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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:
", t1)
print("Normalized tensor:
", 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:
", 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:
", t1)
print("Normalized tensor with p=2:
", 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:
", t)
# normalize the tensor
t0 = normalize(t, p=2.0)
# print the normalized tensor
print("Normalized tensor:
", 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:
", 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:
", 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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