神经网络学习之----Hopfield神经网络（代码实现）

思路：

　　定义三个训练测试图片0 1 2（16*8），即三个吸引子。然后创建一个Hopfield神经网络，把训练数据输入。然后在用测试数据输入测试结果。

import numpy as np
import neurolab as nl
import matplotlib.pyplot as plt# 0 1 2-----------16*8
target =  np.array([[0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,1,0,0,1,0,0,0,1,0,0,0,0,1,0,0,1,0,0,0,0,1,0,0,1,0,0,0,0,1,0,0,1,0,0,0,0,1,0,0,1,0,0,0,0,1,0,0,1,0,0,0,0,1,0,0,1,0,0,0,0,1,0,0,1,0,0,0,0,1,0,0,1,0,0,0,0,1,0,0,1,0,0,0,0,1,0,0,0,1,0,0,1,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,1,1,0,0,0,0,0,0,0,0,0,0],   [0,0,0,0,0,0,0,0,0,0,1,1,1,1,0,0,0,1,1,0,0,1,1,0,0,1,0,0,0,0,1,0,0,1,0,0,0,0,1,0,0,1,0,0,0,0,1,0,0,0,0,0,0,1,1,0,0,0,0,0,1,1,0,0,0,0,0,1,1,0,0,0,0,0,1,1,0,0,0,0,0,1,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,1,0,0,0,0,1,0,0,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0]])#画图函数
def visualized (data, title): fig, ax = plt.subplots()ax.imshow(data, cmap=plt.cm.gray, interpolation='nearest')ax.set_title(title)plt.show()#显示012
for i in range(len(target)):visualized(np.reshape(target[i], (16,8)), i)# In[2]:#hopfield网络的值是1和-1
target[target == 0] = -1#创建一个hopfield神经网络，吸引子为target(012)
net = nl.net.newhop(target)#定义3个测试数据
test_data1 =np.asfarray([0,0,0,0,0,0,0,0,0,0,0,1,1,0,1,0,0,0,1,0,0,1,0,0,0,1,0,0,0,0,1,0,0,1,0,0,1,0,1,0,0,1,0,0,0,0,1,0,0,1,0,0,0,0,1,0,0,1,0,1,0,0,1,0,0,1,0,0,0,0,1,0,0,1,0,0,1,0,1,0,0,1,0,0,0,0,1,0,0,1,0,0,0,0,1,0,0,1,0,1,0,0,1,0,0,0,1,0,0,1,0,0,0,0,1,1,1,0,0,0,0,0,0,0,0,0,0,0])test_data2 =np.asfarray([0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,1,0,0,0,0,0,0,0,1,0,0,1,0,0,0,1,1,0,1,0,0,1,0,0,1,0,1,0,0,0,0,0,1,0,0,0,0,0,1,0,1,0,1,0,0,0,0,1,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,1,0,0,1,0,1,0,0,0,0,0,0,1,1,1,0,0,0,1,0,0,0,0,0,0])test_data3 =np.asfarray([0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,1,0,0,0,0,0,0,1,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,1,0,0,1,0,0,1,0,0,1,0,1,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,1,0,0,0,0,1,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,1,1,0,0,0,1,0,0,0,0,0,0])#显示测试数据
visualized(np.reshape(test_data1, (16,8)), "test_data1")
visualized(np.reshape(test_data2, (16,8)), "test_data2")
visualized(np.reshape(test_data3, (16,8)), "test_data3")# In[3]:

test_data1[test_data1==0] = -1
#把测试数据输入hopfield网络，得到输出
out1 = net.sim([test_data1])
#判断测试数据的数字是多少
for i in range(len(target)):if((out1 == target[i]).all()):print("test_data is :",i)
#显示输出
visualized(np.reshape(out1, (16,8)), "output1")        test_data2[test_data2==0] = -1
#把测试数据输入hopfield网络，得到输出
out2 = net.sim([test_data2])
#判断测试数据的数字是多少
for i in range(len(target)):if((out2 == target[i]).all()):print("test_data is :",i)
#显示输出
visualized(np.reshape(out2, (16,8)), "output2")        test_data3[test_data3==0] = -1
#把测试数据输入hopfield网络，得到输出
out3 = net.sim([test_data3])
#判断测试数据的数字是多少
for i in range(len(target)):if((out3 == target[i]).all()):print("test_data is :",i)
#显示输出
visualized(np.reshape(out3, (16,8)), "output3")

转载于:https://www.cnblogs.com/mengqimoli/p/11103733.html

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