Softmax Activation Function — How It Actually Works

Softmax Activation Function — How It Actually Works

When working on machine learning problems, specifically, deep learning tasks, Softmax activation function is a popular name. Softmax is a function placed at the end of deep learning network to convert logits into classification probabilities.

When working on machine learning problems, specifically, deep learning tasks, Softmax activation function is a popular name. It is usually placed as the last layer in the deep learning model.

It is often used as the last activation function of a neural network to normalize the output of a network to a probability distribution over predicted output classes. — Wikipedia [link]

Softmax is an activation function that scales numbers/logits into probabilities. The output of a softmax is a vector (say v) with probabilities of each possible outcome. The probabilities in vector v sums to one for all possible outcomes or classes.

Mathematically, Softmax is defined as,

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