I strongly believe PyTorch is one of the best deep learning frameworks right now and will only go from strength to strength in the near future. This is a great time to learn how it works and get onboard. ST Gumbel Softmax uses the argmax in the forward pass, whose gradients are then approximated by the normal Gumbel Softmax in the backward pass. So afaik, a ST Gumbel Softmax implementation would require the implementation of both the forward and backward pass functions, since they are different and the forward pass cannot be approximated with autograd.

Argmax and Max Calculus Mark Schmidt January 6, 2016 1 Argmax, Max, and Supremum We de ne the argmax of a function fde ned on a set Das argmax x2D f(x) = fxjf(x) f(y);8y2Dg: In other words, it is the set of inputs xfrom the domain Dthat achieve the highest function value. For example, argmax x2R x 2 = f0g, since x2 is maximized when x= 0. Note ... My softmax function After years of copying one-off softmax code between scripts, I decided to make things a little dry -er: I sat down and wrote a darn softmax function. The goal was to support \(X\) of any dimensionality, and to allow the user to softmax over an arbitrary axis. Maximizes all values from the src tensor into out at the indices specified in the index tensor along a given axis dim.If multiple indices reference the same location, their contributions maximize (cf. scatter_add()).

最近想系统地学习一下深度学习，之前看过pytorch，tensorflow等一些深度学习的框架，也了解过其中的一些基础知识，但是没怎么真正上手练习过，所以这次从最简单的来，用softmax回归来识别MNIST数据集。 at the true argmax, to enforce the input to soft argmax to be unimodal. Window function should be specified as one of the following options: None, "Parzen", "Uniform"

A kind of Tensor that is to be considered a module parameter. Parameters are Tensor subclasses, that have a very special property when used with Module s - when they’re assigned as Module attributes they are automatically added to the list of its parameters, and will appear e.g.