Function Differences with tf.math.divide
tf.math.divide
tf.math.divide(x, y, name=None) -> Tensor
For more information, see tf.math.divide.
mindspore.ops.div
mindspore.ops.div(input, other, rounding_mode=None) -> Tensor
For more information, see mindspore.ops.div.
Differences
TensorFlow: The quotient is obtained by dividing two Tensors element-wise.
MindSpore: When the parameter rounding_mode of MindSpore API is None by default, MindSpore implements the same function as TensorFlow.
Categories |
Subcategories |
TensorFlow |
MindSpore |
Differences |
---|---|---|---|---|
Parameters |
Parameter 1 |
x |
input |
Same function, different parameter names |
Parameter 2 |
y |
other |
Same function, different parameter names |
|
Parameter 3 |
- |
rounding_mode |
This parameter is not available in TensorFlow. MindSpore is an optional parameter that determines the rounding type of the result, and the default value is None |
|
Parameter 4 |
name |
- |
Not involved |
Code Example
When the parameter rounding_mode of MindSpore is not specified, the two APIs achieve the same function and have the same usage.
# TensorFlow
import tensorflow as tf
import numpy
x = tf.constant([[2, 4, 6, 8], [1, 2, 3, 4]])
y = tf.constant([5, 8, 8, 16])
out = tf.math.divide(x, y).numpy()
print(out)
# [[0.4 0.5 0.75 0.5 ]
# [0.2 0.25 0.375 0.25 ]]
# MindSpore
import mindspore
from mindspore import Tensor
import mindspore.ops as ops
import numpy as np
x_ = np.array([[2, 4, 6, 8], [1, 2, 3, 4]])
y_ = np.array([5, 8, 8, 16])
x = Tensor(x_, mindspore.float64)
y = Tensor(y_, mindspore.float64)
output = ops.div(x, y)
print(output)
# [[0.4 0.5 0.75 0.5 ]
# [0.2 0.25 0.375 0.25 ]]