Difference between Sigmoid and Softmax activation function?
Sigmoid activation function is a type of logistic activation function. It is used in the hidden layers of neural networks to transform the linear output into a nonlinear one. Softmax activation function is used in the output layer of neural networks to convert the linear output into a probabilistic one.
Sigmoid activation functions are used when the output of the neural network is continuous. Softmax activation functions are used when the output of the neural network is categorical.
The sigmoid function is a nonlinear, bounded function that maps a real-valued input to an output in between 0 and 1. It is a mathematical function that is used in artificial neural networks to produce an output.
The softmax function is a nonlinear, unbounded function that maps a real-valued input to an output in between 0 and 1 that sums to 1 for each input vector.
Softmax is mostly used in:
- Artificial and Convolutional Neural Networks– When using neural networks, some form of output normalization is usually applied. This idea is meant to map the non-normalized output of the data to the probability distribution for output classes. It’s most often used in the final layers of a neural network-based classifier. Its training is usually conducted using either log-loss or cross-entropy. This is a non linear variant of multinomial logistic regression (Softmax Regression)
- Other multiclass classification methods, such as Multiclass Linear Discriminant Analysis (MLDA), Naive Bayes Classifiers, etc
- Reinforcement Learning —You can use a Softmax function to convert input values into scaled probabilities of the actions.
Softmax is used for multi-classification in the Logistic Regression model, whereas Sigmoid is used for binary classification in the Logistic Regression model.
Characteristics of a Sigmoid Activation Function
- It is used for Binary Classification in the Logistic Regression model
- The probabilities sum of sigmoid does not need to be 1
Characteristics of a Softmax Activation Function
- It is used for Multi-classification in the Logistics Regression model
- The probabilities of softmax sum will be 1