[Deep Learning] 활성화 함수 구현

Sigmoid

 

import numpy as np

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

sigmoid(4)

0.9820137900379085

import matplotlib.pyplot as plt

x = np.arange(-10, 10, 0.01)
y = sigmoid(x)
plt.plot(x, y)

 

ReLU

 

def relu(x):
    return np.maximum(0, x)
    
x = np.arange(-10, 10, 0.01)
y = relu(x)
plt.plot(x, y)

 

Softmax

 

def origin_softmax(x):
    f_x = np.exp(x) / np.sum(np.exp(x))
    return f_x

x = np.array([1.3, 5.1, 2.2, 0.7, 1.1])
y  = origin_softmax(x)

plt.plot(x, y)