[Term Entry] PyTorch Tensor Operations: .log2()

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* [Term Entry] PyTorch Tensor Operations: .log2()

* [Term Entry] PyTorch Tensor Operations: .log2()

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Author: mamtawardhani

content/pytorch/concepts/tensor-operations/terms/log2/log2.md

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+---
+Title: '.log2()'
+Description: 'Computes the base-2 logarithm of each element in the input tensor and returns a new tensor with the results.'
+Subjects:
+  - 'Code Foundations'
+  - 'Computer Science'
+Tags:
+  - 'Elements'
+  - 'Methods'
+  - 'PyTorch'
+  - 'Tensors'
+CatalogContent:
+  - 'learn-python-3'
+  - 'paths/data-science'
+---
+
+The **`.log2()`** method in PyTorch returns a new [tensor](https://www.codecademy.com/resources/docs/pytorch/tensors) 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
+
+```pseudo
+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:
+
+```py
+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)
+```
+
+The output of this code is:
+
+```shell
+tensor([1., 2., 3., 4., 5.])
+```
+
+## 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:
+
+```py
+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)
+```
+
+The output of this code is:
+
+```shell
+tensor([10.5500,  9.2777, 10.9371,  1.3551,  5.2609])
+tensor([3.3992, 3.2138, 3.4512, 0.4384, 2.3953])
+```