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

def sigmoid(x):
  return 1/ (1+np.exp(-x))

x = np.linspace(-10, 10, 100) 
  
plt.plot(x, sigmoid(x)) 
plt.xlabel("x") 
plt.ylabel("Sigmoid(X)") 
  
plt.show()

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

def sigmoid(x):
  return 1/ (1+np.exp(-x))

# 시그모이드 함수의 도함수 유도 예시
# <https://towardsdatascience.com/derivative-of-the-sigmoid-function-536880cf918e>
def sigmoid_prime(x):
    sig = sigmoid(x)
    return sig * (1 - sig)

# 시그모이드 함수 그래프 생성
x_range = np.arange(-10., 10., 0.2)
y_range = np.array([sigmoid(x) for x in x_range])
y_prime = np.array([sigmoid_prime(x) for x in x_range])

plt.plot(x_range, y_range, label='sigmoid') 
plt.plot(x_range, y_prime, label='sigmoid_prime')
plt.legend()
plt.show();