# tf.norm(tensor, ord='euclidean', axis=None, keep_dims=False, name=None)

### tf.norm(tensor, ord='euclidean', axis=None, keep_dims=False, name=None)

See the guide: Math > Matrix Math Functions

Computes the norm of vectors, matrices, and tensors.

This function can compute 3 different matrix norms (Frobenius, 1-norm, and inf-norm) and up to 9218868437227405311 different vectors norms.

#### Args:

• tensor: Tensor of types float32, float64, complex64, complex128
• ord: Order of the norm. Supported values are 'fro', 'euclidean', 0, 1,2,np.infand any positive real number yielding the corresponding p-norm. Default is 'euclidean' which is equivalent to Frobenius norm iftensoris a matrix and equivalent to 2-norm for vectors. Some restrictions apply, a) The Frobenius normfrois not defined for vectors, b) If axis is a 2-tuple (matrix-norm), only 'euclidean', 'fro',1,np.infare supported. See the description ofaxis on how to compute norms for a batch of vectors or matrices stored in a tensor.
• axis: If axis is None (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(tensor, ord=ord) is equivalent to norm(reshape(tensor, [-1]), ord=ord). If axis is a Python integer, the input is considered a batch of vectors, and axist determines the axis in tensor over which to compute vector norms. If axis is a 2-tuple of Python integers it is considered a batch of matrices and axis determines the axes in tensor over which to compute a matrix norm. Negative indices are supported. Example: If you are passing a tensor that can be either a matrix or a batch of matrices at runtime, pass axis=[-2,-1] instead of axis=None to make sure that matrix norms are computed.
• keep_dims: If True, the axis indicated in axis are kept with size 1. Otherwise, the dimensions in axis are removed from the output shape.
• name: The name of the op.

#### Returns:

• output: A Tensor of the same type as tensor, containing the vector or matrix norms. If keep_dims is True then the rank of output is equal to the rank of tensor. Otherwise, if axis is none the output is a scalar, if axis is an integer, the rank of output is one less than the rank of tensor, if axis is a 2-tuple the rank of output is two less than the rank of tensor.

#### Raises:

• ValueError: If ord or axis is invalid.

#### numpy compatibility

Mostly equivalent to numpy.linalg.norm. Not supported: ord <= 0, 2-norm for matrices, nuclear norm. Other differences: a) If axis is None, treats the the flattened tensor` as a vector regardless of rank. b) Explicitly supports 'euclidean' norm as the default, including for higher order tensors.