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!

Invert the softmax function Ask Question Asked 7 years, 6 months ago Modified 1 year, 11 months ago

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 …

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 …

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 …

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 …

Dec 21, 2022 · I am creating a visual transformer from scratch as an exercise to have a better understanding of machine learning. Within the network it multiplies 2 matrices, then the softmax …

Nov 9, 2019 · I am looking for a proof that the multi-class SoftMax logistic regression using Maximum Liklihood has a convex performance function? In particular I am interested in showing the function: …

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, …

May 14, 2022 · The softmax activation function has the nice property that it is translation invariant. The only thing that matters is the distances between the components in $\mathbf z$, not their particular …