人工智能实战2019_第五次作业_蔺立萱

项目	内容
课程链接	人工智能实战 2019（北京航空航天大学）
第五次作业要求	第五次作业：训练一个逻辑与门和逻辑或门
我的课程目标	熟练掌握python语言，神经网络相关应用，实操项目
本次作业的帮助	熟悉神经网络的相关知识，锻炼编程实践能力
我的github账户	linlixuan

1、题目

训练一个逻辑与门和逻辑或门，结果及代码形成博客

---

2、代码

# -*- coding: utf-8 -*-
"""
Created on Mon Apr  8 12:43:25 2019

@author: Jam
"""

import numpy as np
import matplotlib.pyplot as plt

def Initialize(X, m, n):
    W = np.zeros((1,n))
    B = np.zeros((1,1))
    eta = 0.8
    max_epoch = 10000
    return W, B, eta, max_epoch

def Sigmiod(x):
    A = 1/(1+np.exp(-x))
    return A

def ForwardCal(W, X, B):
    Z = np.dot(W,X) + B
    A = Sigmiod(Z)
    return Z, A

def BackwardCal(X, Y, A, m):
    dZ = A - Y
    dB = dZ.sum(axis = 1, keepdims = True)/m
    dW = np.dot(dZ, X.T)/m
    return dW, dB

def UpdateWeights(eta, dW, dB, W, B):
    W = W - eta*dW
    B = B - eta*dB
    return W, B

def And_Or(key):
    X1 =np.array([0,0,1,1])
    X2 = np.array([0,1,0,1])
    X = np.vstack((X1, X2))
    if (key == 'And'):
        Y = np.array([0,0,0,1])
    elif (key == 'Or'):
        Y = np.array([0,1,1,1])
    return X, Y

def CheckLoss(Y, A, m):
    p1 = 1 - Y
    p2 = np.log(A)
    p3 = np.log(1-A)
    p4 = np.multiply(Y, p2)
    p5 = np.multiply(p1, p3)
    Loss = np.sum(-(p4 + p5))
    loss = Loss / m
    return loss
    
def train(key):
    X, Y = And_Or(key)
    n = X.shape[0]
    m = X.shape[1]
    W, B, eta, max_epoch = Initialize(X, m, n)
    epoch = 0

    for epoch in range(max_epoch):
        Z, A = ForwardCal(W, X, B)
        dW, dB = BackwardCal(X, Y, A, m)
        W, B = UpdateWeights(eta, dW, dB, W, B)
        loss = CheckLoss(Y, A, m)
        if loss <= 1e-2:
            break
    print(W, B)
    print(loss)
    print(epoch)
    ShowFigure(X, Y, W, B, m)

if __name__=='__main__':
    key = 'Or' #And
    train(key)

def ShowFigure(X, Y, W, B, m):
    for i in range(m):
        if Y[i] == 0:
            plt.plot(X[0,i], X[1,i], '.', c='r')
        elif Y[i] == 1:
            plt.plot(X[0,i], X[1,i], '^', c='g')

    a = - (W[0,0] / W[0,1])
    b = - (B[0,0] / W[0,1])
    x = np.linspace(-0.1,1.1,100)
    y = a * x + b
    plt.plot(x,y)
    plt.axis([-0.1,1.1,-0.1,1.1])
    plt.show()

---

3、输出结果

-逻辑或门
[[8.51697047 8.51697047]] [[-3.79279452]]
0.009999219577877546
1163

-逻辑与门
[[8.53544939 8.53544939]] [[-12.97388027]]
0.009999210510001869
2171