python绘制神经网络中的Sigmoid和Tanh**函数图像(附代码)
python绘制神经网络中的Sigmoid和Tanh**函数图像(附代码)
最近在研究神经网络,用python绘制了一下常见的Sigmoid函数和Tanh函数,别的不多说,直接上代码:
- #!/usr/bin/python #encoding:utf-8
- import math
- import matplotlib.pyplot as plt
- import numpy as np
- import matplotlib as mpl
- mpl.rcParams['axes.unicode_minus']=False
- def sigmoid(x):
- return 1.0 / (1.0 + np.exp(-x))
- fig = plt.figure(figsize=(6,4))
- ax = fig.add_subplot(111)
- x = np.linspace(-10, 10)
- y = sigmoid(x)
- tanh = 2*sigmoid(2*x) - 1
- plt.xlim(-11,11)
- plt.ylim(-1.1,1.1)
- ax.spines['top'].set_color('none')
- ax.spines['right'].set_color('none')
- ax.xaxis.set_ticks_position('bottom')
- ax.spines['bottom'].set_position(('data',0))
- ax.set_xticks([-10,-5,0,5,10])
- ax.yaxis.set_ticks_position('left')
- ax.spines['left'].set_position(('data',0))
- ax.set_yticks([-1,-0.5,0.5,1])
- plt.plot(x,y,label="Sigmoid",color = "blue")
- plt.plot(2*x,tanh,label="Tanh", color = "red")
- plt.legend()
- plt.show()
最近在研究神经网络,用python绘制了一下常见的Sigmoid函数和Tanh函数,别的不多说,直接上代码:
- #!/usr/bin/python #encoding:utf-8
- import math
- import matplotlib.pyplot as plt
- import numpy as np
- import matplotlib as mpl
- mpl.rcParams['axes.unicode_minus']=False
- def sigmoid(x):
- return 1.0 / (1.0 + np.exp(-x))
- fig = plt.figure(figsize=(6,4))
- ax = fig.add_subplot(111)
- x = np.linspace(-10, 10)
- y = sigmoid(x)
- tanh = 2*sigmoid(2*x) - 1
- plt.xlim(-11,11)
- plt.ylim(-1.1,1.1)
- ax.spines['top'].set_color('none')
- ax.spines['right'].set_color('none')
- ax.xaxis.set_ticks_position('bottom')
- ax.spines['bottom'].set_position(('data',0))
- ax.set_xticks([-10,-5,0,5,10])
- ax.yaxis.set_ticks_position('left')
- ax.spines['left'].set_position(('data',0))
- ax.set_yticks([-1,-0.5,0.5,1])
- plt.plot(x,y,label="Sigmoid",color = "blue")
- plt.plot(2*x,tanh,label="Tanh", color = "red")
- plt.legend()
- plt.show()