Python| TensorFlow nn.sigmoid()
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 sigmoid函数，其定义为_0.png "由 QuickLaTeX.com 渲染")
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Sigmoid函数在 (0, 1) 范围内输出，它非常适合我们需要找到数据属于特定类别的概率的二元分类问题。 sigmoid函数在每一点都是可微的，它的导数是 .由于表达式涉及 sigmoid函数，因此可以重用其值以使反向传播更快。
Sigmoid函数存在“梯度消失”的问题，因为它在两端变平，导致在反向传播期间权重的变化非常小。这会使神经网络拒绝学习并陷入困境。由于这个原因，sigmoid函数的使用正在被其他非线性函数所取代，例如 Rectified Linear Unit (ReLU)。
函数tf.nn.sigmoid() [别名 tf.sigmoid] 为 Tensorflow 中的 sigmoid函数提供支持。
Syntax: tf.nn.sigmoid(x, name=None) or tf.sigmoid(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.
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 sigmoid function and
# storing the result in 'b'
b = tf.nn.sigmoid(a, name ='sigmoid')
# 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.nn.sigmoid(a, name ='sigmoid')
# 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.sigmoid")
plt.xlabel("X")
plt.ylabel("Y")
plt.show()输出：
Input type: Tensor("Const_1:0", shape=(6, ), dtype=float32)
Input: [ 1. -0.5 3.4000001 -2.0999999 0. -6.5 ]
Return type: Tensor("sigmoid:0", shape=(6, ), dtype=float32)
Output: [ 0.7310586 0.37754068 0.96770459 0.10909683 0.5 0.00150118]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.nn.sigmoid(a, name ='sigmoid')
# 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.sigmoid")
plt.xlabel("X")
plt.ylabel("Y")
plt.show()
输出：
Input: 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: [ 0.00669285 0.01357692 0.02734679 0.05431327 0.10500059 0.19332137
0.32865255 0.5 0.67134745 0.80667863 0.89499941 0.94568673
0.97265321 0.98642308 0.99330715]_2.png)