本文主要是介绍梯度下降算法学习,对大家解决编程问题具有一定的参考价值,需要的程序猿们随着小编来一起学习吧!
梯度下降求f(x,y)=x^2 + y^2
先画个图
import numpy as np
from matplotlib import interactive, pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from matplotlib import animation as amat
"function:f()x,y = x^2 +y^2"
def fangcheng(x,y):
return np.power(x,2) + np.power(y,2)
def show(X,Y,func=fangcheng):
fig = plt.figure()
ax = Axes3D(fig)
X,Y = np.meshgrid(X,Y,sparse=True)
Z = func(X,Y)
plt.title("test f(x,y)=x^2 + y^2")
ax.plot_surface(X,Y,Z,rstride=1,cstride=1,cmap='rainbow')
ax.set_xlabel('x label',color='r')
ax.set_ylabel('y label',color='g')
ax.set_zlabel('z label',color='b')
amat.FuncAnimation(fig,fangcheng,frames=200,interval=20,blit=True)
plt.show()
if __name__ == '__main__':
X = np.arange(-1.5,1.5,0.1)
Y = np.arange(-1.5,1.5,0.1)
Z = fangcheng(X,Y)
show(X,Y,fangcheng)
梯度下降求最小值
import numpy as np
from matplotlib import interactive, pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from matplotlib import animation as amat
"function:f(x,y) = x^2 +y^2"
def fangcheng(x,y):
return np.power(x,2) + np.power(y,2)
def draw(px,py,pz,X,Y,func=fangcheng):
fig = plt.figure()
ax = Axes3D(fig)
X,Y = np.meshgrid(X,Y,sparse=True)
Z = func(X,Y)
plt.title("test f(x,y)=x^2 + y^2")
ax.plot_surface(X,Y,Z,rstride=1,cstride=1,cmap='rainbow',)
ax.set_xlabel('x label',color='r')
ax.set_ylabel('y label',color='g')
ax.set_zlabel('z label',color='b')
#amat.FuncAnimation(fig,fangcheng,frames=200,interval=20,blit=True)
ax.plot(px,py,pz,'r.',color = "blue")
plt.show()
def tiduxiajiang(X,Y,maxcrcyles=100,learnrate=0.1):
new_x = [X]
new_y = [Y]
gz = [fangcheng(X,Y)]
current_x = X
current_y = Y
for cycle in range(maxcrcyles):
current_y -=learnrate * 2 * Y
current_x -=learnrate * 2 * X
X = current_x
Y = current_y
new_x.append(X)
new_y.append(Y)
gz.append(fangcheng(X,Y))
return new_x,new_y,gz
if __name__=='__main__':
X = np.arange(-1.5,1.5,0.1)
Y = np.arange(-1.5,1.5,0.1)
x = 1
y = 3
print(x,y)
x,y,z = tiduxiajiang(x,y)
print(x,y,z)
draw(x,y,z,X,Y,fangcheng)
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