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()) |
Output:
