 14:20

It's no different from using ReLU in any other context. In a feedforward network, a standard usage is $$\text{ReLU}(Ax+b)$$. In a CNN, a standard usage is $$\text{ReLU}(\text{convolution}(y))$$: all you do is apply the convolution operation and then the ReLU operation.