ReLU caps the negative value to zero, but its output will be positive equal to the same values as the input. It has a constant gradient for positive values and a zero gradient for negative values. The following is a graph of ReLU:

As shown, ReLU doesn't fire at all for negative values. The computational complexity of this activation function is lower than the functions described previously; hence, the prediction is faster. In the next section, you will see how to interconnect several perceptrons to form a deep neural network.