Function Differences with tf.math.cumsum
tf.math.cumsum
tf.math.cumsum(x, axis=0, exclusive=False, reverse=False, name=None) -> Tensor
For more information, see tf.math.cumsum.
mindspore.ops.cumsum
mindspore.ops.cumsum(x, axis, dtype=None) -> Tensor
For more information, see mindspore.ops.cumsum.
Differences
TensorFlow: Calculates the cumulative sum of the input Tensor on the specified axis.
MindSpore: MindSpore API basically implements the same function as TensorFlow, and there are differences in parameter settings.
Categories |
Subcategories |
TensorFlow |
MindSpore |
Differences |
---|---|---|---|---|
Parameters |
Parameter 1 |
x |
x |
- |
Parameter 2 |
axis |
axis |
MindSpore has no default value and can specify dimensions |
|
Parameter 3 |
exclusive |
- |
MindSpore does not have this parameter |
|
Parameter 4 |
reverse |
- |
MindSpore does not have this parameter |
|
Parameter 5 |
name |
- |
Not involved |
|
Parameter 6 |
- |
dtype |
Setting the output data type in MindSpore |
Code Example 1
The same input tensor, with axis -1, accumulates the innermost layer of the input tensor from left to right, and the two APIs achieve the same function.
# TensorFlow
import tensorflow as tf
a = tf.constant([[3, 4, 6, 10], [1, 6, 7, 9], [4, 3, 8, 7], [1, 3, 7, 9]])
y = tf.cumsum(a, -1)
print(y.numpy())
# [[ 3 7 13 23]
# [ 1 7 14 23]
# [ 4 7 15 22]
# [ 1 4 11 20]]
# MindSpore
from mindspore import Tensor
import mindspore.ops as ops
import numpy as np
x = Tensor(np.array([[3, 4, 6, 10], [1, 6, 7, 9], [4, 3, 8, 7], [1, 3, 7, 9]]))
y = ops.cumsum(x, -1)
print(y)
# [[ 3 7 13 23]
# [ 1 7 14 23]
# [ 4 7 15 22]
# [ 1 4 11 20]]