Function neuralNetwork

NeuralNetwok/NeuralNetwork.py:13–59

(input_layer_size,hidden_layer_size,out_put_layer)

import time

def neuralNetwork(input_layer_size,hidden_layer_size,out_put_layer):
    data_img = loadmat_data("data_digits.mat")
    X = data_img['X']
    y = data_img['y']

    '''scaler = StandardScaler()
    scaler.fit(X)
    X = scaler.transform(X)'''  
    
    m,n = X.shape
    """digits = datasets.load_digits()
    X = digits.data
    y = digits.target
    m,n = X.shape
    
    scaler = StandardScaler()
    scaler.fit(X)
    X = scaler.transform(X)"""
    
    ##　随机显示几行数据
    rand_indices = [t for t in [np.random.randint(x-x, m) for x in range(100)]]  # 生成100个0-m的随机数
    display_data(X[rand_indices,:])     # 显示100个数字    
    
    #nn_params = np.vstack((Theta1.reshape(-1,1),Theta2.reshape(-1,1)))
    
    Lambda = 1
    
    initial_Theta1 = randInitializeWeights(input_layer_size,hidden_layer_size); 
    initial_Theta2 = randInitializeWeights(hidden_layer_size,out_put_layer)
    
    initial_nn_params = np.vstack((initial_Theta1.reshape(-1,1),initial_Theta2.reshape(-1,1)))  #展开theta    
    #np.savetxt("testTheta.csv",initial_nn_params,delimiter=",")
    start = time.time()
    result = optimize.fmin_cg(nnCostFunction, initial_nn_params, fprime=nnGradient, args=(input_layer_size,hidden_layer_size,out_put_layer,X,y,Lambda), maxiter=100)
    print (u'执行时间：',time.time()-start)
    print (result)
    '''可视化 Theta1'''
    length = result.shape[0]
    Theta1 = result[0:hidden_layer_size*(input_layer_size+1)].reshape(hidden_layer_size,input_layer_size+1)
    Theta2 = result[hidden_layer_size*(input_layer_size+1):length].reshape(out_put_layer,hidden_layer_size+1)    
    display_data(Theta1[:,1:length])
    display_data(Theta2[:,1:length])
    '''预测'''
    p = predict(Theta1,Theta2,X)
    print (u"预测准确度为：%f%%"%np.mean(np.float64(p == y.reshape(-1,1))*100))    
    res = np.hstack((p,y.reshape(-1,1)))
    np.savetxt("predict.csv", res, delimiter=',')
    

# 加载mat文件