自己动手实现一个深度学习算法——一、感知机的实现

感知机接收多个输入信号，输出一个信号。

感知机的多个输入信号都有各自固有的权重，这些权重发挥着控制各个信号的重要性的作用。也就是说，权重越大，对应该权重的信号的重要性就越高。

1.感知机的实现

1）与门的实现

# coding: utf-8
import numpy as np


def AND(x1, x2):
    x = np.array([x1, x2])
    w = np.array([0.5, 0.5])
    b = -0.7
    tmp = np.sum(w*x) + b
    if tmp <= 0:
        return 0
    else:
        return 1

if __name__ == '__main__':
    for xs in [(0, 0), (1, 0), (0, 1), (1, 1)]:
        y = AND(xs[0], xs[1])
        print(str(xs) + " -> " + str(y))

2）或门的实现

# coding: utf-8
import numpy as np

def OR(x1, x2):
    x = np.array([x1, x2])
    w = np.array([0.5, 0.5])
    b = -0.2
    tmp = np.sum(w*x) + b
    if tmp <= 0:
        return 0
    else:
        return 1

if __name__ == '__main__':
    for xs in [(0, 0), (1, 0), (0, 1), (1, 1)]:
        y = OR(xs[0], xs[1])
        print(str(xs) + " -> " + str(y))

3）非与门实现

# coding: utf-8
import numpy as np

def NAND(x1, x2):
    x = np.array([x1, x2])
    w = np.array([-0.5, -0.5])
    b = 0.7
    tmp = np.sum(w*x) + b
    if tmp <= 0:
        return 0
    else:
        return 1

if __name__ == '__main__':
    for xs in [(0, 0), (1, 0), (0, 1), (1, 1)]:
        y = NAND(xs[0], xs[1])
        print(str(xs) + " -> " + str(y))

4）异或门的实现

# coding: utf-8
from and_gate import AND
from or_gate import OR
from nand_gate import NAND


def XOR(x1, x2):
    s1 = NAND(x1, x2)
    s2 = OR(x1, x2)
    y = AND(s1, s2)
    return y

if __name__ == '__main__':
    for xs in [(0, 0), (1, 0), (0, 1), (1, 1)]:
        y = XOR(xs[0], xs[1])
        print(str(xs) + " -> " + str(y))