Which of the following is a disadvantage of ReLU activation function?

Which of the following is a disadvantage of ReLU activation function?

Disadvantages: Non-differentiable at zero and ReLU is unbounded. The gradients for negative input are zero, which means for activations in that region, the weights are not updated during backpropagation. This can create dead neurons that never get activated.

Which activation function produces always positive value?

1 Answer. Choose some c>0 and use the activation f(x)=ReLU(x)+c. This is always positive, and preserves some of the nice qualities of ReLUs (though it obviously does not preserve the sparsity property).

Which unit holds the activation function?

RELU :- Stands for Rectified linear unit. It is the most widely used activation function. Chiefly implemented in hidden layers of Neural network. Equation :- A(x) = max(0,x).

Can I use ReLU activation function in output layer?

You output to a linear layer as your Q value estimate can generally take on any real value. And then you add a mean squares error loss with the linear layer output. This set up is similar any general regression problem with a neural nnetwork. Relu is used quite often but for hidden layer activation.

Can a ReLU function handle a negative input?

With the backpropagation algorithm it should be possible that the outputs of the previous hidden layers are changed in such a way that, eventually, the input to the ReLU function will become positive again. Then the ReLU would not be dead anymore.

Is there any way to feed the data into a RELU network?

Is there any way to feed the data into a ReLU network without converting it all to positive and having a separate input which says if the data is negative or positive?

Can a nonlinear activation function be used after each layer?

Without nonlinear activation function after each layer, the whole network act as a simple linear transformation, which does not have so much power for complicated task such as digit recognition and image-net classification.