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]]