Python – tensorflow.math.cumsum()

TensorFlow 是由 Google 设计的开源Python库，用于开发机器学习模型和深度学习神经网络。 cumsum()用于计算输入张量的累积和。

Syntax: tensorflow.math.cumsum(x, axis, exclusive, reverse, name)

Parameters: 

• x: It’s the input tensor. Allowed dtype for this tensor are  float32, float64, int64, int32, uint8, uint16, int16, int8, complex64, complex128, qint8, quint8, qint32, half.
• axis(optional): It’s a tensor of type int32. It’s value should  be in the range  A Tensor of type int32 (default: 0). Must be in the range [-rank(x), rank(x)).  Default value is 0.
• exclusive(optional): It’s of type bool. Default value is False and if set to true then the output for input [a, b, c] will be [0, a, a+b].
• reverse(optional): It’s of type bool. Default value is False and if set to true then the output for input [a, b, c] will be [a+b+c, a+b, a].
• name(optional): It’s defines the name for the operation.

Returns: It returns a tensor of same dtype as x.

示例 1：

Python3

# importing the library
import tensorflow as tf
 
# initializing the input
a = tf.constant([1, 2, 4, 5], dtype = tf.int32) 
 
# Printing the input
print("Input: ",a)
 
# Cumulative sum
res  = tf.math.cumsum(a)
 
# Printing the result
print("Output: ",res)

输出：

Input:  tf.Tensor([1 2 4 5], shape=(4,), dtype=int32)
Output:  tf.Tensor([ 1  3  7 12], shape=(4,), dtype=int32)

示例 2：在此示例中，反向和排他都设置为 True。

Python3

# importing the library
import tensorflow as tf
 
# initializing the input
a = tf.constant([2, 3, 4, 5], dtype = tf.int32) 
 
# Printing the input
print("Input: ",a)
 
# Cumulative sum
res  = tf.math.cumsum(a, reverse = True, exclusive = True)
 
# Printing the result
print("Output: ",res)

输出：

Input:  tf.Tensor([2 3 4 5], shape=(4,), dtype=int32)
Output:  tf.Tensor([12  9  5  0], shape=(4,), dtype=int32)