R4周打卡——Pytorch實現 LSTM火災預測

🍨 本文為🔗365天深度學習訓練營中的學習記錄博客
🍖 原作者：K同學啊

一、準備工作

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1.1導入數據

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1.2數據集可視化

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二、構建數據集

2.1數據集預處理

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2.2設置X、Y

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2.3檢查數據集中有沒有空值

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2.4劃分數據集

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三、構建模型

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3.1定義訓練函數

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3.2定義測試函數

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四、訓練模型

#訓練模型
model = model_lstm()
model = model.to(device)
loss_fn    = nn.MSELoss() # 創建損失函數
learn_rate = 1e-1   # 學習率
opt        = torch.optim.SGD(model.parameters(),lr=learn_rate,weight_decay=1e-4)
epochs     = 50
train_loss = []
test_loss  = []
lr_scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(opt,epochs, last_epoch=-1) for epoch in range(epochs):model.train()epoch_train_loss = train(train_dl, model, loss_fn, opt, lr_scheduler)model.eval()epoch_test_loss = test(test_dl, model, loss_fn)train_loss.append(epoch_train_loss)test_loss.append(epoch_test_loss)template = ('Epoch:{:2d}, Train_loss:{:.5f}, Test_loss:{:.5f}')print(template.format(epoch+1, epoch_train_loss,  epoch_test_loss))print("="*20, 'Done', "="*20)

輸出

learning rate = 0.09990  Epoch: 1, Train_loss:0.00120, Test_loss:0.01197
learning rate = 0.09961  Epoch: 2, Train_loss:0.01372, Test_loss:0.01150
learning rate = 0.09911  Epoch: 3, Train_loss:0.01330, Test_loss:0.01102
learning rate = 0.09843  Epoch: 4, Train_loss:0.01282, Test_loss:0.01050
learning rate = 0.09755  Epoch: 5, Train_loss:0.01228, Test_loss:0.00993
learning rate = 0.09649  Epoch: 6, Train_loss:0.01166, Test_loss:0.00931
learning rate = 0.09524  Epoch: 7, Train_loss:0.01094, Test_loss:0.00863
learning rate = 0.09382  Epoch: 8, Train_loss:0.01013, Test_loss:0.00790
learning rate = 0.09222  Epoch: 9, Train_loss:0.00922, Test_loss:0.00712
learning rate = 0.09045  Epoch:10, Train_loss:0.00823, Test_loss:0.00631
learning rate = 0.08853  Epoch:11, Train_loss:0.00718, Test_loss:0.00550
learning rate = 0.08645  Epoch:12, Train_loss:0.00611, Test_loss:0.00471
learning rate = 0.08423  Epoch:13, Train_loss:0.00507, Test_loss:0.00397
learning rate = 0.08187  Epoch:14, Train_loss:0.00409, Test_loss:0.00330
learning rate = 0.07939  Epoch:15, Train_loss:0.00322, Test_loss:0.00272
learning rate = 0.07679  Epoch:16, Train_loss:0.00247, Test_loss:0.00223
learning rate = 0.07409  Epoch:17, Train_loss:0.00185, Test_loss:0.00183
learning rate = 0.07129  Epoch:18, Train_loss:0.00137, Test_loss:0.00152
learning rate = 0.06841  Epoch:19, Train_loss:0.00100, Test_loss:0.00128
learning rate = 0.06545  Epoch:20, Train_loss:0.00073, Test_loss:0.00110
learning rate = 0.06243  Epoch:21, Train_loss:0.00054, Test_loss:0.00097
learning rate = 0.05937  Epoch:22, Train_loss:0.00040, Test_loss:0.00087
learning rate = 0.05627  Epoch:23, Train_loss:0.00031, Test_loss:0.00079
learning rate = 0.05314  Epoch:24, Train_loss:0.00024, Test_loss:0.00073
learning rate = 0.05000  Epoch:25, Train_loss:0.00020, Test_loss:0.00069
learning rate = 0.04686  Epoch:26, Train_loss:0.00017, Test_loss:0.00066
learning rate = 0.04373  Epoch:27, Train_loss:0.00015, Test_loss:0.00063
learning rate = 0.04063  Epoch:28, Train_loss:0.00013, Test_loss:0.00061
learning rate = 0.03757  Epoch:29, Train_loss:0.00012, Test_loss:0.00059
learning rate = 0.03455  Epoch:30, Train_loss:0.00012, Test_loss:0.00058
learning rate = 0.03159  Epoch:31, Train_loss:0.00011, Test_loss:0.00057
learning rate = 0.02871  Epoch:32, Train_loss:0.00011, Test_loss:0.00056
learning rate = 0.02591  Epoch:33, Train_loss:0.00011, Test_loss:0.00055
learning rate = 0.02321  Epoch:34, Train_loss:0.00011, Test_loss:0.00055
learning rate = 0.02061  Epoch:35, Train_loss:0.00011, Test_loss:0.00055
learning rate = 0.01813  Epoch:36, Train_loss:0.00011, Test_loss:0.00055
learning rate = 0.01577  Epoch:37, Train_loss:0.00011, Test_loss:0.00055
learning rate = 0.01355  Epoch:38, Train_loss:0.00012, Test_loss:0.00056
learning rate = 0.01147  Epoch:39, Train_loss:0.00012, Test_loss:0.00056
learning rate = 0.00955  Epoch:40, Train_loss:0.00012, Test_loss:0.00057
learning rate = 0.00778  Epoch:41, Train_loss:0.00013, Test_loss:0.00058
learning rate = 0.00618  Epoch:42, Train_loss:0.00013, Test_loss:0.00059
learning rate = 0.00476  Epoch:43, Train_loss:0.00013, Test_loss:0.00060
learning rate = 0.00351  Epoch:44, Train_loss:0.00014, Test_loss:0.00060
learning rate = 0.00245  Epoch:45, Train_loss:0.00014, Test_loss:0.00061
learning rate = 0.00157  Epoch:46, Train_loss:0.00014, Test_loss:0.00061
learning rate = 0.00089  Epoch:47, Train_loss:0.00014, Test_loss:0.00061
learning rate = 0.00039  Epoch:48, Train_loss:0.00014, Test_loss:0.00061
learning rate = 0.00010  Epoch:49, Train_loss:0.00014, Test_loss:0.00061
learning rate = 0.00000  Epoch:50, Train_loss:0.00014, Test_loss:0.00061
==================== Done ====================

4.1Loss圖

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4.2調用模型進行預測

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4.3R2值評估

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五、總結

LSTM模型的核心優勢
時間序列數據的建模能力：LSTM（長短時記憶網絡）專門設計來處理和預測序列數據中的長期依賴關系。在火災溫度預測這種時間序列問題中，LSTM能夠有效捕捉過去溫度數據與未來溫度之間的關聯，尤其在火災數據中，溫度往往有較強的時間依賴性。

解決梯度消失問題：傳統的RNN在長時間序列中容易遇到梯度消失問題，而LSTM通過引入記憶單元（cell state）和門控機制有效地緩解了這一問題，能夠記住遠期的信息。

PyTorch的使用
靈活性和易用性：PyTorch框架提供了非常簡潔的API，并且在實現LSTM時具有高度的靈活性，能方便地調整網絡結構、訓練參數及優化方法。

調試和可視化：PyTorch的動態計算圖使得調試過程更加直觀，尤其是在處理復雜的深度學習模型時，可以逐步查看模型輸出、梯度等中間結果。

模型訓練與評估
數據預處理的重要性：時間序列數據的預處理，尤其是數據標準化或歸一化，能夠顯著提高LSTM模型的訓練效果。溫度數據可能存在波動和不規律的變化，因此，去噪和處理缺失數據是模型成功的關鍵因素。

模型評估：對于回歸任務，如火災溫度預測，常用的評估指標包括均方誤差（MSE）、均方根誤差（RMSE）和決定系數（R2）。這些指標可以幫助評估模型的預測精度，進一步調整模型超參數和結構。

模型的表現
訓練和測試誤差：通過模型訓練過程中觀察訓練和測試誤差，可能發現訓練集誤差較低但測試集誤差較高，暗示過擬合的存在。通過交叉驗證和正則化技術可以有效解決這一問題。

時間依賴性：LSTM能夠捕捉到較長時間跨度的數據關系，但有時也會遇到短期依賴的情況，可能需要調整網絡層數、隱藏層維度等參數

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