How to Rescale a Tensor in the Range [0, 1] and Sum to 1 in PyTorch?

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In this article, we are going to discuss How to Rescale a Tensor in the Range [0, 1] and Sum to 1 in PyTorch using Python.

Softmax() method

The Softmax() method helps us to rescale a tensor of n-dimensional along a particular dimension, the elements of this input tensor are in between the range of [0,1] and the sum to 1. This method returns a tensor of the same shape and dimension as the input tensor and the values lie within the range [0, 1]. before moving further let’s see the syntax of the given method.

Syntax: torch.nn.Softmax(dim)

Parameters:

dim: The dim is dimension in which we compute the Softmax.

Returns: It will returns a tensor with same shape and dimension as the input tensor and the values are in between the range [0, 1].

Example 1: In this example, we rescale a 1D tensor in the range [0, 1] and sum to 1.

Python

# import required libraries 
import torch 
  
# define a tensor 
input_tens = torch.tensor([0.1237, 1.8373,  
                           -0.2343, -1.8373, 
                           0.2343]) 
  
print(" input tensor: ", input_tens) 
  
# Define the Softmax function 
softmax = torch.nn.Softmax(dim=0) 
  
# Apply above defined Softmax function 
# on input tensor 
output = softmax(input_tens) 
  
# display tensor that containing Softmax values 
print(" tensor that containing Softmax values: ",  
      output) 
  
# display sum 
print(" sum  = ", output.sum()) 

Output:

Example 2: In this example, we rescale a 2D tensor in the range [0, 1] and sum to 1.

Python

# import required libraries 
import torch 
  
# define a tensor 
input_tens = torch.tensor([[-0.9383, -1.4378, 0.5247], 
                           [0.8787,  0.2248, -1.3348], 
                           [1.3739,  1.3379, -0.2445]]) 
  
print("\n input tensor: \n", input_tens) 
  
# Define the Softmax function 
softmax = torch.nn.Softmax(dim=0) 
  
# Apply above defined Softmax function on  
# input tensor 
output = softmax(input_tens) 
  
# display tensor that containing Softmax values 
print("\n tensor that containing Softmax values: \n", output) 
  
# display sum 
print("\n sum  = ", output.sum()) 