# 경사하강법(수치미분)
import os
import sys
from pathlib import Path
try:
sys.path.append(os.path.join(Path(os.getcwd()).parent, 'lib'))
from common import mean_squares_error, gradient_descent
except ImportError:
print('Library Module Can Not Found')
import numpy as np
# data
times = [2, 4, 6, 8]
scores = [81, 93, 91, 97]
# 경사하강법
result = gradient_descent(mean_squares_error,
np.array([0., 0.]),
epoch=5000,
data_training=(times, scores))
print(f'직선 y = {result[0]}x + {result[1]}')
# 평균제곱오차
print(f'오차(평균제곱오차):{mean_squares_error(np.array(result), (times, scores))}')
def loss(x, data_training):
data_in, data_out = data_training
e = data_out * np.log(sigmoid(x[0] * data_in + x[1])) + (
1 - data_out) * np.log(1 - sigmoid(x[0] * data_in + x[1]))
return -1 * np.mean(e)
# data
times = np.array([2, 4, 6, 8, 10, 12, 14])
passed = np.array([0, 0, 0, 1, 1, 1, 1])
# 경사하강법
params = gradient_descent(loss,
np.array([0., 0.]),
lr=0.5,
epoch=50000,
data_training=(times, passed))
# graph
x = np.arange(0, 15, 0.1)
y = sigmoid(params[0] * x + params[1])
fig, splt = plt.subplots()
splt.scatter(times, passed)
splt.plot(x, y)
plt.show()
# predict
x_p = 7
# 경사하강법
import sys
import numpy as np
import os
from pathlib import Path
try :
sys.path.append(os.path.join(Path(os.getcwd()).parent, 'library'))
from common import gradient_descent
except :
print('Library Module Can Not Found')
def f(x) :
print(np.sum(x**2, axis=0))
return np.sum(x**2, axis=0)
# return x**2
# gradient_descent(f, np.array([-3., 4.]), lr=0.1) # 소수점을 붙여줘야 한다. 아니면 다른 변수형인 int형이 되어 Error.
# gradient_descent(f, np.array([-3., 4.]), lr=10)
gradient_descent(f, np.array([-3., -2]), lr=0.001, epoch=10)
# epoch나 lr의 값을 지정해주지 않으면 선언된 함수의 수로 진행된다.
# lr(learning rate : 학습률) : 갱신하는 양을 결정하는 변수. 얼마만큼 데이터를 이동시켜 최소 기울기를 찾을지 결정.
# epoch : 경사하강법의 반복 횟수.
import os
import sys
from pathlib import Path
import numpy as np
try:
sys.path.append(os.path.join(Path(os.getcwd()).parent, 'lib'))
from common import gradient_descent
except ImportError:
print('Library Module Can Not Found')
def f(x):
return np.sum((x + 2)**2 + 1, axis=0)
gradient_descent(f, np.array([-3., 4.]), lr=0.1)
# gradient_descent(f, np.array([-3., 4.]), lr=10)
# gradient_descent(f, np.array([-3., 4.]), lr=0.001)
#기울기(gradient)
import os
import sys
from pathlib import Path
import numpy as np
try:
sys.path.append(os.path.join(Path(os.getcwd()).parent, 'lib'))
from common import gradient_descent
except ImportError:
print('Library Module Can Not Found')
def f(x):
return np.sum(x**2 ,axis=0)
gradient_descent(f, np.array([-3., 4.]), lr=0.1)
gradient_descent(f, np.array([-3., 4.]), lr=0.001, epoch=1000000)
