实操教程 | 深度学习pytorch训练代码模板(个人习惯)

目录如下：

1. 导入包以及设置随机种子
2. 以类的方式定义超参数
3. 定义自己的模型
4. 定义早停类(此步骤可以省略)
5. 定义自己的数据集Dataset,DataLoader
6. 实例化模型，设置loss，优化器等
7. 开始训练以及调整lr
8. 绘图
9. 预测

import numpy as np
import torch
import torch.nn as nn
import numpy as np
import pandas as pd
from torch.utils.data import DataLoader, Dataset
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt

import random
seed = 42
torch.manual_seed(seed)
np.random.seed(seed)
random.seed(seed)

class


    
 argparse():
    pass

args = argparse()
args.epochs, args.learning_rate, args.patience = [30, 0.001, 4]
args.hidden_size, args.input_size= [40, 30]
args.device, = [torch.device("cuda:0" if torch.cuda.is_available() else "cpu"),]

class Your_model(nn.Module):
    def __init__(self):
        super(Your_model, self).__init__()
        pass
        
    def forward(self,x):
        pass
        return x

class EarlyStopping():
    def __init__(self,patience=7,verbose=False,delta=0):
        self.patience = patience
        self.verbose = verbose
        self.counter = 0
        self.best_score = None
        self.early_stop = False
        self.val_loss_min = np.Inf
        self.delta = delta
    def __call__(self,val_loss,model,path):
        print("val_loss={}".format(val_loss))
        score = -val_loss
        if self.best_score is None:
            self.best_score = score
            self.save_checkpoint(val_loss,model,path)
        elif score < self.best_score+self.delta:
            self.counter+=1
            print(f'EarlyStopping counter: {self.counter} out of {self.patience}')
            if self.counter>=self.patience:
                self.early_stop = True
        else:
            self.best_score = score
            self.save_checkpoint(val_loss,model,path)
            self.counter = 0
    def save_checkpoint(self,val_loss,model,path):
        if self.verbose:
            print(
                f'Validation loss decreased ({self.val_loss_min:.6f} --> {val_loss:.6f}).  Saving model ...')
        torch.save(model.state_dict(), path+'/'+'model_checkpoint.pth')
        self.val_loss_min = val_loss

    
class Dataset_name(Dataset):
    def __init__(self, flag='train'):
        assert flag in ['train', 'test', 'valid']
        self.flag = flag
        self.__load_data__()

    def __getitem__(self, index):
        pass
    def __len__(self):
        pass

    def __load_data__(self, csv_paths: list):
        pass
        print(
            "train_X.shape:{}\ntrain_Y.shape:{}\nvalid_X.shape:{}\nvalid_Y.shape:{}\n"
            .format(self.train_X.shape, self.train_Y.shape, self.valid_X.shape, self.valid_Y.shape))

train_dataset = Dataset_name(flag='train')
train_dataloader = DataLoader(dataset=train_dataset, batch_size=64, shuffle=True)
valid_dataset = Dataset_name(flag='valid')
valid_dataloader = DataLoader(dataset=valid_dataset, batch_size=64, shuffle=True)

model = Your_model().to(args.device)
criterion = torch.nn.MSELoss()
optimizer = torch.optim.Adam(Your_model.parameters(),lr=args.learning_rate)

train_loss = []
valid_loss = []
train_epochs_loss = []
valid_epochs_loss = []

early_stopping = EarlyStopping(patience=args.patience,verbose=True)

for epoch in range(args.epochs):
    Your_model.train()
    train_epoch_loss = []
    for idx,(data_x,data_y) in enumerate(train_dataloader,0):
        data_x = data_x.to(torch.float32).to(args.device)
        data_y = data_y.to(torch.float32).to(args.device)
        outputs = Your_model(data_x)
        optimizer.zero_grad()
        loss = criterion(data_y,outputs)
        loss.backward()
        optimizer.step()
        train_epoch_loss.append(loss.item())
        train_loss.append(loss.item())
        if idx%(len(train_dataloader)//2)==0:
            print("epoch={}/{},{}/{}of train, loss={}".format(
                epoch, args.epochs, idx, len(train_dataloader),loss.item()))
    train_epochs_loss.append(np.average(train_epoch_loss))
    
    #=====================valid============================
    Your_model.eval()
    valid_epoch_loss = []
    for idx,(data_x,data_y) in enumerate(valid_dataloader,0):
        data_x = data_x.to(torch.float32).to(args.device)
        data_y = data_y.to(torch.float32).to(args.device)
        outputs = Your_model(data_x)
        loss = criterion(outputs,data_y)
        valid_epoch_loss.append(loss.item())
        valid_loss.append(loss.item())
    valid_epochs_loss.append(np.average(valid_epoch_loss))
    #==================early stopping======================
    early_stopping(valid_epochs_loss[-1],model=Your_model,path=r'c:\\your_model_to_save')
    if early_stopping.early_stop:
        print("Early stopping")
        break
    #====================adjust lr========================
    lr_adjust = {
            2: 5e-5, 4: 1e-5, 6: 5e-6, 8: 1e-6,
            10: 5e-7, 15: 1e-7, 20: 5e-8
        }
    if epoch in lr_adjust.keys():
        lr = lr_adjust[epoch]
        for param_group in optimizer.param_groups:
            param_group['lr'] = lr
        print('Updating learning rate to {}'.format(lr))

plt.figure(figsize=(12,4))
plt.subplot(121)
plt.plot(train_loss[:])
plt.title("train_loss")
plt.subplot(122)
plt.plot(train_epochs_loss[1:],'-o',label="train_loss")
plt.plot(valid_epochs_loss[1:],'-o',label="valid_loss")
plt.title("epochs_loss")
plt.legend()
plt.show()

# 此处可定义一个预测集的Dataloader。也可以直接将你的预测数据reshape,添加batch_size=1
Your_model.eval()
predict = Your_model(data)

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