Difference Between Sigmoid Function and Softmax Function

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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.

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

http://dataaspirant.com/2017/03/07/difference-between-softmax-function-and-sigmoid-function/