# .norm()

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Published Apr 7, 2024
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In NumPy, the `.norm()` function computes the norm of a matrix, either across the entire array or along a specified axis. It helps identify differences between matrices, pinpoint predictive errors, manage model complexity, and validate numerical algorithms.

## Syntax

``````numpy.linalg.norm(a, ord=None, axis=None, keepdims=False)
``````
• `a`: The input array for which the norm is computed.
• `ord=None` (Optional): Specifies the order of the norm to compute. The default is `None`, which computes the Frobenius norm for matrices and 2-norm for vectors.
• `axis=None` (Optional): Specifies the axis or axes along which to compute the norm. The default is `None`, which computes the norm over the entire array.
• `keepdims=False` (Optional): Specifies whether to keep the dimensions of the original array in the result. The default is `False`.

## Example

The following example demonstrates a straightforward usage of the `.norm()` function to compute the Frobenius norm of a matrix:

```import numpy as np
matrix = np.array([[1, 2], [3, 4]])
frobenius_norm = np.linalg.norm(matrix)
print("Frobenius norm of the matrix:", frobenius_norm)
```

This produces the following output:

```Frobenius norm of the matrix: 5.477225575051661
```

## Codebyte Example

The following example computes different norms for a vector using the `.norm()` function with various values of the `ord` parameter, including 1-norm (Manhattan), 2-norm (Euclidean), and infinity norm:

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