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# PyTorch torch.linalg.norm Function * * PyTorch torch Reference Manual](#) `torch.linalg.norm` is a function in PyTorch's linear algebra module for calculating matrix or vector norms. It supports multiple norm types such as L1, L2, Frobenius norms, etc. ### Function Definition torch.linalg.norm(A, ord=None, dim=None, keepdim=False, out=None, dtype=None) **Parameters**: * `A` (Tensor): Input tensor. * `ord` (int, float, inf, -inf, optional): Norm type. Default is 'fro'. * `dim` (int, tuple, optional): Dimension(s) for norm calculation. * `keepdim` (bool, optional): Whether to retain dimensions. Default is False. * `dtype` (torch.dtype, optional): Output data type. **Returns**: * `torch.Tensor`: Returns the norm value. * * * ## Usage Examples ## Example - Frobenius Norm import torch # Create matrix A = torch.tensor([[1.0,2.0],[3.0,4.0]]) # Frobenius norm norm_fro = torch.linalg.norm(A) print("Matrix A:") print(A) print("Frobenius norm:", norm_fro) Output result: Matrix A: tensor([[1., 2.], [3., 4.]])Frobenius norm: tensor(5.4772) ## Example - Vector L2 Norm import torch # Create vector v = torch.tensor([3.0,4.0]) # L2 norm (default) norm_l2 = torch.linalg.norm(v) # L1 norm norm_l1 = torch.linalg.norm(v,ord=1) print("Vector v:", v) print("L2 norm:", norm_l2) print("L1 norm:", norm_l1) * * PyTorch torch Reference Manual](#)