Python实现浅层神经网络

import numpy as npimport matplotlib.pyplot as pltdef sigmoid(z):    return 1 / (1 + np.exp(-z))def forward(X, w1, w2, b1, b2):    z1 = np.dot(w1,X) + b1  # w1=h*n     X=n*m      z1=h*m    A1 = sigmoid(z1)  # A1=h*m    z2 = np.dot(w2,A1) + b2  # w2=1*h   z2=1*m    A2 = sigmoid(z2)  # A2=1*m    return z1, z2, A1, A2def backward(y, X, A2, A1, z2, z1, w2, w1):    n, m = np.shape(X)    dz2 = A2 - y  # A2=1*m y=1*m    dw2 = 1 / m * np.dot(dz2, A1.T)  # dz2=1*m A1.T=m*h dw2=1*h    db2 = 1 / m * np.sum(dz2, axis=1, keepdims=True)    dz1 = np.dot(w2.T, dz2) * A1 * (1 - A1)  # w2.T=h*1 dz2=1*m z1=h*m A1=h*m dz1=h*m    dw1 = 1 / m * np.dot(dz1, X.T)  # z1=h*m X'=m*n dw1=h*n    db1 = 1 / m * np.sum(dz1, axis=1, keepdims=True)    return  dw1, dw2, db1, db2def costfunction(A2,y):    m,n=np.shape(y)    J =np.sum(y*np.log(A2)+(1-y)*np.log(1-A2))/m    #J = (np.dot(y, np.log(A2.T)) + np.dot((1 - y).T, np.log(1 - A2))) / m    return -JData = np.loadtxt("gua2.txt")X = Data[:, 0:-1]X = X.Ty = Data[:, -1]n, m = np.shape(X)y=y.reshape(1,m)n_x = n  # size of the input layern_y = 1  # size of the output layern_h = 5  # size of the hidden layerw1 = np.random.randn(n_h, n_x) * 0.01  # h*nb1 = np.zeros((n_h, 1))  # h*1w2 = np.random.randn(n_y, n_h) * 0.01  # 1*hb2 = np.zeros((n_y, 1))alpha=0.1number=10000for i in range(0,number):    z1, z2, A1, A2 = forward(X, w1, w2, b1, b2)    dw1, dw2, db1, db2 = backward(y, X, A2, A1, z2, z1, w2, w1)    w1=w1-alpha*dw1    w2=w2-alpha*dw2    b1=b1-alpha*db1    b2=b2-alpha*db2    J=costfunction(A2,y)    if (i % 100 == 0):        print(i)    plt.plot(i, J, 'ro')plt.show()

数据来自周志华西瓜书5-5，隐藏层数为5，参考资料为吴恩达深度学习