tf.linalg.normalize | TensorFlow v2.16.1 (original) (raw)
tf.linalg.normalize
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Normalizes tensor along dimension axis using specified norm.
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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 iftensor 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 tonorm(reshape(tensor, [-1]), ord=ord). If axis is a Python integer, the input is considered a batch of vectors, and axis determines the axis intensor 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, passaxis=[-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 runningtf.cast(tf.linalg.norm(tensor, ord, axis keepdims=True), tensor.dtype). |
| Raises | |
|---|---|
| ValueError | If ord or axis is invalid. |