2.3) Which of the following statements is/are true?

For sigmoid activation function, as input to the function becomes larger and positive, the

activation value approaches 1. Similarly, as it becomes very negative, the activation value

approaches 0.

For training MLPs, we do not use activation function as a step function, as in the flat

regions there is no slope and hence no gradient to learn.

For tanh activation function, as the value increases along the positive horizontal axis, the

activation value tends to 1 and along the negative horizontal axis, it tends towards -1,

with the centre value at 0.5.

The ReLU activation function is a piecewise linear function with a flat region for negative

input values and a linear region for positive input values.