Get the latest tech news

The Softmax function and its derivative


kes an N-dimensional vector of arbitrary real values and produces another N-dimensional vector with real values in the range (0, 1) that add up to 1.0. It maps : And the actual per-element formula is: It's easy to see that is always positive (because of the exponents); moreover, since the numerator appears in the denominator summed up with some other positive numbers, .

None

Get the Android app

Or read this on Hacker News

Read more on:

Photo of Derivative

Derivative

Photo of SoftMax

SoftMax

Photo of Softmax function

Softmax function

Related news:

News photo

Softmax: Why neural networks need non-linearity? life isn't straight-line simple

News photo

Softmax, can you derive the Jacobian? And should you care?

News photo

RE#: High performance derivative-based regular expression matching (2024)