本篇文章为大家展示了利用Python怎么编写一个感知器的逻辑电路,内容简明扼要并且容易理解,绝对能使你眼前一亮,通过这篇文章的详细介绍希望你能有所收获。
pip install pytest
通过一层感知器就可以实现与门、与非门、或门。
先写测试代码 test_perception.py:
from perception import and_operate, nand_operate, or_operate
def test_and_operate():
"""
测试与门
:return:
"""
assert and_operate(1, 1) == 1
assert and_operate(1, 0) == 0
assert and_operate(0, 1) == 0
assert and_operate(0, 0) == 0
def test_nand_operate():
"""
测试与非门
:return:
"""
assert nand_operate(1, 1) == 0
assert nand_operate(1, 0) == 1
assert nand_operate(0, 1) == 1
assert nand_operate(0, 0) == 1
def test_or_operate():
"""
测试或门
:return:
"""
assert or_operate(1, 1) == 1
assert or_operate(1, 0) == 1
assert or_operate(0, 1) == 1
assert or_operate(0, 0) == 0
写完测试代码,后面直接输入命令 pytest -v 即可测试代码。
这三个门的权重和偏置是根据人的直觉或者画图得到的,并且不是唯一的。以下是简单的实现,在 perception.py 中写上:
import numpy as np
def step_function(x):
"""
阶跃函数
:param x:
:return:
"""
if x <= 0:
return 0
else:
return 1
def and_operate(x1, x2):
"""
与门
:param x1:
:param x2:
:return:
"""
x = np.array([x1, x2])
w = np.array([0.5, 0.5])
b = -0.7
return step_function(np.sum(w * x) + b)
def nand_operate(x1, x2):
"""
与非门
:param x1:
:param x2:
:return:
"""
x = np.array([x1, x2])
w = np.array([-0.5, -0.5])
b = 0.7
return step_function(np.sum(w * x) + b)
def or_operate(x1, x2):
"""
或门
:param x1:
:param x2:
:return:
"""
x = np.array([x1, x2])
w = np.array([0.5, 0.5])
b = -0.3
return step_function(np.sum(w * x) + b)
运行 pytest -v 确认测试通过。
========================================================================== test session starts ===========================================================================
platform darwin -- Python 3.6.8, pytest-5.1.2, py-1.8.0, pluggy-0.12.0 -- /Users/mac/.virtualenvs/work/bin/python3
...
collected 3 items
test_perception.py::test_and_operate PASSED [ 33%]
test_perception.py::test_nand_operate PASSED [ 66%]
test_perception.py::test_or_operate PASSED [100%]
=========================================================================== 3 passed in 0.51s ============================================================================
如上图所示,由于异或门不是线性可分的,因此需要多层感知器的结构。
使用两层感知器可以实现异或门。
修改 test_perception.py 文件,加入异或门的测试代码 :
from perception import and_operate, nand_operate, or_operate, xor_operate
以及
def test_xor_operate():
"""
测试异或门
:return:
"""
assert xor_operate(1, 1) == 0
assert xor_operate(1, 0) == 1
assert xor_operate(0, 1) == 1
assert xor_operate(0, 0) == 0
在 perception.py 文件里加入异或门的函数:
def xor_operate(x1, x2):
"""
异或门
:param x1:
:param x2:
:return:
"""
s1 = nand_operate(x1, x2)
s2 = or_operate(x1, x2)
return and_operate(s1, s2)
我们通过与非门和或门的线性组合实现了异或门。
运行命令 pytest -v 测试成功。
========================================================================== test session starts ===========================================================================
platform darwin -- Python 3.6.8, pytest-5.1.2, py-1.8.0, pluggy-0.12.0 -- /Users/mac/.virtualenvs/work/bin/python3
...
collected 4 items
test_perception.py::test_and_operate PASSED [ 25%]
test_perception.py::test_nand_operate PASSED [ 50%]
test_perception.py::test_or_operate PASSED [ 75%]
test_perception.py::test_xor_operate PASSED [100%]
=========================================================================== 4 passed in 0.60s ============================================================================
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