How to Estimate the Gradient of a Function in One or More Dimensions in PyTorch?
In this article, we are going to see how to estimate the gradient of a function in one or more dimensions in PyTorch.
torch.gradient() method estimates the gradient of a function in one or more dimensions using the second-order accurate central differences method, and the function can be defined on a real or complex domain. For controllers and optimizers, gradient estimations are quite valuable. Gradient descent is a prominent optimization method that requires an estimate of the output derivatives with respect to each input at a given location. Let’s have a look at the syntax of the given method first:
- values(Tensor): this parameter is represents the values of the function.
In this example, we estimate the gradient of a function for a 1D tensor.
In this example, we estimate the gradient of a function for a 2D tensor.
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