A question on Neural Network

Question

I am an amateur programmer and have recently learnt how to build a simple neural network using python which has an output range of 0 to 1 using sigmoid activation function. But is there any way in which I can get an output value which is greater than 1? For instance how can I train my Neural network to predict the square root of a number?

Answer 1

Rational Agent Exactly. The activation function, whether a sigmoid, tan-h, relu or any other just indicates a "trigger" that activates (or not) a particular neuron. The shape of the function determines the likeliness of the activation especially in areas close to zero.

Answer 2

Sigmoid function assumes 0-1 values only. It is a cumulative distribution function of probability density.
And since probability cannot be higher than 1, sigmoid may not, either.

By the way, the sigmoid function value is not the prediction of the value! It is just a probability that the given observation is of a predicted class.

Answer 3

why would you like to do that?
Isn't it easier to use a function to calculate the square root of a number?

Answer 4

Just out of curiosity

Answer 5

Thanks.
But isn't there any other way with which a Neural Network can be trained to output square root values.

Answer 6

their are different activation functions besides the sigmoid.however the sigmoid function isn't the networks output if I understand it righr