Python| TensorFlow atan() 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.atan() [别名 tf.math.atan] 为 Tensorflow 中的反正切函数提供支持。它以弧度形式给出输出。输入类型是张量，如果输入包含多个元素，则计算元素的反正切。

Syntax: tf.atan(x, name=None) or tf.math.atan(x, name=None)
Parameters: 
x: A tensor of any of the following types: bfloat16, half, float32, float64, int32, int64, complex64, or complex128. 
name (optional): The name for the operation.
Return type: A tensor with the same type as that of x. 
 

代码#1：

Python3

# Importing the Tensorflow library
import tensorflow as tf
 
# A constant vector of size 6
a = tf.constant([1.0, -0.5, 3.4, 0.2, 0.0, -2],
                           dtype = tf.float32)
 
# Applying the atan function and
# storing the result in 'b'
b = tf.atan(a, name ='atan')
 
# 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_8:0", shape=(6, ), dtype=float32)
Input: [ 1.  -0.5  3.4  0.2  0.  -2. ]
Return type: Tensor("atan:0", shape=(6, ), dtype=float32)
Output: [ 0.7853982  -0.4636476   1.2847449   0.19739556  0.         -1.1071488 ]

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 15 with values from -5 to 5
a = np.linspace(-5, 5, 15)
 
# Applying the inverse tangent function and
# storing the result in 'b'
b = tf.atan(a, name ='atan')
 
# 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.atan")
    plt.xlabel("X")
    plt.ylabel("Y")
 
    plt.show()

输出：

Input: [-5.         -4.28571429 -3.57142857 -2.85714286 -2.14285714 -1.42857143
 -0.71428571  0.          0.71428571  1.42857143  2.14285714  2.85714286
  3.57142857  4.28571429  5.        ]
Output: [-1.37340077 -1.34156439 -1.29778762 -1.23412151 -1.13416917 -0.96007036
 -0.62024949  0.          0.62024949  0.96007036  1.13416917  1.23412151
  1.29778762  1.34156439  1.37340077]