机器学习python神经网络——利用已经训练好的参数进行预测

一、引入库和数据集

import numpy as np
import scipy.io as io
import matplotlib.pyplot as plt
data=io.loadmat('C:/Users/Administrator/Desktop/新建文件夹/ex3data1.mat')      #type(data)-----dict
data.keys()  #字典data的键有哪些，以列表的形式给出
#dict_keys(['__header__', '__version__', '__globals__', 'X', 'y'])
X=data['X']  #字典data对应键为'X'的值
y=data['y']​
X.shape  #(5000,400) 
y.shape  #(5000, 1)
np.unique(y) 
#array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10], dtype=uint8)

二、数据可视化

def plot_100_images():
    random_sample=np.random.choice(range(0,5000),100)   #range(X.shape[0])
    images=X[random_sample,:]   #(100,10)
    y_images=y.ravel()[random_sample]
    fig,ax_array=plt.subplots(figsize=(8,8),nrows=10,ncols=10,sharex=True,sharey=True)
    for row in range(10):
        for col in range(10):
            ax_array[row,col].matshow((images[row*10+col].reshape(20,20)).T)
    plt.xticks([])
    plt.yticks([])
    plt.show()

三、引入权重

data2=io.loadmat('C:/Users/Administrator/Desktop/新建文件夹/ex3weights.mat')
data2.keys()
#dict_keys(['__header__', '__version__', '__globals__', 'Theta1', 'Theta2'])
theta1=data2['Theta1']
theta2=data2['Theta2']
theta1.shape  #(25,401)
theta2.shape  #(10,26)

X=np.insert(X,0,1,axis=1)  #(5000,401)
y=y.ravel()   #(5000,)

四、用神经网络对已经训练好的权重进行预测

1.前向传播

def sigmoid(z):
    return 1/(1+np.exp(-z))

a1=X  #(5000,401)
z2=a1@theta1.T #(5000,25)
a2=sigmoid(z2)  #(#(5000,25))
'''增加偏置项'''
a2=np.insert(a2,0,1,axis=1) #(5000,26)
z3=a2@theta2.T   #(5000,10)
a3=sigmoid(z3)   #(5000,10)

2.预测

y_argmax=np.argmax(a3,axis=1)
y_pred=y_argmax+1
accuracy=np.mean(y_pred==y)  
print('accuracy={}'.format(accuracy))
#accuracy=0.9752

可以发现，神经网络的预测精度要比logistic回归多分类的预测精度高！