# week6

**Repository Path**: sika0819/week6

## Basic Information

- **Project Name**: week6
- **Description**: 第六周作业
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2019-05-21
- **Last Updated**: 2020-12-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README


载入数据


```python
#!/usr/bin/env python3
import numpy as np

samples_and = [
    [0, 0, 0],
    [1, 0, 0],
    [0, 1, 0],
    [1, 1, 1],
]


samples_or = [
    [0, 0, 0],
    [1, 0, 1],
    [0, 1, 1],
    [1, 1, 1],
]


samples_xor = [
    [0, 0, 0],
    [1, 0, 1],
    [0, 1, 1],
    [1, 1, 0],
]
```


```python

def perceptron(samples):
    w = np.array([1, 2])
    b = 0
    a = 1

    for i in range(10):# 训练轮数
        for j in range(4):# 数据（batch_size）
            x = np.array(samples[j][:2])
            y = 1 if np.dot(w, x) + b > 0 else 0 #y=wx+b计算出来的值
            d = np.array(samples[j][2])#真值

            delta_b = a*(d-y) #通过真值与计算值的差来倒推bias
            delta_w = a*(d-y)*x#通过真值与计算值的差来倒推2

            print('epoch {} sample {}  [{} {} {} {} {} {} {}]'.format(
                i, j, w[0], w[1], b, y, delta_w[0], delta_w[1], delta_b
            ))# 打印每一轮训练值
            w = w + delta_w
            b = b + delta_b
```


```python
print('logical and')
perceptron(samples_and)
```

    logical and
    epoch 0 sample 0  [1 2 0 0 0 0 0]
    epoch 0 sample 1  [1 2 0 1 -1 0 -1]
    epoch 0 sample 2  [0 2 -1 1 0 -1 -1]
    epoch 0 sample 3  [0 1 -2 0 1 1 1]
    epoch 1 sample 0  [1 2 -1 0 0 0 0]
    epoch 1 sample 1  [1 2 -1 0 0 0 0]
    epoch 1 sample 2  [1 2 -1 1 0 -1 -1]
    epoch 1 sample 3  [1 1 -2 0 1 1 1]
    epoch 2 sample 0  [2 2 -1 0 0 0 0]
    epoch 2 sample 1  [2 2 -1 1 -1 0 -1]
    epoch 2 sample 2  [1 2 -2 0 0 0 0]
    epoch 2 sample 3  [1 2 -2 1 0 0 0]
    epoch 3 sample 0  [1 2 -2 0 0 0 0]
    epoch 3 sample 1  [1 2 -2 0 0 0 0]
    epoch 3 sample 2  [1 2 -2 0 0 0 0]
    epoch 3 sample 3  [1 2 -2 1 0 0 0]
    epoch 4 sample 0  [1 2 -2 0 0 0 0]
    epoch 4 sample 1  [1 2 -2 0 0 0 0]
    epoch 4 sample 2  [1 2 -2 0 0 0 0]
    epoch 4 sample 3  [1 2 -2 1 0 0 0]
    epoch 5 sample 0  [1 2 -2 0 0 0 0]
    epoch 5 sample 1  [1 2 -2 0 0 0 0]
    epoch 5 sample 2  [1 2 -2 0 0 0 0]
    epoch 5 sample 3  [1 2 -2 1 0 0 0]
    epoch 6 sample 0  [1 2 -2 0 0 0 0]
    epoch 6 sample 1  [1 2 -2 0 0 0 0]
    epoch 6 sample 2  [1 2 -2 0 0 0 0]
    epoch 6 sample 3  [1 2 -2 1 0 0 0]
    epoch 7 sample 0  [1 2 -2 0 0 0 0]
    epoch 7 sample 1  [1 2 -2 0 0 0 0]
    epoch 7 sample 2  [1 2 -2 0 0 0 0]
    epoch 7 sample 3  [1 2 -2 1 0 0 0]
    epoch 8 sample 0  [1 2 -2 0 0 0 0]
    epoch 8 sample 1  [1 2 -2 0 0 0 0]
    epoch 8 sample 2  [1 2 -2 0 0 0 0]
    epoch 8 sample 3  [1 2 -2 1 0 0 0]
    epoch 9 sample 0  [1 2 -2 0 0 0 0]
    epoch 9 sample 1  [1 2 -2 0 0 0 0]
    epoch 9 sample 2  [1 2 -2 0 0 0 0]
    epoch 9 sample 3  [1 2 -2 1 0 0 0]


可以看出第二轮以后就找出了结果，w1=1,w2=2,b=-2


```python
print('logical or')
perceptron(samples_or)
```

    logical or
    epoch 0 sample 0  [1 2 0 0 0 0 0]
    epoch 0 sample 1  [1 2 0 1 0 0 0]
    epoch 0 sample 2  [1 2 0 1 0 0 0]
    epoch 0 sample 3  [1 2 0 1 0 0 0]
    epoch 1 sample 0  [1 2 0 0 0 0 0]
    epoch 1 sample 1  [1 2 0 1 0 0 0]
    epoch 1 sample 2  [1 2 0 1 0 0 0]
    epoch 1 sample 3  [1 2 0 1 0 0 0]
    epoch 2 sample 0  [1 2 0 0 0 0 0]
    epoch 2 sample 1  [1 2 0 1 0 0 0]
    epoch 2 sample 2  [1 2 0 1 0 0 0]
    epoch 2 sample 3  [1 2 0 1 0 0 0]
    epoch 3 sample 0  [1 2 0 0 0 0 0]
    epoch 3 sample 1  [1 2 0 1 0 0 0]
    epoch 3 sample 2  [1 2 0 1 0 0 0]
    epoch 3 sample 3  [1 2 0 1 0 0 0]
    epoch 4 sample 0  [1 2 0 0 0 0 0]
    epoch 4 sample 1  [1 2 0 1 0 0 0]
    epoch 4 sample 2  [1 2 0 1 0 0 0]
    epoch 4 sample 3  [1 2 0 1 0 0 0]
    epoch 5 sample 0  [1 2 0 0 0 0 0]
    epoch 5 sample 1  [1 2 0 1 0 0 0]
    epoch 5 sample 2  [1 2 0 1 0 0 0]
    epoch 5 sample 3  [1 2 0 1 0 0 0]
    epoch 6 sample 0  [1 2 0 0 0 0 0]
    epoch 6 sample 1  [1 2 0 1 0 0 0]
    epoch 6 sample 2  [1 2 0 1 0 0 0]
    epoch 6 sample 3  [1 2 0 1 0 0 0]
    epoch 7 sample 0  [1 2 0 0 0 0 0]
    epoch 7 sample 1  [1 2 0 1 0 0 0]
    epoch 7 sample 2  [1 2 0 1 0 0 0]
    epoch 7 sample 3  [1 2 0 1 0 0 0]
    epoch 8 sample 0  [1 2 0 0 0 0 0]
    epoch 8 sample 1  [1 2 0 1 0 0 0]
    epoch 8 sample 2  [1 2 0 1 0 0 0]
    epoch 8 sample 3  [1 2 0 1 0 0 0]
    epoch 9 sample 0  [1 2 0 0 0 0 0]
    epoch 9 sample 1  [1 2 0 1 0 0 0]
    epoch 9 sample 2  [1 2 0 1 0 0 0]
    epoch 9 sample 3  [1 2 0 1 0 0 0]


可以看出第一轮就找出了结果，w1=1,w2=2,b=0


```python
print('logical xor')
perceptron(samples_xor)
```

    logical xor
    epoch 0 sample 0  [1 2 0 0 0 0 0]
    epoch 0 sample 1  [1 2 0 1 0 0 0]
    epoch 0 sample 2  [1 2 0 1 0 0 0]
    epoch 0 sample 3  [1 2 0 1 -1 -1 -1]
    epoch 1 sample 0  [0 1 -1 0 0 0 0]
    epoch 1 sample 1  [0 1 -1 0 1 0 1]
    epoch 1 sample 2  [1 1 0 1 0 0 0]
    epoch 1 sample 3  [1 1 0 1 -1 -1 -1]
    epoch 2 sample 0  [0 0 -1 0 0 0 0]
    epoch 2 sample 1  [0 0 -1 0 1 0 1]
    epoch 2 sample 2  [1 0 0 0 0 1 1]
    epoch 2 sample 3  [1 1 1 1 -1 -1 -1]
    epoch 3 sample 0  [0 0 0 0 0 0 0]
    epoch 3 sample 1  [0 0 0 0 1 0 1]
    epoch 3 sample 2  [1 0 1 1 0 0 0]
    epoch 3 sample 3  [1 0 1 1 -1 -1 -1]
    epoch 4 sample 0  [0 -1 0 0 0 0 0]
    epoch 4 sample 1  [0 -1 0 0 1 0 1]
    epoch 4 sample 2  [1 -1 1 0 0 1 1]
    epoch 4 sample 3  [1 0 2 1 -1 -1 -1]
    epoch 5 sample 0  [0 -1 1 1 0 0 -1]
    epoch 5 sample 1  [0 -1 0 0 1 0 1]
    epoch 5 sample 2  [1 -1 1 0 0 1 1]
    epoch 5 sample 3  [1 0 2 1 -1 -1 -1]
    epoch 6 sample 0  [0 -1 1 1 0 0 -1]
    epoch 6 sample 1  [0 -1 0 0 1 0 1]
    epoch 6 sample 2  [1 -1 1 0 0 1 1]
    epoch 6 sample 3  [1 0 2 1 -1 -1 -1]
    epoch 7 sample 0  [0 -1 1 1 0 0 -1]
    epoch 7 sample 1  [0 -1 0 0 1 0 1]
    epoch 7 sample 2  [1 -1 1 0 0 1 1]
    epoch 7 sample 3  [1 0 2 1 -1 -1 -1]
    epoch 8 sample 0  [0 -1 1 1 0 0 -1]
    epoch 8 sample 1  [0 -1 0 0 1 0 1]
    epoch 8 sample 2  [1 -1 1 0 0 1 1]
    epoch 8 sample 3  [1 0 2 1 -1 -1 -1]
    epoch 9 sample 0  [0 -1 1 1 0 0 -1]
    epoch 9 sample 1  [0 -1 0 0 1 0 1]
    epoch 9 sample 2  [1 -1 1 0 0 1 1]
    epoch 9 sample 3  [1 0 2 1 -1 -1 -1]


可以看出结果一直无法被找到。是因为感知器只能处理线性问题

异或在二维上的表现：

![image](异或感知器.jpg)

可以看出，二维空间里无法找到一条直线把这两个角色区分开