Python| TensorFlow log() 方法

Tensorflow 是谷歌开发的开源机器学习库。它的应用之一是开发深度神经网络。
模块tensorflow.math为许多基本的数学运算提供支持。函数tf.log() [别名 tf.math.log] 为 Tensorflow 中的自然对数函数提供支持。它期望复数形式的输入为$$a+bi$$或浮点数。输入类型是张量，如果输入包含多个元素，则计算元素对数， $y=\log_e x}$$ .

Syntax: tf.log(x, name=None) or tf.math.log(x, name=None)
Parameters: 
x: A Tensor of type bfloat16, half, float32, float64, complex64 or complex128. 
name (optional): The name for the operation.
Return type: A Tensor with the same size and type as that of x. 
 

代码#1：

Python3

# Importing the Tensorflow library
import tensorflow as tf
 
# A constant vector of size 5
a = tf.constant([-0.5, -0.1, 0, 0.1, 0.5], dtype = tf.float32)
 
# Applying the log function and
# storing the result in 'b'
b = tf.log(a, name ='log')
 
# Initiating a Tensorflow session
with tf.Session() as sess:
    print('Input type:', a)
    print('Input:', sess.run(a))
    print('Return type:', b)
    print('Output:', sess.run(b))

输出：

Input type: Tensor("Const:0", shape=(5, ), dtype=float32)
Input: [-0.5 -0.1  0.   0.1  0.5]
Return type: Tensor("log:0", shape=(5, ), dtype=float32)
Output: [       nan        nan       -inf -2.3025851 -0.6931472]

$$ nan $$表示负值不存在自然对数，并且$$ -inf $$表示当输入接近零时它接近负无穷大。
代码 #2：可视化

Python3

# Importing the Tensorflow library
import tensorflow as tf
 
# Importing the NumPy library
import numpy as np
 
# Importing the matplotlib.pyplot function
import matplotlib.pyplot as plt
 
# A vector of size 20 with values from 0 to 1 and 1 to 10
a = np.append(np.linspace(0, 1, 10), np.linspace(1, 10, 10))
 
# Applying the logarithmic function and
# storing the result in 'b'
b = tf.log(a, name ='log')
 
# Initiating a Tensorflow session
with tf.Session() as sess:
    print('Input:', a)
    print('Output:', sess.run(b))
    plt.plot(a, sess.run(b), color = 'red', marker = "o")
    plt.title("tensorflow.abs")
    plt.xlabel("X")
    plt.ylabel("Y")
    plt.grid()
 
    plt.show()

输出：

Input: [ 0.          0.11111111  0.22222222  0.33333333  0.44444444  0.55555556
  0.66666667  0.77777778  0.88888889  1.          1.          2.
  3.          4.          5.          6.          7.          8.
  9.         10.        ]
Output: [       -inf -2.19722458 -1.5040774  -1.09861229 -0.81093022 -0.58778666
 -0.40546511 -0.25131443 -0.11778304  0.          0.          0.69314718
  1.09861229  1.38629436  1.60943791  1.79175947  1.94591015  2.07944154
  2.19722458  2.30258509]