實操教程 | 深度學習pytorch訓練代碼模板(個人習慣)

目錄如下：

1. 導入包以及設置隨機種子
2. 以類的方式定義超參數(shù)
3. 定義自己的模型
4. 定義早停類(此步驟可以省略)
5. 定義自己的數(shù)據(jù)集Dataset,DataLoader
6. 實例化模型，設置loss，優(yōu)化器等
7. 開始訓練以及調整lr
8. 繪圖
9. 預測

一、導入包以及設置隨機種子

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)

二、以類的方式定義超參數(shù)

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"),]

三、定義自己的模型

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

四、定義早停類(此步驟可以省略)

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

五、定義自己的數(shù)據(jù)集Dataset,DataLoader

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)

六、實例化模型，設置loss，優(yōu)化器等

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)

七、開始訓練以及調整lr

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))

八、繪圖

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()

九、預測

# 此處可定義一個預測集的Dataloader。也可以直接將你的預測數(shù)據(jù)reshape,添加batch_size=1
Your_model.eval()
predict = Your_model(data)

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