What is the importance of the sigmoid function in backpropagation learning?
What is the importance of the sigmoid function in backpropagation learning?
Homework Answers
It is an activation function of form f(x) = 1 / 1 + exp(-x).
Its output lies in the range of 0 to 1.
It is an S-shape curve.
It is popular because
1. Vanishing gradient problem
2. Secondly, its output isn’t zero centered. It makes the gradient updates go too far in different directions. 0 < output < 1, and it makes optimization harder.
3. Sigmoids saturate and kill gradients.
4. Sigmoids have slow convergence.
If any image score is less than 0 then it converts that image score to 0.
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