APPLICATION OF SUPERVISED LEARNING
NEURAL NETWORK
|
Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
|
What does a ReLu activation function do?
|
|
Returns 0 or 1 depending on if the number is positive or negative
|
|
|
Returns e to the power of the input
|
|
|
Returns 0 if the number is negative, and the x=y if the input is positive
|
|
|
Returns a number between 0 or 1, based on the absolute value of the input
|
Explanation:
Detailed explanation-1: -When most of these neurons return output zero, the gradients fail to flow during backpropagation, and the weights are not updated. Ultimately a large part of the network becomes inactive, and it is unable to learn further.
Detailed explanation-2: -ReLU function is its derivative both are monotonic. The function returns 0 if it receives any negative input, but for any positive value x, it returns that value back. Thus it gives an output that has a range from 0 to infinity.
Detailed explanation-3: -The rectified linear activation function or ReLU for short is a piecewise linear function that will output the input directly if it is positive, otherwise, it will output zero.
There is 1 question to complete.