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.
|
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TensorFlow 1 version
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