PyTorch线性回归和逻辑回归实战示例
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2020-09-20
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PyTorch线性回归和逻辑回归实战示例线性回归和逻辑回归实战示例
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线性回归实战线性回归实战
使用PyTorch定义线性回归模型一般分以下几步：
1.设计网络架构
2.构建损失函数(loss)和优化器(optimizer)
3.训练(包括前馈(forward)、反向传播(backward)、更新模型参数(update))
#author:yuquanle
#data:2018.2.5
#Study of LinearRegression use PyTorch
import torch
from torch.autograd import Variable
# train data
x_data = Variable(torch.Tensor([[1.0], [2.0], [3.0]]))
y_data = Variable(torch.Tensor([[2.0], [4.0], [6.0]]))
class Model(torch.nn.Module):
def __init__(self):
super(Model, self).__init__()
self.linear = torch.nn.Linear(1, 1) # One in and one out
def forward(self, x):
y_pred = self.linear(x)
return y_pred
# our model
model = Model()
criterion = torch.nn.MSELoss(size_average=False) # Defined loss function
optimizer = torch.optim.SGD(model.parameters(), lr=0.01) # Defined optimizer
# Training: forward, loss, backward, step
# Training loop
for epoch in range(50):
# Forward pass
y_pred = model(x_data)
# Compute loss
loss = criterion(y_pred, y_data)
print(epoch, loss.data[0])
# Zero gradients
optimizer.zero_grad()
# perform backward pass
loss.backward()
# update weights
optimizer.step()
# After training
hour_var = Variable(torch.Tensor([[4.0]]))
print("predict (after training)", 4, model.forward(hour_var).data[0][0])
迭代十次打印结果：
0 123.87958526611328
1 55.19491195678711
2 24.61777114868164
3 11.005026817321777
4 4.944361686706543
5 2.2456750869750977
6 1.0436556339263916
7 0.5079189538955688
8 0.2688019871711731
9 0.16174012422561646
predict (after training) 4 7.487752914428711
loss还在继续下降，此时输入4得到的结果还不是预测的很准
当迭代次数设置为50时：
0 35.38422393798828
5 0.6207122802734375
10 0.012768605723977089
15 0.0020055510103702545
20 0.0016929294215515256
25 0.0015717096393927932
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