思路：

　　定義三個訓練測試圖片0 1 2（16*8），即三個吸引子。然后創建一個Hopfield神經網絡，把訓練數據輸入。然后在用測試數據輸入測試結果。

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]])#畫圖函數 def visualized (data, title): fig, ax = plt.subplots()ax.imshow(data, cmap=plt.cm.gray, interpolation='nearest')ax.set_title(title)plt.show()#顯示012 for i in range(len(target)):visualized(np.reshape(target[i], (16,8)), i)# In[2]:#hopfield網絡的值是1和-1 target[target == 0] = -1#創建一個hopfield神經網絡，吸引子為target(012) net = nl.net.newhop(target)#定義3個測試數據 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])#顯示測試數據 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 #把測試數據輸入hopfield網絡，得到輸出 out1 = net.sim([test_data1]) #判斷測試數據的數字是多少 for i in range(len(target)):if((out1 == target[i]).all()):print("test_data is :",i) #顯示輸出 visualized(np.reshape(out1, (16,8)), "output1") test_data2[test_data2==0] = -1 #把測試數據輸入hopfield網絡，得到輸出 out2 = net.sim([test_data2]) #判斷測試數據的數字是多少 for i in range(len(target)):if((out2 == target[i]).all()):print("test_data is :",i) #顯示輸出 visualized(np.reshape(out2, (16,8)), "output2") test_data3[test_data3==0] = -1 #把測試數據輸入hopfield網絡，得到輸出 out3 = net.sim([test_data3]) #判斷測試數據的數字是多少 for i in range(len(target)):if((out3 == target[i]).all()):print("test_data is :",i) #顯示輸出 visualized(np.reshape(out3, (16,8)), "output3")

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轉載于:https://www.cnblogs.com/mengqimoli/p/11103733.html

與50位技術專家面對面20年技術見證，附贈技術全景圖

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