# computeNorm(input:output:axes:) Computes the Euclidean norm and writes the result to the output tensor. ``` static func computeNorm(input: BNNSNDArrayDescriptor, output: BNNSNDArrayDescriptor, axes: [Int]? = nil) throws ``` ## Parameters `input` The descriptor of the input. `output` The descriptor of the output. `axes` The indices of the axes over which the function computes the norm. Set to `nil` to specify that the function computes the norm over the entire tensor. ## Discussion Use this function to compute the norm of either an entire tensor or an axis or axes of a tensor. For example, the following code defines a 3D tensor: ```swift let inputValues: [Float] = [1, 2, 3, 4, 5, 6, 10, 20, 30, 40, 50, 60, 100, 200, 300, 400, 500, 600, 1000, 2000, 3000, 4000, 5000, 6000] let input = BNNSNDArrayDescriptor.allocate( initializingFrom: inputValues, shape: .imageCHW(3, 2, 4)) ``` Define the `axes` parameter as either `[0, 1, 2]` or `nil` to specify that the operation computes the norm of the entire tensor. In this case, the norm is a scalar value, and the destination’s data layout must be a [`BNNS.DataLayout.vector`](/documentation/Accelerate/BNNS/DataLayout/vector) with a size of `1`. ```swift let output = BNNSNDArrayDescriptor.allocateUninitialized( scalarType: Float.self, shape: .vector(1)) try? BNNS.computeNorm(input: input, output: output, axes: [0, 1, 2]) // Prints `[9587.45]`. print(output.makeArray(of: Float.self)!) ``` On return, the output descriptor contains a single value that is the square root of the sum of squares of each element in the tensor:  Specify an `axes` of `[2]` to compute the norms along the second axis. In this case, the destination must be a matrix with a size that matches the zeroth and first dimensions of the source tensor: ```swift let output = BNNSNDArrayDescriptor.allocateUninitialized( scalarType: Float.self, shape: .matrixColumnMajor(3, 2)) try? BNNS.computeNorm(input: input, output: output, axes: [2]) // Prints // [1005.0378, 2010.075, 3015.113, // 4020.1511, 5025.189, 6030.227] print(output.makeArray(of: Float.self)!) ``` On return, the output descriptor contains six values that are the norms of the slices along the second axis of the input tensor:  To compute the norm along more that one dimension, define the destination tensor with a size of the dimensions you’re not calculating over. For example, the following code defines an `axes` with a value of `[0, 2]` to compute the norm of dimensions zero and two: ```swift let output = BNNSNDArrayDescriptor.allocateUninitialized( scalarType: Float.self, shape: .vector(2)) try? BNNS.computeNorm(input: input, output: output, axes: [0, 2]) // Prints `[3760.507, 8819.171]` print(output.makeArray(of: Float.self)!) ``` On return, the output descriptor contains two values that are the norms of the top and bottom slices of the input tensor:  --- Copyright © 2026 Apple Inc. All rights reserved. | [Terms of Use](https://www.apple.com/legal/internet-services/terms/site.html) | [Privacy Policy](https://www.apple.com/privacy/privacy-policy)