mindspore.ops.L2Normalize
- class mindspore.ops.L2Normalize(axis=0, epsilon=0.0001)[source]
L2 Normalization Operator.
This operator will normalize the input using the given axis. The function is shown as follows:
\[\displaylines{{\text{output} = \frac{x}{\sqrt{\text{max}( \sum_{i}^{}\left | x_i \right | ^2, \epsilon)}}}}\]where \(\epsilon\) is epsilon and \(\sum_{i}^{}\left | x_i \right | ^2\) calculate the sum of squares of the input x along the dimension axis.
Note
On Ascend, input data type of float64 is currently not supported.
- Parameters
- Inputs:
x (Tensor) - Input to compute the normalization. Tensor of shape \((N, *)\), where \(*\) means any number of additional dimensions. Data type must be float16, float32 or float64.
- Outputs:
Tensor, with the same type and shape as the x.
- Raises
TypeError – If axis is not one of the following: list, tuple or int.
TypeError – If epsilon is not a float.
TypeError – If x is not a Tensor.
TypeError – If dtype of x is not in [float16, float32, float64].
ValueError – If dimension of x is not greater than 0.
- Supported Platforms:
Ascend
GPU
CPU
Examples
>>> import mindspore >>> import numpy as np >>> from mindspore import Tensor, ops >>> l2_normalize = ops.L2Normalize() >>> x = Tensor(np.random.randint(-256, 256, (2, 3, 4)), mindspore.float32) >>> output = l2_normalize(x) >>> print(output.shape) (2, 3, 4)