import torch
import numpy as np
from torch import nn
from torch.autograd import variable
import visdom
import matplotlib.pyplot as plt
import random
viz = visdom.visdom(env='train')
loss_win = viz.line(np.arange(10))
#torch.cat()實現tensor拼接
def make_features(x):
x = x.unsqueeze(1)
return torch.cat([x ** i for i in range(1,4)],1)
#定義實際的函式
#unsqueeze將原來的tensor大小由3變成（3，1）
w_target = torch.floattensor([0.5,3,2.4]).unsqueeze(1)
b_target = torch.floattensor([0.9])
def f(x):
return x.mm(w_target) + b_target[0]
#定義每次的訓練集
#每次取batch_size個資料點，然後轉換成矩陣的形式
def get_batch(batch_size=32,random = none):
if random is none:
random = torch.randn(batch_size)
batch_size = random.size()[0]
x = make_features(random)
y = f(x)
return variable(x),variable(y)
#定義模型
# model = nn.linear(3,1)也可以
n = 3
class poly_model(nn.module) :
def __init__(self, n) :
super().__init__()
self.poly = nn.linear(n, 1)
def forward(self, x) :
return self.poly(x)
model = poly_model(n)
criterion = nn.mseloss()
optimizer = torch.optim.sgd(model.parameters(),lr=1e-3)
print('****')
epoch=0
while true:
batch_x,batch_y = get_batch()
#forward
output = model(batch_x)
loss = criterion(output,batch_y)
print_loss = loss.item()
if(epoch + 1) % 20 == 0:
print('epoch[{}],loss:'.format(epoch+1,loss.item()))
#backward
optimizer.zero_grad()
loss.backward()
optimizer.step()
epoch+=1
if(print_loss<1e-3):
print('loss:{} after {} batchs'.format(print_loss,epoch))
break
x = [random.randint(-200,200)*0.01 for i in range(20)]
x = np.array(sorted(x))
featurn_x,y = get_batch(random = torch.from_numpy(x).float())
y = y.data.numpy()
plt.plot(x,y,'ro',label='original data')
model.eval()
x_sample = np.arange(-2,2,0.01)
x, y = get_batch(random = torch.from_numpy(x_sample).float())
y = model(x)
y_sample = y.data.numpy()
plt.plot(x_sample,y_sample,label='fitting line')
plt.show()
多項式回歸
import numpy as np import matplotlib.pyplot as plt x np.random.uniform 3,3,size 100 x x.reshape 1,1 y 0.5 x 2 x 2 np.random.normal 0,1,100 plt.scatter...
多項式回歸
多項式回歸 import torch import numpy defmake features x 獲取 x,x 2,x 3 的矩陣 x x.unsqueeze 1 將一維資料變為 n，1 二維矩陣形式 return torch.cat x i for i in range 1 4 1 按列拼接 ...
多項式回歸
線性回歸適用於資料成線性分布的回歸問題，如果樣本是非線性分布，線性回歸就不再使用，轉而可以採用非線性模型進行回歸，比如多項式回歸 多項式回歸模型定義 與線性模型，多項式模型引入了高次項 y w 0 w1 x w2 x2 w 3x3 wnxn y w 0 w 1x w 2x 2 w 3x 3 w nx...