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## 課堂筆記 - 深度學習 Deep Learning (5) Lab1

• 用python實作PLA (直接把訓練資料寫進程式裡)

``````import numpy as np
import matplotlib.pyplot as plt

dataset = np.array([

[1, 0, 1],
[1, 3, -1],
[2, -6, 1],
[-1, -3, 1],
[-5, 5, -1],
[5, 2, 1],
[-2, 2, -1],
[-7, 2, -1],
[4, -4, 1],
[-5, -1, -1]

])

num = 10
# 切割dataset的數據
x1 = dataset[:, 0]
x2 = dataset[:, 1]
y = dataset[:, 2]

def pla_with_data():

# 初始值 >> w=[0,0] b=0
w = np.zeros((2, 1))
dot = 0
b = 0
flag = 1

for k in range(100):   # 限制無窮迴圈 >> 次數設定100次
flag = 1
for i in range(num):     # 看每個點是否為正確

dot = x1[i]*int(w[0])+x2[i]*int(w[1])   # 將一個點的座標帶入 跟w作內積
if sign(dot, b) != y[i]:  # 與參考資料y不相符 >> 線劃分錯誤

flag = 0
w[0] += y[i] * x1[i]    # 矯正 w = w + y*x
w[1] += y[i] * x2[i]
b = b + y[i]            # 矯正 b = b + y
# print(w, b)

else:
continue  # 與參考資料y相符 >> 下一個點

if flag == 1:
break  # 全部的點都與參考資料y相符 >> 劃分完成

return w, b

def sign(dot, b):
if dot+b >= 0:
return 1
else:
return -1

def draw(x1, x2, y):

# 製作figure
fig = plt.figure()

# 圖表的設定

# 散佈圖
for i in range(num):
if y[i] == 1:
ax.scatter(x1[i], x2[i], color='red')
else:
ax.scatter(x1[i], x2[i], color='black')

# data
x = [2, -5, -2]
y = [-4, 1, -2]
ax.scatter(x, y, c='g', marker="x")

plt.show()

prex1 = [2, -5, -2]
prex2 = [-4, 1, -2]

w, b = pla_with_data()

for i in range(3):
pre = np.sign((prex1[i]*w[0]+prex2[i]*w[1])+b)
print('predict example %s = %s' % (i+1, pre))

print('w1 = %s , w2 = %s , b = %s' % (w[0], w[1], b))
draw(x1, x2, y)
``````