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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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