Python| TensorFlow cos() 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.cos() [别名tf.math.cos ] 为 Tensorflow 中的余弦函数提供支持。它期望以弧度形式输入，输出在 [-1, 1] 范围内。输入类型是张量，如果输入包含多个元素，则计算元素余弦。

Syntax: tf.cos(x, name=None) or tf.math.cos(x, name=None)

Parameters:
x: A tensor of any of the following types: float16, float32, float64, complex64, or complex128.
name (optional): The name for the operation.

Return type: A tensor with the same type as that of x.

编程需要懂一点英语

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, -2.1, 0.0, -6.5], dtype = tf.float32)
  
# Applying the sin function and
# storing the result in 'b'
b = tf.cos(a, name ='cos')
  
# 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))

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 sigmoid function and
# storing the result in 'b'
b = tf.cos(a, name ='cos')
  
# 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.cos") 
    plt.xlabel("X") 
    plt.ylabel("Y") 
  
    plt.show()

输出：

Input type: Tensor("Const_2:0", shape=(6, ), dtype=float32)
Input: [ 1.        -0.5        3.4000001 -2.0999999  0.        -6.5      ]
Return type: Tensor("cos:0", shape=(6, ), dtype=float32)
Output: [ 0.54030228  0.87758255 -0.96679819 -0.50484604  1.          0.97658765]

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 sigmoid function and
# storing the result in 'b'
b = tf.cos(a, name ='cos')
  
# 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.cos") 
    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: [ 0.28366219 -0.41384591 -0.90903414 -0.9598162  -0.5413659   0.1417459
  0.75556135  1.          0.75556135  0.1417459  -0.5413659  -0.9598162
 -0.90903414 -0.41384591  0.28366219]