code_02_moons.py

# -*- coding: utf-8 -*-
"""
@author: 代码医生工作室
@公众号：xiangyuejiqiren   （内有更多优秀文章及学习资料）
@来源: <PyTorch从深度学习到图神经网络>配套代码 
@配套代码技术支持：bbs.aianaconda.com  
Created on Fri Feb  1 00:07:25 2019
"""

import sklearn.datasets     #引入数据集
import torch
import numpy as np
import matplotlib.pyplot as plt
from code_03_moons_fun import LogicNet,plot_losses,predict,plot_decision_boundary



torch.manual_seed(0)
torch.cuda.manual_seed_all(0)

torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False

np.random.seed(0)           #设置随机数种子
X, Y = sklearn.datasets.make_moons(200,noise=0.2) #生成2组半圆形数据

arg = np.squeeze(np.argwhere(Y==0),axis = 1)     #获取第1组数据索引
arg2 = np.squeeze(np.argwhere(Y==1),axis = 1)#获取第2组数据索引

plt.title("moons data")
plt.scatter(X[arg,0], X[arg,1], s=100,c='b',marker='+',label='data1')
plt.scatter(X[arg2,0], X[arg2,1],s=40, c='r',marker='o',label='data2')
plt.legend()
plt.show()



model = LogicNet(inputdim=2,hiddendim=3,outputdim=2)#初始化模型
optimizer = torch.optim.Adam(model.parameters(), lr=0.01)#定义优化器


xt = torch.from_numpy(X).type(torch.FloatTensor)#将Numpy数据转化为张量
yt = torch.from_numpy(Y).type(torch.LongTensor)
epochs = 1000#定义迭代次数
losses = []#定义列表，用于接收每一步的损失值
for i in range(epochs):
    loss = model.getloss(xt,yt)
    losses.append(loss.item())
    optimizer.zero_grad()#清空之前的梯度
    loss.backward()#反向传播损失值
    optimizer.step()#更新参数



plot_losses(losses)


from sklearn.metrics import accuracy_score
print(accuracy_score(model.predict(xt),yt))


plot_decision_boundary(lambda x : predict(model,x) ,xt.numpy(), yt.numpy())