학교/인공지능
16번째 수업(0508)
공부 기록장
2024. 5. 8. 15:50
변형 과제 C E lll 을 테스트 케이스로 두고 결과 보이기
다층 퍼셉트론(학습부분)
# MLP
# 2 class : XOR
# 4 patterns / 1 class
# 2 Layer
import numpy as np
import math as m
def sigmoid(x) :
return(1.0/(1+m.exp(-x)))
lrate = 0.05
INDIM = 26
H1DIM = 5
OUTDIM = 3
PTTN_NUM = 3
x = np.array([[1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
0.1, 0.1, 1.0, 0.1, 0.1,
0.1, 0.1, 1.0, 0.1, 0.1,
0.1, 0.1, 1.0, 0.1, 0.1,
0.1, 0.1, 1.0, 0.1, 0.1], #T
[1.0, 0.1, 1.0, 1.0, 1.0, 1.0,
1.0, 0.1, 0.1, 0.1, 0.1,
1.0, 0.1, 0.1, 0.1, 0.1,
1.0, 0.1, 0.1, 0.1, 0.1,
0.1, 1.0, 1.0, 1.0, 1.0], #C
[1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
1.0, 0.1, 0.1, 0.1, 0.1,
1.0, 1.0, 1.0, 1.0, 1.0,
1.0, 0.1, 0.1, 0.1, 0.1,
1.0, 1.0, 1.0, 1.0, 1.0,] #E
])
t = np.array([ [1.0, 0.0, 0.0],
[0.0, 1.0, 0.0],
[0.0, 0.0, 1.0]
])
x_t = np.array([[1.0, 0.1, 1.0, 1.0, 1.0, 1.0,
1.0, 0.1, 0.1, 0.1, 0.1,
1.0, 0.1, 0.1, 0.1, 0.1,
1.0, 0.1, 0.1, 0.1, 1.0,
0.1, 1.0, 1.0, 1.0, 1.0], #C
[1.0, 1.0, 1.0, 1.0, 1.0, 0.1,
1.0, 0.1, 0.1, 0.1, 0.1,
1.0, 1.0, 1.0, 1.0, 1.0,
1.0, 0.1, 0.1, 0.1, 0.1,
1.0, 1.0, 1.0, 1.0, 0.1], #E
[1.0, 1.0, 0.1, 1.0, 0.1, 1.0, #lll
1.0, 0.1, 1.0, 0.1, 1.0,
1.0, 0.1, 1.0, 0.1, 1.0,
1.0, 0.1, 1.0, 0.1, 1.0,
1.0, 0.1, 1.0, 0.1, 1.0]])
w1 = np.zeros([H1DIM, INDIM])
for i in range(H1DIM):
for j in range(INDIM):
w1[i][j] = np.random.rand() / 10.0
w2 = np.zeros([OUTDIM, H1DIM])
for i in range(OUTDIM):
for j in range(H1DIM):
w2[i][j] = np.random.rand() / 10.0
print(w1)
print(w2)
y1 = np.zeros(H1DIM)
y2 = np.zeros(OUTDIM)
d1 = np.zeros(H1DIM)
d2 = np.zeros(OUTDIM)
for epoch in range (100000):
if (epoch % 1000) == 0:
print("epoch", epoch)
for p in range(PTTN_NUM):
###########################################
# feed forwarding
###########################################
# LAYER-1 (Hidden Layer)
for i in range(H1DIM):
out = 0.0
for j in range(INDIM):
out += w1[i][j] * x[p][j]
y1[i] = sigmoid(out)
# LAYER-2 (Output Layer)
# y1[0] = 1.0 ### bias
for i in range(OUTDIM):
out = 0.0
for j in range(H1DIM):
out += w2[i][j] * y1[j]
y2[i] = sigmoid(out)
###########################################
# Back Propagation
###########################################
# delta(error) for Layer-2 (Output Layer)
for i in range(OUTDIM):
d2[i] = (t[p][i] - y2[i]) * (y2[i]*(1 - y2[i]))
# delta(error) for Layer-1 (Hidden Layer)
for i in range(H1DIM):
for j in range(OUTDIM) :
d1[i] = d2[j] * w2[j][i] * (y1[i]*(1 - y1[i]))
###########################################
# Weight Adjustment
###########################################
# for Layer-2 (Output Layer)
for i in range(OUTDIM):
for j in range(H1DIM):
w2[i][j] += lrate * d2[i] * y1[j]
# for Layer-1 (Hidden Layer)
for i in range(H1DIM):
for j in range(INDIM):
w1[i][j] += lrate * d1[i] * x[p][j]
if (epoch % 1000) == 0:
print("pattern number[%d] : " % p)
print("target : ", "%7.2f" % t[p][0], "%7.2f" % t[p][1], "%7.2f" % t[p][2])
print("output : ", "%7.2f" % y2[0], "%7.2f" % y2[1], "%7.2f" % y2[2])
다층 퍼셉트론(출력부분)
for p in range(3):
for i in range(H1DIM):
out = 0.0
for j in range(INDIM):
out += w1[i][j] * x_t[p][j]
y1[i] = sigmoid(out)
# LAYER-2 (Output Layer)
# y1[0] = 1.0 ### bias
for i in range(OUTDIM):
out = 0.0
for j in range(H1DIM):
out += w2[i][j] * y1[j]
y2[i] = sigmoid(out)
print("pattern number[%d] : " % p)
print("output : ", "%7.2f" % y2[0], "%7.2f" % y2[1], "%7.2f" % y2[2])
epoch 로 10만번하게 하지 말고 cost가 0.1 또는 0.3 이하로 될 경우 exit 하게 해라
INDIM = 4 o o o (OUTDIM)
H1DIM = 5 o o o o o (H1DIM)
OUTDIM = 3 o o o o (INDIM)
pss 는 패턴 에러, tss 는 토탈 에러를 뜻함
일반적으로 OUTDIM < H1DIM < INDIM , ex) 4 < 10 < 26
pytorch 에서 np.array 는 torch.tensor 로 대체
w = torch.randn(INDIM, OUTDIM, requires_grad = true) 에서 requires_grad = true는 무조건 쓸것
b = torch.randn(INDIM, requires_grad = true)