There are many activation functions in machine learning. I explained some most commonly used activation functions.
Sigmoid is used as the gating function for the 3 gates(in, out, forget) in LSTM, because it outputs a value between 0 and 1, there can be either no flow or complete flow of information throughout the gates. To overcome the vanishing gradient problem, we need a method whose second derivative can sustain for a long range before going to zero. Tanh is a good function that has all the above properties.
A neuron unit should be bounded, easily differentiable, monotonic and easy to handle. You can use the ReLU function in place of the tanh function. Before changing the choice for activation functions, you must know what are the advantages and disadvantages of your choice over others.
Sigmoid formula:
Sigmoid(z) = 1 / (1 + exp(-z))
1st order derivative: sigmoid'(z) = -exp(-z) / 1 + exp(-z)^2
Advantages
Sigmoid function has all the fundamental properties of a good activation function.
Tanh formula:
Mathematical expression:
tanh(z) = [exp(z) - exp(-z)] / [exp(z) + exp(-z)]
1st order derivative:
tanh'(z) = 1 - ([exp(z) - exp(-z)] / [exp(z) + exp(-z)])^2 = 1 - tanh^2(z)
Advantages:
(1) Often found to converge faster in practice
(2) Gradient computation is less expensive
Hard Tanh formula:
Mathematical expression:
hardtanh(z) = -1 if z < -1; z if -1 <= z <= 1; 1 if z > 1
1st order derivative:
hardtanh'(z) = 1 if -1 <= z <= 1; 0 otherwise
Advantages:
(1) Computationally cheaper than Tanh
(2) Saturate for magnitudes of z greater than 1
ReLU formula
Mathematical expression:
relu(z) = max(z, 0)
1st order derivative:
relu'(z) = 1 if z > 0; 0 otherwise
Advantages:
(1) Does not saturate even for large values of z
(2) Found much success in computer vision applications
Leaky ReLU
Mathematical expression:
leaky(z) = max(z, k dot z) where 0 < k < 1
1st order derivative:
relu'(z) = 1 if z > 0; k otherwise
Advantages:
(1) Allows propagation of error for non-positive z which ReLU doesn't
I hope this explanation helps.
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