python实现简单神经网络算法
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2020-09-20
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python实现简单神经网络算法实现简单神经网络算法
主要为大家详细介绍了python实现简单神经网络算法，具有一定的参考价值，感兴趣的小伙伴们可以参考一下
python实现简单神经网络算法，供大家参考，具体内容如下
python实现二层神经网络实现二层神经网络
包括输入层和输出层
import numpy as np
#sigmoid function
def nonlin(x, deriv = False):
if(deriv == True):
return x*(1-x)
return 1/(1+np.exp(-x))
#input dataset
x = np.array([[0,0,1],
[0,1,1],
[1,0,1],
[1,1,1]])
#output dataset
y = np.array([[0,0,1,1]]).T
np.random.seed(1)
#init weight value
syn0 = 2*np.random.random((3,1))-1
for iter in xrange(100000):
l0 = x #the first layer,and the input layer
l1 = nonlin(np.dot(l0,syn0)) #the second layer,and the output layer
l1_error = y-l1
l1_delta = l1_error*nonlin(l1,True)
syn0 += np.dot(l0.T, l1_delta)
print "outout after Training:"
print l1
import numpy as np
#sigmoid function
def nonlin(x, deriv = False):
if(deriv == True):
return x*(1-x)
return 1/(1+np.exp(-x))
#input dataset
x = np.array([[0,0,1],
[0,1,1],
[1,0,1],
[1,1,1]])
#output dataset
y = np.array([[0,0,1,1]]).T
np.random.seed(1)
#init weight value
syn0 = 2*np.random.random((3,1))-1
for iter in xrange(100000):
l0 = x #the first layer,and the input layer
l1 = nonlin(np.dot(l0,syn0)) #the second layer,and the output layer
l1_error = y-l1
l1_delta = l1_error*nonlin(l1,True)
syn0 += np.dot(l0.T, l1_delta)
print "outout after Training:"
print l1
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