📝 修改神修修改readme

Author: lawlite19

images/NeuralNetwork_05.png

@@ -565,8 +565,8 @@ def nnCostFunction(nn_params,input_layer_size,hidden_layer_size,num_labels,X,y,L
 # 梯度
 def nnGradient(nn_params,input_layer_size,hidden_layer_size,num_labels,X,y,Lambda):
     length = nn_params.shape[0]
-    Theta1 = nn_params[0:hidden_layer_size*(input_layer_size+1)].reshape(hidden_layer_size,input_layer_size+1)
-    Theta2 = nn_params[hidden_layer_size*(input_layer_size+1):length].reshape(num_labels,hidden_layer_size+1)
+    Theta1 = nn_params[0:hidden_layer_size*(input_layer_size+1)].reshape(hidden_layer_size,input_layer_size+1).copy()   # 这里使用copy函数，否则下面修改Theta的值，nn_params也会一起修改
+    Theta2 = nn_params[hidden_layer_size*(input_layer_size+1):length].reshape(num_labels,hidden_layer_size+1).copy()
     m = X.shape[0]
     class_y = np.zeros((m,num_labels))      # 数据的y对应0-9，需要映射为0/1的关系    
     # 映射y
@@ -581,9 +581,8 @@ def nnGradient(nn_params,input_layer_size,hidden_layer_size,num_labels,X,y,Lambd
     
     Theta1_grad = np.zeros((Theta1.shape))  #第一层到第二层的权重
     Theta2_grad = np.zeros((Theta2.shape))  #第二层到第三层的权重
-    
-    Theta1[:,0] = 0;
-    Theta2[:,0] = 0;
+      
+   
     '''正向传播，每次需要补上一列1的偏置bias'''
     a1 = np.hstack((np.ones((m,1)),X))
     z2 = np.dot(a1,np.transpose(Theta1))
@@ -592,15 +591,19 @@ def nnGradient(nn_params,input_layer_size,hidden_layer_size,num_labels,X,y,Lambd
     z3 = np.dot(a2,np.transpose(Theta2))
     h  = sigmoid(z3)
     
+    
     '''反向传播，delta为误差，'''
     delta3 = np.zeros((m,num_labels))
     delta2 = np.zeros((m,hidden_layer_size))
     for i in range(m):
-        delta3[i,:] = h[i,:]-class_y[i,:]
+        #delta3[i,:] = (h[i,:]-class_y[i,:])*sigmoidGradient(z3[i,:])  # 均方误差的误差率
+        delta3[i,:] = h[i,:]-class_y[i,:]                              # 交叉熵误差率
         Theta2_grad = Theta2_grad+np.dot(np.transpose(delta3[i,:].reshape(1,-1)),a2[i,:].reshape(1,-1))
         delta2[i,:] = np.dot(delta3[i,:].reshape(1,-1),Theta2_x)*sigmoidGradient(z2[i,:])
         Theta1_grad = Theta1_grad+np.dot(np.transpose(delta2[i,:].reshape(1,-1)),a1[i,:].reshape(1,-1))
     
+    Theta1[:,0] = 0
+    Theta2[:,0] = 0          
     '''梯度'''
     grad = (np.vstack((Theta1_grad.reshape(-1,1),Theta2_grad.reshape(-1,1)))+Lambda*np.vstack((Theta1.reshape(-1,1),Theta2.reshape(-1,1))))/m
     return np.ravel(grad)