For me, the main insight was to simplify the gradient of the log sum from the denominator of the softmax using the definition of the softmax: $$\pi_ {\theta} (s,a)$$. Thanks!

For others who end up here, this thread is about computing the derivative of the cross-entropy function, which is the cost function often used with a softmax layer (though the derivative of the cross-entropy …

The largest element in the input vector remains the largest element after the softmax function is applied to the vector, hence the "max" part. The "soft" signifies that the function keeps information about the …

Nov 19, 2016 · Question: Is the softmax function Lipschitz in the 2-norm? If so, is it Lipschitz with Lipschitz constant 1? I am asking because I have reason to believe that this is the case (through …

Dec 8, 2019 · Except it raises questions about what the user is trying to do. Sigmoid should be points connected by line segments, the softmax should use the same list of points, presumably from -5 to 5 …

Aug 31, 2021 · I would like to plot the softmax activiation function. I know that there is already a similar question but unfortunately neither the comments, nor the answer provided there could really help me …

Is it possible to revert the softmax function in order to obtain the original values xi x i?

May 29, 2016 · How to Derive Softmax Function Ask Question Asked 9 years, 7 months ago Modified 1 year, 2 months ago

Jul 22, 2024 · But why is that? If one takes the derivative of the MSE function, the sum typically vanished due to the same method as mentioned here. Why do we have to distribute the derivative …

Mar 12, 2013 · I'm reading Bishop's book on Pattern Recognition and machine learning and I wanted to reproduce a calculation for the softmax function, also known as normalized exponential. Basically, …