tf.linalg.normalize

TensorFlow 1 version View source on GitHub

Normalizes tensor along dimension axis using specified norm.

tf.linalg.normalize(
    tensor,
    ord='euclidean',
    axis=None,
    name=None
)

This uses tf.linalg.norm to compute the norm along axis.

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, 2-norm and inf-norm).

Args:

  • tensor: Tensor of types float32, float64, complex64, complex128
  • ord: Order of the norm. Supported values are 'fro', 'euclidean', 1, 2, np.inf and any positive real number yielding the corresponding p-norm. Default is 'euclidean' which is equivalent to Frobenius norm if tensor is a matrix and equivalent to 2-norm for vectors. Some restrictions apply: a) The Frobenius norm 'fro' is not defined for vectors, b) If axis is a 2-tuple (matrix norm), only 'euclidean', 'fro', 1, 2, np.inf are supported. See the description of axis 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 axis 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.
  • name: The name of the op.

Returns:

  • normalized: A normalized Tensor with the same shape as tensor.
  • norm: The computed norms with the same shape and dtype tensor but the final axis is 1 instead. Same as running tf.cast(tf.linalg.norm(tensor, ord, axis keepdims=True), tensor.dtype).

Raises:

  • ValueError: If ord or axis is invalid.

Compat aliases