Python| TensorFlow nn.tanh()

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.nn为许多基本的神经网络操作提供支持。

许多激活函数之一是双曲正切函数（也称为 tanh），其定义为 $tanh(x) = (e^z - e^{-z}) / (e^z + e^{-z})$ .
双曲正切函数在 (-1, 1) 范围内输出，因此将强烈的负输入映射到负值。与 sigmoid函数不同，只有接近零的值被映射到接近零的输出，这在一定程度上解决了“梯度消失”问题。双曲正切函数在每一点都是可微的，它的导数为 $1 - tanh^2(x)$ .由于表达式涉及 tanh函数，因此可以重用其值以使反向传播更快。

尽管与 sigmoid函数相比，网络“卡住”的可能性较低，但双曲正切函数仍然存在“梯度消失”的问题。整流线性单元 (ReLU) 可以用来克服这个问题。

函数tf.nn.tanh() [别名 tf.tanh] 为 Tensorflow 中的双曲正切函数提供支持。

Syntax: tf.nn.tanh(x, name=None) or tf.tanh(x, name=None)
Parameters:
x: A tensor of any of the following types: float16, float32, double, complex64, or complex128.
name (optional): The name for the operation.
Return : 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 tanh function and
# storing the result in 'b'
b = tf.nn.tanh(a, name ='tanh')
 
# 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 tanh function and
# storing the result in 'b'
b = tf.nn.tanh(a, name ='tanh')
 
# 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.nn.tanh")
    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("tanh_2:0", shape=(6, ), dtype=float32)
Output: [ 0.76159418 -0.46211717  0.9977749  -0.97045201  0.         -0.99999547]

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 tanh function and
# storing the result in 'b'
b = tf.nn.tanh(a, name ='tanh')
 
# 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.nn.tanh")
    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: [-0.9999092  -0.99962119 -0.99842027 -0.99342468 -0.97284617 -0.89137347
 -0.61335726  0.          0.61335726  0.89137347  0.97284617  0.99342468
  0.99842027  0.99962119  0.9999092 ]