mindspore.ops.lrn
- mindspore.ops.lrn(x, depth_radius=5, bias=1.0, alpha=1.0, beta=0.5, norm_region='ACROSS_CHANNELS')[source]
Local Response Normalization.
Warning
lrn is deprecated on Ascend due to potential accuracy problem. It’s recommended to use other normalization methods, e.g.
mindspore.ops.batch_norm
.where the
indicates the specific value of the pixel corresponding to in feature map; where the indicates the depth_radius; where the indicates the bias; where the indicates the alpha; where the indicates the beta.- Parameters
depth_radius (int) – Half-width of the 1-D normalization window with the shape of 0-D. Default:
5
.bias (float) – An offset (usually positive to avoid dividing by 0). Default:
1.0
.alpha (float) – A scale factor, usually positive. Default:
1.0
.beta (float) – An exponent. Default:
0.5
.norm_region (str) – Specifies normalization region. Options:
"ACROSS_CHANNELS"
. Default:"ACROSS_CHANNELS"
.x (Tensor) – A 4-D Tensor with float16 or float32 data type.
- Returns
Tensor, with the same shape and data type as x.
- Raises
- Supported Platforms:
GPU
CPU
Examples
>>> import mindspore >>> import numpy as np >>> from mindspore import Tensor, ops >>> input_x = Tensor(np.array([[[[0.1], [0.2]], ... [[0.3], [0.4]]]]), mindspore.float32) >>> output = ops.lrn(input_x) >>> print(output) [[[[0.09534626] [0.1825742 ]] [[0.2860388 ] [0.3651484 ]]]]