hopfield神经网络不能达到预期
#!/usr/bin/env python# coding: utf-8
# In:
import numpy as np
import neurolab as nl
import matplotlib.pyplot as plt
# In:
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,
0,0,1,0,1,0,0,0,
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,
0,0,1,1,1,1,1,0,
0,0,0,0,0,0,0,0],
[0,0,0,0,0,0,0,0,
0,0,1,1,1,1,0,0,
0,1,0,0,0,0,1,0,
0,1,0,0,0,0,1,0,
0,0,0,0,0,0,1,0,
0,0,0,0,0,0,1,0,
0,0,0,0,0,0,1,0,
0,0,0,0,0,1,0,0,
0,0,0,0,1,0,0,0,
0,0,0,1,0,0,0,0,
0,0,1,0,0,0,0,0,
0,1,0,0,0,0,0,0,
0,1,0,0,0,0,0,0,
0,1,0,0,0,0,0,0,
0,1,1,1,1,1,1,0,
0,0,0,0,0,0,0,0]
])
# In:
def visualized(data,title):
fig,ax = plt.subplots()
ax.imshow(data,cmap=plt.cm.gray,interpolation='nearest')
ax.set_title(title)
plt.show()
for i in range(len(target)):
visualized(np.reshape(target,(16,8)),i)
# In:
#HOPfield是1和-1
target = -1
#创建一个hopfield神经网络，吸引子为（012）
net = nl.net.newhop(target)
# In:
#测试数据
test_data1 = 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,
0,1,0,0,0,0,0,0,
0,1,0,1,0,0,1,0,
0,1,0,0,0,0,1,1,
0,1,0,0,1,0,1,0,
0,1,0,0,0,0,1,0,
0,0,0,0,0,0,1,0,
0,1,0,0,0,0,1,0,
0,1,0,0,1,0,0,0,
0,0,0,0,1,0,1,0,
0,1,0,1,0,0,0,0,
0,0,1,0,0,1,0,0,
0,0,0,1,1,0,0,0,
0,0,0,0,0,0,0,0])
test_data2 = np.array([0,1,0,0,0,1,0,0,
0,0,0,0,1,0,0,0,
0,0,0,1,1,0,0,0,
0,0,1,0,1,0,0,0,
0,1,0,0,0,0,1,0,
0,1,0,0,1,0,1,0,
0,0,0,0,1,0,0,0,
0,0,0,0,1,0,0,0,
0,0,0,1,0,0,1,0,
0,1,0,0,1,0,0,0,
0,0,0,0,1,0,0,0,
0,0,0,0,0,0,0,0,
0,0,0,0,1,0,0,0,
0,0,0,0,1,0,0,0,
1,1,1,1,0,1,1,1,
0,0,1,0,0,0,1,0])
test_data3 = np.array([0,0,1,0,0,0,0,0,
0,0,1,0,1,1,0,0,
0,0,0,0,0,0,0,0,
0,0,0,0,0,0,1,0,
1,1,0,0,1,0,0,0,
0,1,0,0,0,0,1,0,
0,1,0,0,1,0,0,0,
0,1,0,0,0,1,0,0,
0,0,0,0,0,0,0,0,
0,0,1,1,0,0,1,1,
0,0,1,0,0,0,0,0,
0,1,0,0,0,0,0,0,
0,1,0,1,0,0,1,0,
0,1,0,1,0,0,0,0,
0,1,1,0,0,1,1,0,
0,0,0,0,0,0,0,0])
test_data4 = np.array([0,0,0,0,0,0,0,0,
0,0,1,1,1,1,0,0,
0,1,0,0,0,0,1,0,
0,1,0,0,0,0,1,0,
0,0,0,0,0,0,1,0,
0,0,0,0,0,0,1,0,
0,0,0,0,0,0,1,0,
0,0,0,0,0,1,0,0,
0,0,0,0,1,0,0,0,
0,0,0,1,0,0,0,0,
0,0,1,0,0,0,0,0,
0,1,0,0,0,0,0,0,
0,1,0,0,0,0,0,0,
0,1,0,0,0,0,0,0,
0,1,1,1,1,1,1,0,
0,0,0,0,0,0,0,0])
# In:
visualized(np.reshape(test_data1,(16,8)),'test1')
visualized(np.reshape(test_data2,(16,8)),'test2')
visualized(np.reshape(test_data3,(16,8)),'test3')
visualized(np.reshape(test_data4,(16,8)),'test4')
# In:
def test(test_data):
global target
test_data = -1
out = net.sim()
for i in range(len(target)):
if ((out == target).all()):
print('test_data is ',i)
visualized(np.reshape(out,(16,8)),'a')
test(test_data4)
# In:
test_data4 = -1
out1 = net.sim()
for i in range(len(target)):
if ((out1 == target).all()):
print('test_Data is :',i)
visualized(np.reshape(out1,(16,8)),'a')
# In[ ]:
这是三个输出图，问题就是，不管换哪一个测试数据，都会得到同一个输出，实在是找不到问题所在。
哪里都找不到答案，只能回到鱼C老家提问了呜呜呜
艹，介么高级，不会 很高级，我有同学也做这个，他有次跟我说，python做深度学习，确实方便，但是精度不够，不知道为什么，结果不能达到预期，换了matlib同样的代码思路，结果就跑出来。
我只是听他说过，具体什么原因，我不知道。如果有错误，请指出。 ……
据我所知，这个论坛好像没多少人干这个吧
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