版权声明：本文为博主原创文章，转载请标明出处。 https://blog.csdn.net/chuan403082010/article/details/86370551
nn_data.txt数据
6.5 4 1
7.5 4.5 1
2 0.5 0
4 1 0
1 2 0
7 6 1
5 6 1
5.5 4.5 1
1 1 0
3.5 2 0
5 2 0
7 2 0
8.5 3 0
9 4 0
1.5 3.5 0
4 3 0
1 5 0
3 5 0
2 6.5 0
3.5 6.5 0import numpy as np
import matplotlib.pyplot as plt
#loaddata
def loaddata(filename):
fr = open(filename)
x = []
y = []
for line in fr.readlines():
line = line.strip().split()
x.append([float(line[0]),float(line[1])])
y.append(float(line[-1]))
return np.mat(x),np.mat(y).T
#data scaling
def scaling(data):
max = np.max(data)
min = np.min(data)
return (data - min)/(max - min),max,min
#sigmoid
def sigmoid(data):
return 1/(1 + np.exp(-data))
# w b calc
def wb_calc(X,ymat,alpha = 0.1,n_hidden_dim = 3,reg_lamda=0,maxIter = 10001):
#init w b
w1 = np.mat(np.random.randn(2,n_hidden_dim))
b1 = np.mat(np.random.randn(1,n_hidden_dim))
w2 = np.mat(np.random.randn(n_hidden_dim, 1))
b2 = np.mat(np.random.randn(1, 1))
w1_save = []
w2_save = []
b1_save = []
b2_save = []
ss = []
for step in range(maxIter):
#FP
z1 = X * w1 + b1#(20,2) (2,3) + (1,3) = (20,3)
a1 = sigmoid(z1) #(20,3)
z2 = a1 * w2 + b2#(20,3) (3,1) + (1,1) = (20,1)
a2 = sigmoid(z2)#(20,1)
#BP
a0 = X.copy()
detal2 =a2 - ymat#(20,1)
delta1 =np.mat((detal2 * w2.T).A * (a1.A * (1- a1).A))
# (20,1)(1,3) .* (20,3) = (20,3)
dw1 = a0.T * delta1 + reg_lamda * w1#(2,20)(20,3)+ (2,3) = (2,3)
db1 = np.sum(delta1,0) #
#db1 = np.mat(np.ones()) * datal
dw2 = a1.T * detal2 + reg_lamda * w2
db2 = np.sum(detal2,0) #?
#update w b
w1 -= alpha * dw1
b1 -= alpha * db1
w2 -= alpha * dw2
b2 -= alpha * db2
if step % 100== 0:
w1_save.append(w1.copy())
w2_save.append(w2.copy())
b1_save.append(b1.copy())
b2_save.append(b2.copy())
ss.append(step)
return w1,b1,w2,b2,w1_save,b1_save,w2_save,b2_save,ss
#implement
xmat,ymat = loaddata('nn_data.txt')
# print('x:',xmat,xmat.shape)
# print('y:',ymat,ymat.shape)
xmat_s,xmat_max,xmat_min = scaling(xmat)
# print('x_s:',xmat_s)
w1,b1,w2,b2,w1_save,b1_save,w2_save,b2_save,ss = wb_calc(xmat_s,ymat,alpha=0.05,maxIter=10000,n_hidden_dim = 10)
#普通展示
# plotx1 = np.arange(0,10,0.01) #array
# plotx2 = np.arange(0,10,0.01)
# plotX1,plotX2 = np.meshgrid(plotx1,plotx2)
# plotx_new = np.c_[plotX1.ravel(),plotX2.ravel()]
# ploty_new2 = (plotx_new - xmat_min)/(xmat_max - xmat_min)
#
# plot_z1 = ploty_new2 * w1 + b1
# plot_a1 = sigmoid(plot_z1)
# plot_z2 = plot_a1 * w2 + b2
# plot_a2 = sigmoid(plot_z2)
# ploty_new = np.reshape(plot_a2,plotX1.shape)
#
# plt.contourf(plotX1,plotX2,ploty_new,1,alpha = 0.5)
# plt.scatter(xmat[:,0][ymat ==0].A,xmat[:,1][ymat==0].A,label = '0',marker='o',s = 100)
# plt.scatter(xmat[:,0][ymat ==1].A,xmat[:,1][ymat==1].A,label = '1',marker='^',s = 150)
# plt.grid()
# plt.legend()
# plt.show()
# 动画展示
plotx1 = np.arange(0,10,0.01) #array
plotx2 = np.arange(0,10,0.01)
plotX1,plotX2 = np.meshgrid(plotx1,plotx2)
plotx_new = np.c_[plotX1.ravel(),plotX2.ravel()]
ploty_new2 = (plotx_new - xmat_min)/(xmat_max - xmat_min)
for i in range(len(w1_save)):
plt.clf()
plot_z1 = ploty_new2 * w1_save[i] + b1_save[i]
plot_a1 = sigmoid(plot_z1)
plot_z2 = plot_a1 * w2_save[i] + b2_save[i]
plot_a2 = sigmoid(plot_z2)
ploty_new = np.reshape(plot_a2,plotX1.shape)
plt.contourf(plotX1,plotX2,ploty_new,1,alpha = 0.5)
plt.scatter(xmat[:,0][ymat ==0].A,xmat[:,1][ymat==0].A,label = '0',marker='o',s = 100)
plt.scatter(xmat[:,0][ymat ==1].A,xmat[:,1][ymat==1].A,label = '1',marker='^',s = 150)
plt.grid()
plt.legend()
plt.title('iter:%s'%np.str(ss[i]))
plt.pause(0.001)
plt.show()
