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() function
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:
Syntax: torch.gradient(values)
Parameters:
- values(Tensor): this parameter is represents the values of the function.
Example 1
In this example, we estimate the gradient of a function for a 1D tensor.
Python3
import torch
tens = torch.tensor([-2., 1., -3., 4., 5.])
print(" Input tensor: ", tens)
def fun(tens):
return tens**2+5
values = fun(tens)
print(" Function Values: ", values)
grad = torch.gradient(values)
print(" Estimated Gradients of fun() - ", grad)
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Output:
Example 2
In this example, we estimate the gradient of a function for a 2D tensor.
Python3
import torch
tens = torch.tensor([[-1., 3., -5.],
[-4., 5., 2.],
[-2., 3., 4.], ])
print("\n Input tensor: \n", tens)
def fun(tens):
return tens**3
values = fun(tens)
print("\n Function Values: \n", values)
grad_dim_0 = torch.gradient(values, dim=0)
print("\n Estimated Gradients of fun() in dim=0 - \n", grad_dim_0)
grad_dim_1 = torch.gradient(values, dim=1)
print("\n Estimated Gradients of fun() in dim=1 - \n", grad_dim_1)
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Output: