Why use relu activation function

ReLU stands for Rectified Linear Unit. The main advantage of using the ReLU function over other activation functions is that it does not activate all the neurons at the same time.

1. Why does CNN use ReLU activation function?
2. Is ReLU the best activation function?
3. Why does ReLU work better than TanH?

Why does CNN use ReLU activation function?

As a consequence, the usage of ReLU helps to prevent the exponential growth in the computation required to operate the neural network. If the CNN scales in size, the computational cost of adding extra ReLUs increases linearly.

Is ReLU the best activation function?

The main advantages of the ReLU activation function are: Convolutional layers and deep learning: It is the most popular activation function for training convolutional layers and deep learning models. Computational simplicity: The rectifier function is trivial to implement, requiring only a max() function.

Why does ReLU work better than TanH?

ReLu is the best and most advanced activation function right now compared to the sigmoid and TanH because all the drawbacks like Vanishing Gradient Problem is completely removed in this activation function which makes this activation function more advanced compare to other activation function.