Question

Let's say, you are using activation function X in hidden layers of neural network. At a neuron for any given input, you get the output as 0.0001". Which of the following activation function could X represent? (A) ReLU (B)tanh (9) SIGMOID (D) None of these​

Answer 1

The given output value of 0.0001 does not directly correspond to any of the common activation functions (ReLU, tanh, sigmoid) typically used in neural networks. Let's briefly discuss each of these activation functions:

A) ReLU (Rectified Linear Activation): ReLU outputs 0 for any input less than 0 and the input value itself for any input greater than or equal to 0. So, it can't be ReLU because ReLU would output 0 for any input less than 0, not 0.0001.

B) Tanh (Hyperbolic Tangent): Tanh squashes input values between -1 and 1. It can produce small positive or negative values, but 0.0001 is not a typical output value for tanh.

C) Sigmoid: Sigmoid squashes input values between 0 and 1. It can produce small positive values, but 0.0001 is smaller than the typical output of sigmoid for most inputs.

Given the options provided (A, B, C), none of them seems to correspond directly to the output of 0.0001. It's possible that another activation function not listed here was used, or there may be additional factors affecting the output.

So, the correct answer is (D) None of these.

Step-by-step explanation: