PyTorch .log2()

The .log2() method in PyTorch returns a new tensor by computing the logarithm base 2 of each element in the input tensor. This operation is useful in numerous data-science and machine-learning workflows where values are interpreted on a log scale (e.g., information theory, binary magnitude comparisons).

Syntax

torch.log2(input, *, out=None) → Tensor

Parameters:

• input (Tensor): The tensor whose elements are to be transformed by base-2 logarithm.
• out (Tensor, optional): A tensor to store the output; must have the same shape as input if provided.

Return value:

Returns a new tensor of the same shape as input where each element is log₂(input[i]).

Example 1: Basic Usage of .log2()

In this example, the base-2 logarithm is computed for a tensor containing powers of 2:

import torch

# Define a tensor
input_tensor = torch.tensor([2.0, 4.0, 8.0, 16.0, 32.0])

# Compute base-2 logarithm
output_tensor = torch.log2(input_tensor)

print(output_tensor)

Copy to clipboard
Copy to clipboard

The output of this code is:

tensor([1., 2., 3., 4., 5.])

Copy to clipboard
Copy to clipboard

Example 2: Applying .log2() on Random Values

In this example, a tensor with random positive values is transformed using base-2 logarithm to analyze data on a log scale:

import torch

# Generate a tensor of random positive values
data = torch.rand(5) * 10 + 1

# Apply log2 transformation
log_tensor = torch.log2(data)

print(data)
print(log_tensor)

Copy to clipboard
Copy to clipboard

The output of this code is:

tensor([10.5500,  9.2777, 10.9371,  1.3551,  5.2609])
tensor([3.3992, 3.2138, 3.4512, 0.4384, 2.3953])

Copy to clipboard
Copy to clipboard