Function norm

  • norm(x, ord?, axis?, keepDims?): Tensor

  • Computes the norm of scalar, vectors, and matrices. This function can compute several different vector norms (the 1-norm, the Euclidean or 2-norm, the inf-norm, and in general the p-norm for p > 0) and matrix norms (Frobenius, 1-norm, and inf-norm).

    const x = tf.tensor1d([1, 2, 3, 4]);

    x.norm().print();  // or tf.norm(x)

    Parameters

    • x: Tensor<Rank> | TensorLike

      The input array.

    • Optional ord: number | "euclidean" | "fro"

      Optional. Order of the norm. Supported norm types are following:

      ord norm for matrices norm for vectors
      'euclidean' Frobenius norm 2-norm
      'fro' Frobenius norm
      Infinity max(sum(abs(x), axis=1)) max(abs(x))
      -Infinity min(sum(abs(x), axis=1)) min(abs(x))
      1 max(sum(abs(x), axis=0)) sum(abs(x))
      2 sum(abs(x)^2)^(1/2)
    • Optional axis: number | number[]

      Optional. If axis is null (the default), the input is considered a vector and a single vector norm is computed over the entire set of values in the Tensor, i.e. norm(x, ord) is equivalent to norm(x.reshape([-1]), ord). If axis is an integer, the input is considered a batch of vectors, and axis determines the axis in x over which to compute vector norms. If axis is a 2-tuple of integer it is considered a batch of matrices and axis determines the axes in NDArray over which to compute a matrix norm.

    • Optional keepDims: boolean

      Optional. If true, the norm has the same dimensionality as the input.

    Returns Tensor

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