Which activation function is often used in the output layer for multi-class classification problems?Review LaterSigmoidReLUSoftmaxTanh

Correct Activation Function for Multi-Class Classification

In multi-class classification problems, the activation function commonly used in the output layer is Softmax.

Explanation

1. Softmax Function: The Softmax function converts the raw output scores (logits) from the last layer of a neural network into probabilities. It does this by exponentiating each score and then normalizing by dividing by the sum of the exponentiated scores. This results in a probability distribution over the classes, where the probabilities of all classes will sum to 1.

2. Other Activation Functions:

   • Sigmoid: Typically used for binary classification, as it outputs a value between 0 and 1 but doesn’t provide a probabilistic interpretation for multiple classes.
   • ReLU (Rectified Linear Unit): Commonly used in hidden layers for its ability to mitigate vanishing gradient issues, but not suitable for output layers in multi-class scenarios.
   • Tanh: Like Sigmoid, it’s also not preferred for multi-class outputs due to its range (-1 to 1) and is generally used in hidden layers.

Conclusion

Therefore, for multi-class classification tasks, the Softmax activation function is the appropriate choice as it provides a clear probabilistic interpretation of predictions across multiple classes.