3장 신경망 코드들 모음

1) 계단함수 구현하기 - 간단한 버전

def step_func(x):
    if x>0:
        print("1")
        return 1
    elif x<=0 :
        print("0")
        return 0
 
step_func(3)
step_func(-1)

 

2) 계단함수 구현하기 - 배열 지원 가능버전 : np를 반드시 활용해야 함

import numpy as np
 
def step_func1(x) :
    y = x > 0
    print(y.astype(np.int))
    return y.astype(np.int)
 
def step_func2(x) :
    y = x > 0
    print(y.astype(np.int))
    return y.astype(np.int)
 
step_func2(np.array([-1, 3]))
step_func1(np.array([4]))

3) 계단함수 그래프 그리기 - matplotlib 활용하기

** 함수 구현 부분에서, 굳이 np.array(~)를 할 필요가 없다. 알아서 array형식으로 출력됨

import numpy as np
import matplotlib.pylab as plt
 
def step_func(x):
    return np.array(x>0, dtype=np.int)
 
x=np.arange(-5.0, 5.0, 0.1)
y=step_func(x)
 
plt.plot(x,y)
plt.ylim(-0.1, 1.1)
plt.show()
 

4) sigmoid 함수 구현

import numpy as np
 
def sigmoid(x):
    print(1/(1+np.exp(-x)))
    return 1/(1+np.exp(-x))
 
sigmoid(np.array([-1, 1, 2]))
 

5) sigmoid 함수 matplotlib로 표현 - 이런 식으로 함수를 구현해도, 출력되는 값은 array형식으로 나온다

import numpy as np
import matplotlib.pylab as plt
 
def sigmoid(x):
    return 1/(1+np.exp(-x))
 
x = np.arange(-5, 5, 0.1)
y = sigmoid(x)
 
plt.plot(x,y)
plt.ylim(-0.1, 1.1)
plt.show()
 

6) ReLU 함수 - 기본 구현

import numpy as np
 
def ReLU(x):
    print(np.maximum(x,0))
    return np.maximum(x,0)
 
ReLU(np.arange(-4,5,2));

7) 다차원 배열 계산

import numpy as np
 
A = np.array([[1,2],[3,4]])
B = np.array([[5,6],[7,8]])
 
print(np.dot(A,B))

8) 신경망의 내적

import numpy as np
 
def neural(x,w):
    b=-2
    print(np.dot(x,w)+b)
    return np.dot(x,w)+b
 
x=np.array([1,2])
w=np.array([[1,3,5],[2,4,6]])
 
neural(x,w)
 
 
 

9) 3충 신경망 구현

import numpy as np
 
def sigmoid(x):
    return 1/(1+np.exp(-x))
 
def identity_func(x):
    return x
 
def init_network():
    network={}
    network['w1']=np.array([[0.1, 0.2, 0.5], [0.2, 0.4, 0.6]])
    network['b1']=np.array([0.1, 0.2, 0.3])
    network['w2']=np.array([[0.1, 0.4], [0.2, 0.5], [0.3, 0.6]])
    network['b2']=np.array([[0.1], [0.2]])
    network['w3']=np.array([[0.1, 0.3], [0.2, 0.4]])
    network['b3']=np.array([0.1, 0.2])
 
    return network
 
def forward(network, x):
    w1, w2, w3 = network['w1'], network['w2'], network['w3']
    b1, b2, b3 = network['b1'], network['b2'], network['b3']
 
    a1=np.dot(x,w1)+b1
    z1=sigmoid(a1)
    a2=np.dot(z1,w2)+b2
    z2=sigmoid(a2)
    a3=np.dot(z2,w3)+b3
    y=identity_func(a3)
 
    return y
 
network = init_network()
 
x=np.array([1.0, 0.5])
y = forward(network,x)
 
print(y)

10) 소프트맥스 함수 구현 - 기본형

import numpy as np
 
def softmax(a):
    exp_a = np.exp(a)
    sum_exp_a = sum(exp_a)
    
    y=exp_a=sum_exp_a
    
    return y
 
 

11) 소프트맥스 함수 구현 - 수정 (좀 더 표현하기 쉬운 방식)

import numpy as np
 
def softmax(a):
    c=np.max(a)
    
    exp_a = np.exp(a-c)
    sum_exp_a = sum(exp_a)
    
    y=exp_a/sum_exp_a
 
    return y
 
 

12) 소프트맥스 함수의 특징 - 합이 1

import numpy as np
 
def softmax(a):
    c=np.max(a)
 
    exp_a = np.exp(a-c)
    sum_exp_a = sum(exp_a)
 
    y=exp_a/sum_exp_a
 
    return y
 
a=np.array([0.3, 2.9, 4.0])
part=softmax(a)
 
print(np.sum(part))

13) MNIST 데이터셋 사용하기 - 

** dataset.mnist 만드는 방법 : dataset이라는 폴더를 따로 만든 뒤, mnist.py 파일을 넣어줌.

   그걸 현재 내 py 경로에다가 넣어주면 됨 (Script나 include 디렉토리에 넣는거 아님)

import sys, os
sys.path.append(os.pardir)
from dataset.mnist import load_mnist
 
(x_train, t_train),(x_test, t_test)=load_mnist(flatten=True,normalize=False)
 
print(x_train.shape)
print(t_train.shape)
print(x_test.shape)
print(t_test.shape)

14) MNIST 데이터셋 출력하기

import sys, os
sys.path.append(os.pardir)
from dataset.mnist import load_mnist
import numpy as np
from PIL import Image
 
def img_show(img):
    pil_img=Image.fromarray(np.uint8(img))
    pil_img.show()
 
(x_train, t_train),(x_test, t_test)=load_mnist(flatten=True,normalize=False)
 
img = x_train[0]
label = t_train[0]
print(label)
 
print(img.shape)
img = img.reshape(28, 28)
print(img.shape)
 
img_show(img)
 

15) 신경망의 추론 처리 

import pickle
from dataset.mnist import load_mnist
import numpy as np
 
def get_data():
    (x_train, t_train), (x_test, t_test) = load_mnist(normalize=True, flatten=True, one_hot_label=False)
 
    return x_test, t_test
 
def init_network():
    with open("sample_weight.pkl",'rb') as f:
        network = pickle.load(f)
 
    return network
 
def sigmoid(x):
    return 1/(1+np.exp(-x))
 
def softmax(x):
    c = max(x)
 
    exp_x = np.exp(x-c)
    sum_exp_x = np.sum(exp_x)
 
    return exp_x/sum_exp_x
 
def predict(network, x):
    w1, w2, w3 = network['w1'], network['w2'], network['w3']
    b1, b2, b3 = network['b1'], network['b2'], network['b3']
 
    a1 = np.dot(x, w1) + b1
    z1 = sigmoid(a1)
    a2 = np.dot(z1, w2) + b2
    z2 = sigmoid(a2)
    a3 = np.dot(z2, w3) + b3
    y =softmax(a3)
    
    return y

16) 신경망 추론 처리 - 정확도 처리

import pickle
from dataset.mnist import load_mnist
import numpy as np
 
 
def get_data():
    (x_train, t_train), (x_test, t_test) = load_mnist(normalize=True, flatten=True, one_hot_label=False)
 
    return x_test, t_test
 
 
def init_network():
    with open("sample_weight.pkl", 'rb') as f:
        network = pickle.load(f)
 
    return network
 
 
def sigmoid(x):
    return 1 / (1 + np.exp(-x))
 
 
def softmax(x):
    c = max(x)
 
    exp_x = np.exp(x - c)
    sum_exp_x = np.sum(exp_x)
 
    return exp_x / sum_exp_x
 
 
def predict(network, x):
    w1, w2, w3 = network['W1'], network['W2'], network['W3']
    b1, b2, b3 = network['b1'], network['b2'], network['b3']
 
    a1 = np.dot(x, w1) + b1
    z1 = sigmoid(a1)
    a2 = np.dot(z1, w2) + b2
    z2 = sigmoid(a2)
    a3 = np.dot(z2, w3) + b3
    y = softmax(a3)
 
    return y
 
 
x, t = get_data()
network = init_network()
 
print(network)
 
accuracy_cnt = 0
 
for i in range(len(x)):
    y = predict(network, x[i])
    p = np.argmax(y)
 
    if p == t[i]:
        accuracy_cnt += 1
 
print("Accuracy:" + str(float(accuracy_cnt)/len(x)))