人工神经网络实验：第一次上机-Toy模板网

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pratice03：

1.问题描述：

就是通过设置只有一个hidden层的神经网络，对一个平面的灰面和白面进行分类

2.完整代码：

# 可提供解题和验证代码，自行产生数据

import torch
import torch.nn as nn
import torch.nn.functional as F
from torch import tensor
from torch.nn import Parameter


class Act_abs(nn.Module):
    def __init__(self):
        super(Act_abs, self).__init__()

    def forward(self, x):
        x[abs(x) >= 1] = 1
        x[abs(x) < 1] = 0
        return x

class Act_v(nn.Module):
    def __init__(self):
        super(Act_v, self).__init__()

    def forward(self, x):
        x[x < 1] = 0
        x[x >= 1] = 1
        return x

class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.lin1 = nn.Linear(2,2,bias =False)
        self.lin2 = nn.Linear(2,1,bias =False)
        self.lin1.weight = Parameter(tensor([[ 1.0,  0.0], [0.0, 1.0]]))
        self.lin2.weight = Parameter(tensor([[ 1.0,1.0]]))
        self.act1 = Act_abs()
        self.act2 = Act_abs()

    def forward(self, x):
        y = self.lin1(x)
        y = self.act1(y)
        y = self.lin2(y)
        y = self.act2(y)
        return y

if __name__ == '__main__':
    net = Net()
    # 第一类
    dotIn = [tensor([0.0,0.0]),tensor([0.2,0.2]),tensor([-0.2,-0.2]),tensor([0.2,-0.2]),tensor([-0.2,0.2])]
    # 边界点（第二类）
    dotEdge = [tensor([1.0,1.0]),tensor([-1.0,1.0]),tensor([1.0,-1.0]),tensor([-1.0,-1.0]),tensor([1.0, 0.2]), tensor([-1.0, 0.2]), tensor([1.0, -0.2]), tensor([-1.0, -0.2])]
    # 第二类
    dotOut = [tensor([1.5, 0.2]), tensor([-1.5, 0.2]), tensor([1.5, -0.2]), tensor([-1.5, -0.2])]
    print('----------内部点--------------')
    for x in dotIn:
        pre = net(x)
        if pre[0] == 0.0:
            print('第一类')
        else:
            print('第二类')
    print('----------边界点--------------')
    for x in dotEdge:
        pre = net(x)
        if pre[0] == 0.0:
            print('第一类')
        else:
            print('第二类')
    print('----------外部点--------------')
    for x in dotOut:
        pre = net(x)
        if pre[0] == 0.0:
            print('第一类')
        else:
            print('第二类')

    print('-----------------生成随机数据------------------')
    acc = 0
    rand_num = 10**4
    for i in range(rand_num):
        X = (torch.rand(2) - 0.5) * 4  # [-2, 2]
        if abs(X[0]) > 1 or abs(X[1]) > 1:
            y = tensor([1])
        else:
            y = tensor([0])

        pre = net(X)

        if pre == y:
            acc+=1

    print('识别准确率Acc: '+ str((acc/rand_num)*100) + '%')

3.代码分析过程：

(1) 人工神经网络实验：第一次上机,人工智能,深度学习,pytorch,python