服务器之家

服务器之家 > 正文

pytorch实现线性拟合方式

时间:2020-04-22 09:56     来源/作者:wangqianqianya

一维线性拟合

数据为y=4x+5加上噪音

结果:

pytorch实现线性拟合方式
?
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
import numpy as np
from mpl_toolkits.mplot3d import Axes3D
from matplotlib import pyplot as plt
from torch.autograd import Variable
import torch
from torch import nn
 
X = torch.unsqueeze(torch.linspace(-1, 1, 100), dim=1)
Y = 4*X + 5 + torch.rand(X.size())
 
class LinearRegression(nn.Module):
 def __init__(self):
  super(LinearRegression, self).__init__()
  self.linear = nn.Linear(1, 1) # 输入和输出的维度都是1
 def forward(self, X):
  out = self.linear(X)
  return out
 
model = LinearRegression()
criterion = nn.MSELoss()
optimizer = torch.optim.SGD(model.parameters(), lr=1e-2)
 
num_epochs = 1000
for epoch in range(num_epochs):
 inputs = Variable(X)
 target = Variable(Y)
 # 向前传播
 out = model(inputs)
 loss = criterion(out, target)
 
 # 向后传播
 optimizer.zero_grad() # 注意每次迭代都需要清零
 loss.backward()
 optimizer.step()
 
 if (epoch + 1) % 20 == 0:
  print('Epoch[{}/{}], loss:{:.6f}'.format(epoch + 1, num_epochs, loss.item()))
model.eval()
predict = model(Variable(X))
predict = predict.data.numpy()
plt.plot(X.numpy(), Y.numpy(), 'ro', label='Original Data')
plt.plot(X.numpy(), predict, label='Fitting Line')
plt.show()

多维:

?
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
from itertools import count
import torch
import torch.autograd
import torch.nn.functional as F
 
POLY_DEGREE = 3
def make_features(x):
 """Builds features i.e. a matrix with columns [x, x^2, x^3]."""
 x = x.unsqueeze(1)
 return torch.cat([x ** i for i in range(1, POLY_DEGREE+1)], 1)
 
 
W_target = torch.randn(POLY_DEGREE, 1)
b_target = torch.randn(1)
 
 
def f(x):
 return x.mm(W_target) + b_target.item()
def get_batch(batch_size=32):
 random = torch.randn(batch_size)
 x = make_features(random)
 y = f(x)
 return x, y
# Define model
fc = torch.nn.Linear(W_target.size(0), 1)
batch_x, batch_y = get_batch()
print(batch_x,batch_y)
for batch_idx in count(1):
 # Get data
 
 
 # Reset gradients
 fc.zero_grad()
 
 # Forward pass
 output = F.smooth_l1_loss(fc(batch_x), batch_y)
 loss = output.item()
 
 # Backward pass
 output.backward()
 
 # Apply gradients
 for param in fc.parameters():
  param.data.add_(-0.1 * param.grad.data)
 
 # Stop criterion
 if loss < 1e-3:
  break
 
 
def poly_desc(W, b):
 """Creates a string description of a polynomial."""
 result = 'y = '
 for i, w in enumerate(W):
  result += '{:+.2f} x^{} '.format(w, len(W) - i)
 result += '{:+.2f}'.format(b[0])
 return result
 
 
print('Loss: {:.6f} after {} batches'.format(loss, batch_idx))
print('==> Learned function:\t' + poly_desc(fc.weight.view(-1), fc.bias))
print('==> Actual function:\t' + poly_desc(W_target.view(-1), b_target))

以上这篇pytorch实现线性拟合方式就是小编分享给大家的全部内容了,希望能给大家一个参考,也希望大家多多支持服务器之家。

原文链接:https://blog.csdn.net/wangqianqianya/article/details/102764971

标签:

相关文章

热门资讯

2022年最旺的微信头像大全 微信头像2022年最新版图片
2022年最旺的微信头像大全 微信头像2022年最新版图片 2022-01-10
蜘蛛侠3英雄无归3正片免费播放 蜘蛛侠3在线观看免费高清完整
蜘蛛侠3英雄无归3正片免费播放 蜘蛛侠3在线观看免费高清完整 2021-08-24
背刺什么意思 网络词语背刺是什么梗
背刺什么意思 网络词语背刺是什么梗 2020-05-22
yue是什么意思 网络流行语yue了是什么梗
yue是什么意思 网络流行语yue了是什么梗 2020-10-11
暖暖日本高清免费中文 暖暖在线观看免费完整版韩国
暖暖日本高清免费中文 暖暖在线观看免费完整版韩国 2021-05-08
返回顶部