Python – tensorflow.math.xlog1py()

TensorFlow是谷歌设计的开源Python库，用于开发机器学习模型和深度学习神经网络。

xlog1py()是用来计算元素明智的x * log1p(y)。

语法: tensorflow.math.xlog1py(x, y, name)

参数:

• x:它是一个张量。允许的d类型是bfloat16, half, float32, float64, complex64, complex128。
• y:它是一个张量。下面的dtypes是bfloat16, half, float32, float64, complex64, complex128。
• name（可选）:它定义了该操作的名称。

返回：它返回一个张量。

示例 1:

# importing the library
import tensorflow as tf
  
# Initializing the input tensor
a = tf.constant([ -5, -7, 2, 0, 7], dtype = tf.float64)
b = tf.constant([ 1, 3, 9, 4, 7], dtype = tf.float64)
  
# Printing the input tensor
print('a: ', a)
print('b: ', b)
  
# Calculating result
res = tf.math.xlog1py(a, b)
  
# Printing the result
print('Result: ', res)

输出:

a:  tf.Tensor([-5. -7.  2.  0.  7.], shape=(5, ), dtype=float64)
b:  tf.Tensor([1. 3. 9. 4. 7.], shape=(5, ), dtype=float64)
Result:  tf.Tensor([-3.4657359  -9.70406053  4.60517019  0.         14.55609079], shape=(5, ), dtype=float64)

示例 2:

# importing the library
import tensorflow as tf
import numpy as np
  
# Initializing the input tensor
a = tf.constant([ -5 + 2j, -7-5j, 2 + 2j, 5-3j, 7 + 6j], dtype = tf.complex128)
b = tf.constant([ 0 + 0j, 3-1j, 9 + 5j, 4-3j, -6-8j], dtype = tf.complex128)
  
# Printing the input tensor
print('a: ', a)
print('b: ', b)
  
# Calculating result
res = tf.math.xlog1py(a, b)
  
# Printing the result
print('Result: ', res)

输出:

a:  tf.Tensor([-5.+2.j -7.-5.j  2.+2.j  5.-3.j  7.+6.j], shape=(5, ), dtype=complex128)
b:  tf.Tensor([ 0.+0.j  3.-1.j  9.+5.j  4.-3.j -6.-8.j], shape=(5, ), dtype=complex128)
Result:  tf.Tensor(
[ -0.        +0.j         -11.14114002-5.36818272j
   3.90101852+5.75560896j   7.19464281-7.99163829j
  28.48660115-1.43986039j], shape=(5, ), dtype=complex128)