Python–Tensorflow math . aggregate _ n()方法
原文:https://www . geesforgeks . org/python-tensorflow-math-grade _ n-method/
张量流math.accumulate_n()方法执行传递的张量列表的元素求和。结果是执行操作后的张量。运算是在 a 和 b 的表示上完成的，这种方法属于数学模块。
语法:
tf.math.accumulate_n( inputs, shape=None, tensor_dtype=None, name=None)论据
- 输入:该参数取 Tensor 对象的列表，每个对象的形状和类型相同。
- 形状:这是可选参数，它定义了输入元素的预期形状。
- 数据类型:这是可选参数，它定义了输入的预期数据类型。
- 名称:这是可选参数，这是操作的名称。
Return: 它返回一个与输入元素具有相同形状和类型的张量。
Let’s see this concept with the help of few examples:Example 1:
# Importing the Tensorflow library
import tensorflow as tf
# A constant a and b
a = tf.constant([[1, 3], [6, 7]])
b = tf.constant([[5, 2], [3, 8]])
# Applying the accumulate_n() function
# storing the result in 'c'
c = tf.math.accumulate_n([a, b, b])
# Initiating a Tensorflow session
with tf.Session() as sess:
print("Input 1", a)
print(sess.run(a))
print("Input 2", b)
print(sess.run(b))
print("Output: ", c)
print(sess.run(c))
输出:
Input 1 Tensor("Const_67:0", shape=(2, 2), dtype=int32)
[[1 3]
[6 7]]
Input 2 Tensor("Const_68:0", shape=(2, 2), dtype=int32)
[[5 2]
[3 8]]
Output: Tensor("AccumulateNV2_2:0", shape=(2, 2), dtype=int32)
[[11 7]
[12 23]]
例 2:
# Importing the Tensorflow library
import tensorflow as tf
# A constant a and b
a = tf.constant([[2, 4], [1, 3]])
b = tf.constant([[5, 3], [4, 6]])
# Applying the accumulate_n() function
# storing the result in 'c'
c = tf.math.accumulate_n([b, a, b], shape =[2, 2], tensor_dtype = tf.int32)
# Initiating a Tensorflow session
with tf.Session() as sess:
print("Input 1", a)
print(sess.run(a))
print("Input 2", b)
print(sess.run(b))
print("Output: ", c)
print(sess.run(c))
输出:
Input 1 Tensor("Const_73:0", shape=(2, 2), dtype=int32)
[[2 4]
[1 3]]
Input 2 Tensor("Const_74:0", shape=(2, 2), dtype=int32)
[[5 3]
[4 6]]
Output: Tensor("AccumulateNV2_5:0", shape=(2, 2), dtype=int32)
[[12 10]
[ 9 15]]
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