人工智能实战2019_第5次个人作业_邹镇洪

Written by joezou(邹镇洪), 2019/4/5

项目	内容
这个作业属于课程	人工智能实战 2019 - 北京航空航天大学
这个作业的要求在	人工智能实战第五次作业（个人）
我在这个课程的目标是	学会利用云部署机器学习模型并完成一个app
这个作业在这些方面帮助我实现目标	练习python代码和学会softmax函数
其他参考文献	无

目的：拟合逻辑与门，逻辑或门

与门

label	data1	data2	data3	data4
x1	0	0	1	1
x2	0	1	0	1
y	0	0	0	1

或门

label	data1	data2	data3	data4
x1	0	0	1	1
x2	0	1	0	1
y	0	1	1	1

def ReadAndData(gate):
    X = np.array([0,0,1,1,0,1,0,1]).reshape(2,4)
    AND = np.array([0,0,0,1]).reshape(1,4)
    OR = np.array([0,1,1,1]).reshape(1,4)
    if gate=='and':
        return X,AND
    if gate=='or':
        return X,OR

def Sigmoid(x):
    s=1/(1+np.exp(-x))
    return s

def ForwardCalculation(W,B,X):
    z = np.dot(W, X) + B
    a = Sigmoid(z)
    return a

def BackPropagation(X,Y,A):
    dloss_z = A - Y
    db = dloss_z
    dw = np.dot(dloss_z, X.T)
    return dw, db

def UpdateWeights(w, b, dW, dB, eta):
    w = w - eta * dW
    b = b - eta * dB
    return w,b

def CheckLoss(w, b, X, Y, count):
    A = ForwardCalculation(w, b, X)
    p1 = Y * np.log(A)
    p2 = (1-Y) * np.log(1-A)
    LOSS = -(p1 + p2)
    loss = np.sum(LOSS) / count
    return loss

def InitialParameters(num_input, num_output, flag):
    if flag == 0:
        W1 = np.zeros((num_output, num_input))
    elif flag == 1:
        W1 = np.random.normal(size=(num_output, num_input))
    elif flag == 2:
        W1=np.random.uniform(-np.sqrt(6)/np.sqrt(num_input+num_output),\
                                        np.sqrt(6)/np.sqrt(num_output+num_input),\
                                        size=(num_output,num_input))

    B1 = np.zeros((num_output, 1))
    return W1,B1

def ShowResult(W,B,X,Y):

    w = -W[0,0]/W[0,1]
    b = -B[0,0]/W[0,1]
    x = np.array([0,1])
    y = w * x + b
    plt.plot(x,y)
   
    for i in range(X.shape[1]):
        if Y[0,i] == 0:
            plt.scatter(X[0,i],X[1,i],marker="o",c='b',s=64)
        else:
            plt.scatter(X[0,i],X[1,i],marker="^",c='r',s=64)
    plt.axis([-0.1,1.1,-0.1,1.1])
    plt.show()

if __name__ == '__main__':
    n_input = 2
    n_output = 1
    W,B = InitialParameters(n_input, n_output,1)
    # initialize_data
    eta = 0.1
    iteration, max_iteration = 0, 10000
    eps = 1e-2
    loss = 0
    GATE = 'or'
    X, Y = ReadAndData(GATE)
    # count of samples
    num_features = X.shape[0]
    num_example = X.shape[1]

    for iteration in range(max_iteration):
        for i in range(num_example):
            # get x and y value for one sample
            x = X[:,i].reshape(num_features,1)
            y = Y[:,i].reshape(1,1)
            # get z from x,y
            z = ForwardCalculation(W, B, x)
            # calculate gradient of w and b
            dW, dB = BackPropagation(x, y, z)
            # update w,b
            W, B = UpdateWeights(W, B, dW, dB, eta)
            # calculate loss for this batch
            loss = CheckLoss(W,B,X,Y,num_example)
            # condition 1 to stop
            if loss < eps:
                break;
        #print(iteration,i,loss,W,B)
        if loss < eps:
            break;

    print("Gate = %s, w = %.5f %.5f, b = %.5f" % (GATE,W[0,0],W[0,1],B[0,0]))

    ShowResult(W,B,X,Y)

结果展示：

Gate = or, flag = 0, w = 8.51377 8.51633, b = -3.79194, iteration=2322
Gate = or, flag = 1, w = 8.51580 8.51501, b = -3.79102, iteration=2319
Gate = or, flag = 2, w = 8.51418 8.51649, b = -3.79098, iteration=2312

Gate = and, flag = 0, w = 8.53594 8.53331, b = -12.97199, iteration=4321
Gate = and, flag = 1, w = 8.53464 8.53318, b = -12.97296, iteration=4311
Gate = and, flag = 2, w = 8.53470 8.53323, b = -12.97305, iteration=4319

可知参数初始化在当前网络下没有明显差距

绘图

• 与门
  
• 或门