Sigmoid Vs Softmax

Sigmoid Graph

Softmax Graph

Difference Between Sigmoid Function and Softmax Function

The below are the tabular differences between Sigmoid and Softmax function.

	Softmax Function	Sigmoid Function
1	Used for multi-classification in logistic regression model.	Used for binary classification in logistic regression model.
2	The probabilities sum will be 1	The probabilities sum need not be 1.
3	Used in the different layers of neural networks.	Used as activation function while building neural networks.
4	The high value will have the higher probability than other values.	The high value will have the high probability but not the higher probability.

Conclusion

In this article, you learn in detail about two functions that determine the logistic regression model. Just for a glance.

• Softmax: Used for the multi-classification task.
• Sigmoid: Used for the binary classification task.