第R4周：LSTM-火灾温度预测

• 🍨 本文为🔗365天深度学习训练营 中的学习记录博客
• 🍖 原作者：K同学啊

一、前期准备工作

import torch.nn.functional as F
import numpy  as np
import pandas as pd
import torch
from torch    import nn

1.导入数据

data = pd.read_csv("woodpine2.csv")data

	Time	Tem1	CO 1	Soot 1
0	0.000	25.0	0.000000	0.000000
1	0.228	25.0	0.000000	0.000000
2	0.456	25.0	0.000000	0.000000
3	0.685	25.0	0.000000	0.000000
4	0.913	25.0	0.000000	0.000000
...	...	...	...	...
5943	366.000	295.0	0.000077	0.000496
5944	366.000	294.0	0.000077	0.000494
5945	367.000	292.0	0.000077	0.000491
5946	367.000	291.0	0.000076	0.000489
5947	367.000	290.0	0.000076	0.000487

5948 rows × 4 columns

2. 数据集可视化

import matplotlib.pyplot as plt
import seaborn as snsplt.rcParams['savefig.dpi'] = 500 #图片像素
plt.rcParams['figure.dpi']  = 500 #分辨率fig, ax =plt.subplots(1,3,constrained_layout=True, figsize=(14, 3))sns.lineplot(data=data["Tem1"], ax=ax[0])
sns.lineplot(data=data["CO 1"], ax=ax[1])
sns.lineplot(data=data["Soot 1"], ax=ax[2])
plt.show()

dataFrame = data.iloc[:,1:]dataFrame

	Tem1	CO 1	Soot 1
0	25.0	0.000000	0.000000
1	25.0	0.000000	0.000000
2	25.0	0.000000	0.000000
3	25.0	0.000000	0.000000
4	25.0	0.000000	0.000000
...	...	...	...
5943	295.0	0.000077	0.000496
5944	294.0	0.000077	0.000494
5945	292.0	0.000077	0.000491
5946	291.0	0.000076	0.000489
5947	290.0	0.000076	0.000487

5948 rows × 3 columns

二、构建数据集

1. 数据集预处理

from sklearn.preprocessing import MinMaxScalerdataFrame = data.iloc[:,1:].copy()
sc  = MinMaxScaler(feature_range=(0, 1)) #将数据归一化，范围是0到1for i in ['CO 1', 'Soot 1', 'Tem1']:dataFrame[i] = sc.fit_transform(dataFrame[i].values.reshape(-1, 1))dataFrame.shape

(5948, 3)

2. 设置X, y

width_X = 8
width_y = 1##取前8个时间段的Tem1、CO 1、Soot 1为X，第9个时间段的Tem1为y。
X = []
y = []in_start = 0for _, _ in data.iterrows():in_end  = in_start + width_Xout_end = in_end   + width_yif out_end < len(dataFrame):X_ = np.array(dataFrame.iloc[in_start:in_end , ])y_ = np.array(dataFrame.iloc[in_end  :out_end, 0])X.append(X_)y.append(y_)in_start += 1X = np.array(X)
y = np.array(y).reshape(-1,1,1)X.shape, y.shape

((5939, 8, 3), (5939, 1, 1))

检查数据集中是否有空值

print(np.any(np.isnan(X)))
print(np.any(np.isnan(y)))

False
False

3. 划分数据集

X_train = torch.tensor(np.array(X[:5000]), dtype=torch.float32)
y_train = torch.tensor(np.array(y[:5000]), dtype=torch.float32)X_test  = torch.tensor(np.array(X[5000:]), dtype=torch.float32)
y_test  = torch.tensor(np.array(y[5000:]), dtype=torch.float32)
X_train.shape, y_train.shape

(torch.Size([5000, 8, 3]), torch.Size([5000, 1, 1]))

from torch.utils.data import TensorDataset, DataLoadertrain_dl = DataLoader(TensorDataset(X_train, y_train),batch_size=64, shuffle=False)test_dl  = DataLoader(TensorDataset(X_test, y_test),batch_size=64, shuffle=False)

三、模型训练

1. 构建模型

class model_lstm(nn.Module):def __init__(self):super(model_lstm, self).__init__()self.lstm0 = nn.LSTM(input_size=3 ,hidden_size=320, num_layers=1, batch_first=True)self.lstm1 = nn.LSTM(input_size=320 ,hidden_size=320, num_layers=1, batch_first=True)self.fc0   = nn.Linear(320, 1)def forward(self, x):out, hidden1 = self.lstm0(x) out, _ = self.lstm1(out, hidden1) out    = self.fc0(out) return out[:, -1:, :]   #取1个预测值,否则经过lstm会得到8*1个预测model = model_lstm()
model

model_lstm((lstm0): LSTM(3, 320, batch_first=True)(lstm1): LSTM(320, 320, batch_first=True)(fc0): Linear(in_features=320, out_features=1, bias=True)
)

model(torch.rand(30,8,3)).shape

torch.Size([30, 1, 1])

2. 定义训练函数

# 训练循环
import copy
def train(train_dl, model, loss_fn, opt, lr_scheduler=None):size        = len(train_dl.dataset)  num_batches = len(train_dl)   train_loss  = 0  # 初始化训练损失和正确率for x, y in train_dl:  x, y = x.to(device), y.to(device)# 计算预测误差pred = model(x)          # 网络输出loss = loss_fn(pred, y)  # 计算网络输出和真实值之间的差距# 反向传播opt.zero_grad()  # grad属性归零loss.backward()  # 反向传播opt.step()       # 每一步自动更新# 记录losstrain_loss += loss.item()if lr_scheduler is not None:lr_scheduler.step()print("learning rate = {:.5f}".format(opt.param_groups[0]['lr']), end="  ")train_loss /= num_batchesreturn train_loss

3. 定义测试函数

def test (dataloader, model, loss_fn):size        = len(dataloader.dataset)  # 测试集的大小num_batches = len(dataloader)          # 批次数目test_loss   = 0# 当不进行训练时，停止梯度更新，节省计算内存消耗with torch.no_grad():for x, y in dataloader:x, y = x.to(device), y.to(device)# 计算lossy_pred = model(x)loss        = loss_fn(y_pred, y)test_loss += loss.item()test_loss /= num_batchesreturn test_loss

4. 正式训练模型

#设置GPU训练
device=torch.device("cuda" if torch.cuda.is_available() else "cpu")
device

device(type=‘cpu’)

#训练模型
model = model_lstm()
model = model.to(device)
loss_fn    = nn.MSELoss() # 创建损失函数
learn_rate = 1e-1   # 学习率
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)

learning rate = 0.09990 Epoch: 1, Train_loss:0.00123, Test_loss:0.01228
learning rate = 0.09961 Epoch: 2, Train_loss:0.01404, Test_loss:0.01183
learning rate = 0.09911 Epoch: 3, Train_loss:0.01365, Test_loss:0.01135
learning rate = 0.09843 Epoch: 4, Train_loss:0.01321, Test_loss:0.01085
learning rate = 0.09755 Epoch: 5, Train_loss:0.01270, Test_loss:0.01029
learning rate = 0.09649 Epoch: 6, Train_loss:0.01212, Test_loss:0.00968
learning rate = 0.09524 Epoch: 7, Train_loss:0.01144, Test_loss:0.00901
learning rate = 0.09382 Epoch: 8, Train_loss:0.01065, Test_loss:0.00827
learning rate = 0.09222 Epoch: 9, Train_loss:0.00975, Test_loss:0.00748
learning rate = 0.09045 Epoch:10, Train_loss:0.00876, Test_loss:0.00665
learning rate = 0.08853 Epoch:11, Train_loss:0.00769, Test_loss:0.00580
learning rate = 0.08645 Epoch:12, Train_loss:0.00658, Test_loss:0.00497
learning rate = 0.08423 Epoch:13, Train_loss:0.00548, Test_loss:0.00418
learning rate = 0.08187 Epoch:14, Train_loss:0.00444, Test_loss:0.00346
learning rate = 0.07939 Epoch:15, Train_loss:0.00349, Test_loss:0.00283
learning rate = 0.07679 Epoch:16, Train_loss:0.00268, Test_loss:0.00230
learning rate = 0.07409 Epoch:17, Train_loss:0.00200, Test_loss:0.00188
learning rate = 0.07129 Epoch:18, Train_loss:0.00147, Test_loss:0.00154
learning rate = 0.06841 Epoch:19, Train_loss:0.00107, Test_loss:0.00129
learning rate = 0.06545 Epoch:20, Train_loss:0.00078, Test_loss:0.00110
learning rate = 0.06243 Epoch:21, Train_loss:0.00057, Test_loss:0.00096
learning rate = 0.05937 Epoch:22, Train_loss:0.00042, Test_loss:0.00085
learning rate = 0.05627 Epoch:23, Train_loss:0.00032, Test_loss:0.00078
learning rate = 0.05314 Epoch:24, Train_loss:0.00025, Test_loss:0.00072
learning rate = 0.05000 Epoch:25, Train_loss:0.00021, Test_loss:0.00068
learning rate = 0.04686 Epoch:26, Train_loss:0.00017, Test_loss:0.00065
learning rate = 0.04373 Epoch:27, Train_loss:0.00015, Test_loss:0.00062
learning rate = 0.04063 Epoch:28, Train_loss:0.00014, Test_loss:0.00060
learning rate = 0.03757 Epoch:29, Train_loss:0.00013, 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:0.00012, 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:0.00012, Test_loss:0.00055
learning rate = 0.01577 Epoch:37, Train_loss:0.00012, 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:0.00013, 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:0.00014, Test_loss:0.00058
learning rate = 0.00476 Epoch:43, Train_loss:0.00014, Test_loss:0.00059
learning rate = 0.00351 Epoch:44, Train_loss:0.00014, Test_loss:0.00059
learning rate = 0.00245 Epoch:45, Train_loss:0.00014, Test_loss:0.00059
learning rate = 0.00157 Epoch:46, Train_loss:0.00014, Test_loss:0.00060
learning rate = 0.00089 Epoch:47, Train_loss:0.00014, Test_loss:0.00060
learning rate = 0.00039 Epoch:48, Train_loss:0.00014, Test_loss:0.00060
learning rate = 0.00010 Epoch:49, Train_loss:0.00014, Test_loss:0.00060
learning rate = 0.00000 Epoch:50, Train_loss:0.00014, Test_loss:0.00060
==================== Done ====================

四、模型评估

1. Loss图片

import matplotlib.pyplot as plt
from datetime import datetime
current_time = datetime.now() # 获取当前时间plt.figure(figsize=(5, 3),dpi=120)plt.plot(train_loss    , label='LSTM Training Loss')
plt.plot(test_loss, label='LSTM Validation Loss')plt.title('Training and Validation Loss')
plt.xlabel(current_time) # 打卡请带上时间戳，否则代码截图无效
plt.legend()
plt.show()

2. 调用模型进行预测

predicted_y_lstm = sc.inverse_transform(model(X_test).detach().numpy().reshape(-1,1))                    # 测试集输入模型进行预测
y_test_1         = sc.inverse_transform(y_test.reshape(-1,1))
y_test_one       = [i[0] for i in y_test_1]
predicted_y_lstm_one = [i[0] for i in predicted_y_lstm]plt.figure(figsize=(5, 3),dpi=120)
# 画出真实数据和预测数据的对比曲线
plt.plot(y_test_one[:2000], color='red', label='real_temp')
plt.plot(predicted_y_lstm_one[:2000], color='blue', label='prediction')plt.title('Title')
plt.xlabel('X')
plt.ylabel('Y')
plt.legend()
plt.show()

3. R2值评估

from sklearn import metrics
"""
RMSE ：均方根误差  ----->  对均方误差开方
R2   ：决定系数，可以简单理解为反映模型拟合优度的重要的统计量
"""
RMSE_lstm  = metrics.mean_squared_error(predicted_y_lstm_one, y_test_1)**0.5
R2_lstm    = metrics.r2_score(predicted_y_lstm_one, y_test_1)print('均方根误差: %.5f' % RMSE_lstm)
print('R2: %.5f' % R2_lstm)

均方根误差: 6.92733
R2: 0.83259

总结：

本周主要学习了LSTM模型，并且通过实践更加深入地了解到了LSTM模型。