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p3 minibatch SGD.py
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p3 minibatch SGD.py
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import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import random
# 本代码是一个最简单的线形回归问题，优化函数为minibatch SGD
rate = 0.2 # learning rate
def da(y,y_p,x):
return (y-y_p)*(-x)
def db(y,y_p):
return (y-y_p)*(-1)
def calc_loss(a,b,x,y):
tmp = y - (a * x + b)
tmp = tmp ** 2 # 对矩阵内的每一个元素平方
SSE = sum(tmp) / (2 * len(x))
return SSE
def draw_hill(x,y):
a = np.linspace(-20,20,100)
print(a)
b = np.linspace(-20,20,100)
x = np.array(x)
y = np.array(y)
allSSE = np.zeros(shape=(len(a),len(b)))
for ai in range(0,len(a)):
for bi in range(0,len(b)):
a0 = a[ai]
b0 = b[bi]
SSE = calc_loss(a=a0,b=b0,x=x,y=y)
allSSE[ai][bi] = SSE
a,b = np.meshgrid(a, b)
return [a,b,allSSE]
def shuffle_data(x,y):
# 随机打乱x，y的数据，并且保持x和y一一对应
seed = random.random()
random.seed(seed)
random.shuffle(x)
random.seed(seed)
random.shuffle(y)
def get_batch_data(x,y,batch=3):
shuffle_data(x,y)
x_new = x[0:batch]
y_new = y[0:batch]
return [x_new,y_new]
# 模拟数据
x = [30 ,35,37, 59, 70, 76, 88, 100]
y = [1100, 1423, 1377, 1800, 2304, 2588, 3495, 4839]
# 数据归一化
x_max = max(x)
x_min = min(x)
y_max = max(y)
y_min = min(y)
for i in range(0,len(x)):
x[i] = (x[i] - x_min)/(x_max - x_min)
y[i] = (y[i] - y_min)/(y_max - y_min)
[ha,hb,hallSSE] = draw_hill(x,y)
hallSSE = hallSSE.T# 重要，将所有的losses做一个转置。原因是矩阵是以左上角至右下角顺序排列元素，而绘图是以左下角为原点。
# 初始化a,b值
a = 10.0
b = -20.0
fig = plt.figure(1, figsize=(12, 8))
# 绘制图1的曲面
ax = fig.add_subplot(2, 2, 1, projection='3d')
ax.set_top_view()
ax.plot_surface(ha, hb, hallSSE, rstride=2, cstride=2, cmap='rainbow')
# 绘制图2的等高线图
plt.subplot(2,2,2)
ta = np.linspace(-20, 20, 100)
tb = np.linspace(-20, 20, 100)
plt.contourf(ha,hb,hallSSE,15,alpha=0.5,cmap=plt.cm.hot)
C = plt.contour(ha,hb,hallSSE,15,colors='black')
plt.clabel(C,inline=True)
plt.xlabel('a')
plt.ylabel('b')
plt.ion() # iteration on
all_loss = []
all_step = []
last_a = a
last_b = b
for step in range(1,200):
loss = 0
all_da = 0
all_db = 0
shuffle_data(x,y)
[x_new,y_new] = get_batch_data(x,y,batch=4)
for i in range(0,len(x_new)):
y_p = a*x_new[i] + b
loss = loss + (y_new[i] - y_p)*(y_new[i] - y_p)/2
all_da = all_da + da(y_new[i],y_p,x_new[i])
all_db = all_db + db(y_new[i],y_p)
#loss_ = calc_loss(a = a,b=b,x=np.array(x),y=np.array(y))
loss = loss/len(x_new)
# 绘制图1中的loss点
ax.scatter(a, b, loss, color='black')
# 绘制图2中的loss点
plt.subplot(2,2,2)
plt.scatter(a,b,s=5,color='blue')
plt.plot([last_a,a],[last_b,b],color='aqua')
# 绘制图3中的回归直线
plt.subplot(2, 2, 3)
plt.plot(x, y)
plt.plot(x, y, 'o')
x_ = np.linspace(0, 1, 2)
y_draw = a * x_ + b
plt.plot(x_, y_draw)
# 绘制图4的loss更新曲线
all_loss.append(loss)
all_step.append(step)
plt.subplot(2,2,4)
plt.plot(all_step,all_loss,color='orange')
plt.xlabel("step")
plt.ylabel("loss")
# print('a = %.3f,b = %.3f' % (a,b))
last_a = a
last_b = b
a = a - rate*all_da
b = b - rate*all_db
if step%1 == 0:
print("step: ", step, " loss: ", loss)
plt.show()
plt.pause(0.01)
plt.show()
plt.pause(99999999999)