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# How to normalize a tensor in PyTorch?

A tensor in PyTorch can be normalized using the **normalize()** function
provided in the **torch.nn.functional** module. This is a non-linear
activation function.

It performs

**Lp normalization**of a given tensor over a specified dimension.It returns a tensor of normalized value of the elements of original tensor.

A 1D tensor can be normalized over dimension 0, whereas a 2D tensor can be normalized over both dimensions 0 and 1, i.e., column-wise or row-wise.

An n-dimensional tensor can be normalized over dimensions (0,1, 2,..., n-1).

## Syntax

torch.nn.functional.normalize(input, p=2.0, dim = 1)

## Parameters

**Input**– Input tensor**p**– Power (exponent) value in norm formulation**dim**– Dimension over which the elements are normalized.

## Steps

We could use the following steps to normalize a tensor −

Import the

**torch**library. Make sure you have it already installed.

import torch from torch.nn.functional import normalize

Create a tensor and print it.

t = torch.tensor([[1.,2.,3.],[4.,5.,6.]]) print("Tensor:", t)

Normalize the tensor using different p values and over different dimensions. The above defined tensor is a 2D tensor, so we can normalize it over two dimensions.

t1 = normalize(t, p=1.0, dim = 1) t2 = normalize(t, p=2.0, dim = 0)

Print the above computed normalized tensor.

print("Normalized tensor:\n", t1) print("Normalized tensor:\n", t2)

## Example 1

# import torch library import torch from torch.nn.functional import normalize # define a torch tensor t = torch.tensor([1., 2., 3., -2., -5.]) # print the above tensor print("Tensor:\n", t) # normalize the tensor t1 = normalize(t, p=1.0, dim = 0) t2 = normalize(t, p=2.0, dim = 0) # print normalized tensor print("Normalized tensor with p=1:\n", t1) print("Normalized tensor with p=2:\n", t2)

## Output

Tensor: tensor([ 1., 2., 3., -2., -5.]) Normalized tensor with p=1: tensor([ 0.0769, 0.1538, 0.2308, -0.1538, -0.3846]) Normalized tensor with p=2: tensor([ 0.1525, 0.3050, 0.4575, -0.3050, -0.7625])

## Example 2

# import torch library import torch from torch.nn.functional import normalize # define a 2D tensor t = torch.tensor([[1.,2.,3.],[4.,5.,6.]]) # print the above tensor print("Tensor:\n", t) # normalize the tensor t0 = normalize(t, p=2.0) # print the normalized tensor print("Normalized tensor:\n", t0) # normalize the tensor in dim 0 or column-wise tc = normalize(t, p=2.0, dim = 0) # print the normalized tensor print("Column-wise Normalized tensor:\n", tc) # normalize the tensor in dim 1 or row-wise tr = normalize(t, p=2.0, dim = 1) # print the normalized tensor print("Row-wise Normalized tensor:\n", tr)

## Output

Tensor: tensor([[1., 2., 3.], [4., 5., 6.]]) Normalized tensor: tensor([[0.2673, 0.5345, 0.8018], [0.4558, 0.5698, 0.6838]]) Column-wise Normalized tensor: tensor([[0.2425, 0.3714, 0.4472], [0.9701, 0.9285, 0.8944]]) Row-wise Normalized tensor: tensor([[0.2673, 0.5345, 0.8018], [0.4558, 0.5698, 0.6838]])

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