python-pytorch基础之神经网络回归

定义数据集

import torch
import random

定义函数

# 生成数据
def get_rancledata():
    width=random.random()
    height=random.random()
    
    s=width*height
    
    return width,height,s

get_rancledata()

(0.1571327616035657, 0.5335562021159256, 0.08383915950918565)

生成数据集

class dataset(torch.utils.data.Dataset):
    def __init__(self):
        pass
    
    def __len__(self):
        return 1000
    
    def __getitem__(self,i):
        
        width,height,s=get_rancledata()
        x=torch.FloatTensor([width,height])
        
        # 这里注意也是需要转换成tensor的，否则训练会报类型错误
        y=torch.FloatTensor([s])
        
        return x,y
    
dataset=dataset()

len(dataset),dataset[4999]

(1000, (tensor([0.2137, 0.6781]), tensor([0.1449])))

使用Dataloader加载dataset

loader=torch.utils.data.DataLoader(
    dataset=dataset,
    shuffle=True,
    batch_size=9
)

len(loader),next(iter(loader))

(112,
 [tensor([[0.7389, 0.1202],
          [0.5764, 0.7888],
          [0.7244, 0.0229],
          [0.5102, 0.0755],
          [0.8550, 0.4998],
          [0.9992, 0.5890],
          [0.1704, 0.0162],
          [0.2132, 0.9157],
          [0.7946, 0.8907]]),
  tensor([[0.0888],
          [0.4546],
          [0.0166],
          [0.0385],
          [0.4273],
          [0.5885],
          [0.0028],
          [0.1953],
          [0.7077]])])

定义神经网络

定义

# 输入两个，输出一个
class Model(torch.nn.Module):
    def __init__(self):
        super().__init__()
            
        self.fb=torch.nn.Sequential(
            torch.nn.Linear(in_features=2,out_features=32),
            torch.nn.ReLU(),
            torch.nn.Linear(in_features=32,out_features=32),
            torch.nn.ReLU(),
            torch.nn.Linear(in_features=32,out_features=1)
        )
        
    def forward(self,x):
        return self.fb(x)

实例化

model=Model()

torch.rand(4,2)

tensor([[0.4510, 0.1455],
        [0.4963, 0.2974],
        [0.9480, 0.9913],
        [0.9053, 0.4228]])

查看是否是输出的一个

# 测试
model(torch.rand(8,2)).shape

torch.Size([8, 1])

训练

编写trian方法

def train():
    # 选择损失函数
    loss_fn=torch.nn.MSELoss()
    # 选择优化器
    optimizer=torch.optim.Adam(model.parameters(),lr=1e-4)
    
    #遍历多少轮
    for epoch in range(100):
        
        #全量遍历
        for i ,(x,y) in enumerate(loader):
            #计算损失
            #计算梯度
            #优化参数
            #优化梯度清零

            out=model(x)
            loss=loss_fn(out,y)
            loss.backward()
            
            optimizer.step()
            optimizer.zero_grad()
    
            if epoch % 20 ==0:
                print(epoch,loss.item())
    torch.save(model,"huigui.model")

训练并保存模型

train()

0 0.03260539472103119
0 0.06368591636419296
0 0.08260147273540497
0 0.04632813110947609
0 0.08333451300859451
0 0.10992465913295746
0 0.12929300963878632
0 0.061169371008872986
0 0.08229123800992966
0 0.0604255348443985
0 0.11475709825754166
0 0.13913851976394653
0 0.09228374809026718
0 0.10618235915899277
0 0.12170673906803131
0 0.05438697338104248
0 0.11730150133371353
0 0.07718850672245026
0 0.11877405643463135
0 0.0647420659661293
0 0.1062769666314125
0 0.08034960925579071
0 0.06462960690259933
0 0.029708124697208405
0 0.19415663182735443
0 0.022178875282406807
0 0.023824863135814667
0 0.06074700132012367
0 0.014404748566448689
0 0.015829702839255333
0 0.07006165385246277
0 0.0908271074295044
0 0.023783870041370392
0 0.09584006667137146
0 0.16521167755126953
0 0.09473344683647156
0 0.12153694033622742
0 0.030839459970593452
0 0.019292233511805534
40 8.071886259131134e-05
40 2.137169212801382e-05
40 0.00010651862248778343
40 7.332033419515938e-05
40 0.00010564295371295884
40 4.790672755916603e-05
40 3.7615245673805475e-05
40 3.413142985664308e-05
40 6.713613402098417e-05
40 0.0006545005016960204

测试模型结果

构造数据

# 从loader加载一批数据来测试

x,y=next(iter(loader))
x,y

测试

# 方法一
# out=model(x)

# 方法二 加载模型
model1=torch.load("huigui.model")
out=model1(x)

# 打印在一起，便于观察,
# 这个cat函数很有用注意
torch.cat([out,y],dim=1)

结论

从上面结果看

    [ 0.6257,  0.6214],
    
    [ 0.5435,  0.5454],
    
    [ 0.0227,  0.0203],
    
    [-0.0044,  0.0033],
    
    [ 0.5257,  0.5296],
    
    [ 0.4749,  0.4805],
    
    [ 0.4665,  0.4649],
    
    [ 0.4143,  0.4141],
    
    [ 0.0130,  0.0138]]

第一列是预测的，第二列是实际的，可以查看两列值相差很小，说明模型有效