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How to create tensors with gradients in PyTorch?
To create a tensor with gradients, we use an extra parameter "requires_grad = True" while creating a tensor.
requires_grad is a flag that controls whether a tensor requires a gradient or not.
Only floating point and complex dtype tensors can require gradients.
If requires_grad is false, then the tensor is same as the tensor without the requires_grad parameter.
Syntax
torch.tensor(value, requires_grad = True)
Parameters
value – tensor data, user-defined or randomly generated.
requires_grad – a flag, if True, the tensor is included in the gradient computation.
Output
It returns a tensor with requires_grad as True.
Steps
Import the required library. The required library is torch.
Define a tensor with requires_grad = True
Display the created tensor with gradients.
Let's have a couple of examples for a better understanding of how it works.
Example 1
In the following example, we created two tensors. One tensor is without requires_grad = True and the other is with requires_grad = True.
# import torch library
import torch
# create a tensor without gradient
tensor1 = torch.tensor([1.,2.,3.])
# create another tensor with gradient
tensor2 = torch.tensor([1.,2.,3.], requires_grad = True)
# print the created tensors
print("Tensor 1:", tensor1)
print("Tensor 2:", tensor2)Output
Tensor 1: tensor([1., 2., 3.]) Tensor 2: tensor([1., 2., 3.], requires_grad=True)
Example 2
# import required library
import torch
# create a tensor without gradient
tensor1 = torch.randn(2,2)
# create another tensor with gradient
tensor2 = torch.randn(2,2, requires_grad = True)
# print the created tensors
print("Tensor 1:
", tensor1)
print("Tensor 2:
", tensor2)Output
Tensor 1:
tensor([[-0.9223, 0.1166],
[ 1.6904, 0.6709]])
Tensor 2:
tensor([[ 1.1912, -0.1402],
[-0.2098, 0.1481]], requires_grad=True)Example 3
In the following example, we created a tensor with gradients using numpy array.
# import the required libraries import torch import numpy as np # create a tensor of random numbers with gradients # generate 2x2 numpy array of random numbers v = np.random.randn(2,2) # create a tensor with above random numpy array tensor1 = torch.tensor(v, requires_grad = True) # print above created tensor print(tensor1)
Output
tensor([[ 0.7128, 0.8310], [ 1.6389, -0.3444]], dtype=torch.float64, requires_grad=True)
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