|TensorFlow 1 version||View source on GitHub|
tensor along dimension
axis using specified norm.
tf.linalg.normalize( tensor, ord='euclidean', axis=None, name=None )
tf.linalg.norm to compute the norm along
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).
ord: Order of the norm. Supported values are
np.infand any positive real number yielding the corresponding p-norm. Default is
'euclidean'which is equivalent to Frobenius norm if
tensoris 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
np.infare supported. See the description of
axison how to compute norms for a batch of vectors or matrices stored in a tensor.
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
axisis a Python integer, the input is considered a batch of vectors, and
axisdetermines the axis in
tensorover which to compute vector norms. If
axisis a 2-tuple of Python integers it is considered a batch of matrices and
axisdetermines the axes in
tensorover 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=Noneto make sure that matrix norms are computed.
name: The name of the op.
normalized: A normalized
Tensorwith the same shape as
norm: The computed norms with the same shape and dtype
tensorbut the final axis is 1 instead. Same as running
tf.cast(tf.linalg.norm(tensor, ord, axis keepdims=True), tensor.dtype).