Function Differences with tf.raw_ops.LRN

tf.raw_ops.LRN

tf.raw_ops.LRN(input, depth_radius=5, bias=1, alpha=1, beta=0.5, name=None) -> Tensor

For more information, see tf.raw_ops.LRN.

mindspore.ops.LRN

mindspore.ops.LRN(depth_radius=5, bias=1.0, alpha=1.0, beta=0.5, norm_region="ACROSS_CHANNELS")(x) -> Tensor

For more information, see mindspore.ops.LRN.

Differences

TensorFlow: Performs a local response normalization operation, and returns a Tensor with the same type as the input.

MindSpore: MindSpore API implements the same functions as TensorFlow, with different parameter names and one more parameter specifying the normalized region norm_region.

Categories	Subcategories	TensorFlow	MindSpore	Differences
Parameters	Parameter 1	input	x	Same function, different parameter names
	Parameter 2	depth_radius	depth_radius	-
	Parameter 3	bias	bias	-
	Parameter 4	alpha	alpha	-
	Parameter 5	beta	beta	-
	Parameter 6	-	norm_region	Specify the normalized region. TensorFlow does not have this parameter
	Parameter 7	name	-	Not Involved

Code Example 1

The outputs of MindSpore and TensorFlow are consistent.

# TensorFlow
import tensorflow as tf
import numpy as np

input_x = tf.constant(np.array([[[[0.1], [0.2]],[[0.3], [0.4]]]]), dtype=tf.float32)

output = tf.raw_ops.LRN(input=input_x, depth_radius=1, bias=0.00001, alpha=0.0000001, beta=0.00001)
print(output.numpy())
# [[[[0.10001152]
#     [0.2002304]]
#     [[0.3003455]
#     [0.40004608]]]]

# MindSpore
import mindspore
from mindspore import Tensor
import mindspore.ops.operations as ops
import numpy as np

input_x = Tensor(np.array([[[[0.1], [0.2]],[[0.3], [0.4]]]]), mindspore.float32)
lrn = ops.LRN(depth_radius=1, bias=0.00001, alpha=0.0000001, beta=0.00001)
output = lrn(input_x)
print(output)
# [[[[0.10001152]
#     [0.2002304]]
#     [[0.3003455]
#     [0.40004608]]]]