联邦学习/机器学习笔记（二）

一维线性回归+pytorch代码实现

import torch
import numpy as np
import torch.nn as nn
import torch.optim as optim
from torch.autograd import Variable
import matplotlib.pyplot as plt
import os
os.environ['KMP_DUPLICATE_LIB_OK'] = 'TRUE'

'''一维线性回归'''
# 建立数据
x_train = np.array([[3.3], [4.4], [5.5], [6.71], [6.93], [4.168],
                    [9.779], [6.182], [7.59], [2.167], [7.042],
                    [10.781], [5.313], [7.997], [3.1]], dtype = np.float32)

y_train = np.array([[1.7], [2.76], [2.09], [3.19], [1.694], [1.573],
                    [3.336], [2.596], [2.53], [1.221], [2.827],
                    [3.465], [1.65], [2.904], [1.3]], dtype = np.float32)
# 将数据转换换成tensor
x_train = torch.from_numpy(x_train)
y_train = torch.from_numpy(y_train)

# 建立模型
class LinearRegression(nn.Module):
    def __init__(self):
        super(LinearRegression, self).__init__()
        self.linear = nn.Linear(1, 1)  # 输入和输出是一维的

    def forward(self, x):
        out = self.linear(x)
        return out

if torch.cuda.is_available():
    model = LinearRegression().cuda()
else:
    model = LinearRegression()

# 定义损失函数和优化函数
criterion = nn.MSELoss()  # 使用均方误差
# optimizer = optim.SGD(model.parameters(), lr=1e-3, momentum=0.9)
optimizer = optim.SGD(model.parameters(), lr=0.01)

# 训练模型
num_epochs = 1500
for epoch in range(num_epochs):
    if torch.cuda.is_available():
        inputs = Variable(x_train).cuda()
        target = Variable(y_train).cuda()
    else:
        inputs = Variable(x_train)
        target = Variable(y_train)

    # 前向传播
    out = model(inputs)            #得到网络向前传播的结果，实际上model(inputs)等价于model.forward(inputs)
    loss = criterion(out, target)

    # 反向传播
    optimizer.zero_grad()    #归零梯度
    loss.backward()
    optimizer.step()

    if (epoch + 1) % 20 == 0:
        print('Epoch[{}/{}], loss: {: .6f}'.format(epoch + 1, num_epochs, loss.item()))

    # 预测结果
model.eval()
predict = model(Variable(x_train))
# print(predict)
predict = predict.data.numpy()
plt.plot(x_train.numpy(), y_train.numpy(), 'ro', label = 'Original data')
plt.plot(x_train.numpy(), predict, label = 'Fitting Line')
plt.show()

运行结果

C:\Users\Administrator\anaconda3\python.exe "D:/paper reading/code/learningcode/oneline.py"
Epoch[20/1500], loss:  0.232388
Epoch[40/1500], loss:  0.226161
Epoch[60/1500], loss:  0.220542
Epoch[80/1500], loss:  0.215471
Epoch[100/1500], loss:  0.210896
Epoch[120/1500], loss:  0.206768
Epoch[140/1500], loss:  0.203043
Epoch[160/1500], loss:  0.199682
Epoch[180/1500], loss:  0.196649
Epoch[200/1500], loss:  0.193912
Epoch[220/1500], loss:  0.191443
Epoch[240/1500], loss:  0.189215
Epoch[260/1500], loss:  0.187204
Epoch[280/1500], loss:  0.185390
Epoch[300/1500], loss:  0.183753
Epoch[320/1500], loss:  0.182276
Epoch[340/1500], loss:  0.180943
Epoch[360/1500], loss:  0.179741
Epoch[380/1500], loss:  0.178656
Epoch[400/1500], loss:  0.177676
Epoch[420/1500], loss:  0.176793
Epoch[440/1500], loss:  0.175996
Epoch[460/1500], loss:  0.175276
Epoch[480/1500], loss:  0.174627
Epoch[500/1500], loss:  0.174041
Epoch[520/1500], loss:  0.173513
Epoch[540/1500], loss:  0.173036
Epoch[560/1500], loss:  0.172606
Epoch[580/1500], loss:  0.172217
Epoch[600/1500], loss:  0.171867
Epoch[620/1500], loss:  0.171551
Epoch[640/1500], loss:  0.171266
Epoch[660/1500], loss:  0.171008
Epoch[680/1500], loss:  0.170776
Epoch[700/1500], loss:  0.170567
Epoch[720/1500], loss:  0.170377
Epoch[740/1500], loss:  0.170207
Epoch[760/1500], loss:  0.170053
Epoch[780/1500], loss:  0.169914
Epoch[800/1500], loss:  0.169789
Epoch[820/1500], loss:  0.169675
Epoch[840/1500], loss:  0.169573
Epoch[860/1500], loss:  0.169481
Epoch[880/1500], loss:  0.169398
Epoch[900/1500], loss:  0.169323
Epoch[920/1500], loss:  0.169256
Epoch[940/1500], loss:  0.169194
Epoch[960/1500], loss:  0.169139
Epoch[980/1500], loss:  0.169090
Epoch[1000/1500], loss:  0.169045
Epoch[1020/1500], loss:  0.169004
Epoch[1040/1500], loss:  0.168968
Epoch[1060/1500], loss:  0.168935
Epoch[1080/1500], loss:  0.168905
Epoch[1100/1500], loss:  0.168878
Epoch[1120/1500], loss:  0.168854
Epoch[1140/1500], loss:  0.168832
Epoch[1160/1500], loss:  0.168813
Epoch[1180/1500], loss:  0.168795
Epoch[1200/1500], loss:  0.168779
Epoch[1220/1500], loss:  0.168764
Epoch[1240/1500], loss:  0.168751
Epoch[1260/1500], loss:  0.168739
Epoch[1280/1500], loss:  0.168729
Epoch[1300/1500], loss:  0.168719
Epoch[1320/1500], loss:  0.168710
Epoch[1340/1500], loss:  0.168703
Epoch[1360/1500], loss:  0.168696
Epoch[1380/1500], loss:  0.168689
Epoch[1400/1500], loss:  0.168684
Epoch[1420/1500], loss:  0.168678
Epoch[1440/1500], loss:  0.168674
Epoch[1460/1500], loss:  0.168669
Epoch[1480/1500], loss:  0.168666
Epoch[1500/1500], loss:  0.168662

Process finished with exit code 0