【RNN练习】LSTM-火灾温度预测

Elfin_z2024-06-29 21:06

前期准备工作

python 复制代码 
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

python 复制代码 
data = pd.read_csv(r"D:\Personal Data\Learning Data\DL Learning Data\LSTM\woodpine2.csv")
data

2. 数据可视化

python 复制代码 
import matplotlib.pyplot as plt
import seaborn as sns

plt.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()
python 复制代码 
dataFrame = data.iloc[:,1:]
dataFrame

二、构建数据集

1. 数据集预处理

python 复制代码 
from sklearn.preprocessing import MinMaxScaler

dataFrame = data.iloc[:,1:].copy()
sc = MinMaxScaler(feature_range=(0,1))   #将数据归一化
for i in ['CO 1', 'Soot 1', 'Tem1']:
    dataFrame[i] = sc.fit_transform(dataFrame[i].values.reshape(-1,1))

dataFrame.shape

输出：

python 复制代码 
(5948, 3)

2. 设置X、y

python 复制代码 
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 = 0
for _,_ in data.iterrows():
    in_end = in_start + width_X
    out_end = in_end + width_y

    if 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 += 1

X = np.array(X)
y = np.array(y).reshape(-1,1,1)
X.shape, y.shape

输出：

python 复制代码 
((5939, 8, 3), (5939, 1, 1))

检查数据集是否有空值

python 复制代码 
#检查数据集是否有空值
print(np.any(np.isnan(X)))
print(np.any(np.isnan(y)))

3. 划分数据集

python 复制代码 
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

输出：

python 复制代码 
(torch.Size([5000, 8, 3]), torch.Size([5000, 1, 1]))
python 复制代码 
from torch.utils.data import TensorDataset, DataLoader
train_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

python 复制代码 
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)
        # self.fc1 = nn.Sequential(nn.Linear(300, 2))
 
    def forward(self, x):
        #如果不传入h0和c0，pytorch会将其初始化为0
        out, hidden1 = self.lstm0(x) 
        out, _ = self.lstm1(out, hidden1) 
        out = self.fc0(out) 
        return out[:, -1:, :]   #取2个预测值,否则经过lstm会得到8*2个预测 
model = model_lstm()
model

输出：

python 复制代码 
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)
)
python 复制代码 
model(torch.rand(30,8,3)).shape

输出：

python 复制代码 
torch.Size([30, 1, 1])

2.定义训练函数

python 复制代码 
# 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)
        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loss
        train_loss += loss.item()
    if lr_scheduler is not None:
        lr_scheduler.step()
        print("learning rate = ", opt.param_groups[0]['lr'])
    train_loss /= num_batches
    return train_loss

3. 定义测试函数

python 复制代码 
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loss
            y_pred = model(x)
            loss        = loss_fn(y_pred, y)
            test_loss += loss.item()
        
    test_loss /= num_batches
    return test_loss

4. 正式训练模型

python 复制代码 
#设置GPU训练
import torch
device=torch.device("cuda" if torch.cuda.is_available() else "cpu")
device

输出：

python 复制代码 
device(type='cuda')
python 复制代码 
#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     = 150
train_loss = []
test_loss  = []
lr_scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(opt,epochs, last_epoch=-1) 
best_val =[0, 1e5]
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)
    if best_val[1] >  epoch_test_loss:
        best_val =[epoch, epoch_test_loss]
        best_model_wst = copy.deepcopy(model.state_dict())
 
    train_loss.append(epoch_train_loss)
    test_loss.append(epoch_test_loss)
    
    template = ('Epoch:{:2d}, Train_loss:{:.6f}, Test_loss:{:.6f}')
    print(template.format(epoch+1, epoch_train_loss,  epoch_test_loss))
print("*"*20, 'Done', "*"*20)

输出：

python 复制代码 
learning rate =  0.09998903417374227
Epoch: 1, Train_loss:0.001263, Test_loss:0.012446
learning rate =  0.09995614150494292
Epoch: 2, Train_loss:0.014234, Test_loss:0.012052
learning rate =  0.09990133642141358
Epoch: 3, Train_loss:0.013899, Test_loss:0.011644
learning rate =  0.09982464296247522
Epoch: 4, Train_loss:0.013514, Test_loss:0.011209
learning rate =  0.09972609476841367
Epoch: 5, Train_loss:0.013065, Test_loss:0.010731
learning rate =  0.0996057350657239
Epoch: 6, Train_loss:0.012539, Test_loss:0.010204
learning rate =  0.09946361664814941
Epoch: 7, Train_loss:0.011923, Test_loss:0.009617
learning rate =  0.09929980185352524
Epoch: 8, Train_loss:0.011208, Test_loss:0.008970
learning rate =  0.09911436253643444
Epoch: 9, Train_loss:0.010392, Test_loss:0.008263
learning rate =  0.09890738003669028
Epoch:10, Train_loss:0.009476, Test_loss:0.007508
learning rate =  0.098678945143658
Epoch:11, Train_loss:0.008475, Test_loss:0.006716
learning rate =  0.09842915805643156
Epoch:12, Train_loss:0.007412, Test_loss:0.005910
learning rate =  0.09815812833988291
Epoch:13, Train_loss:0.006323, Test_loss:0.005112
learning rate =  0.09786597487660335
Epoch:14, Train_loss:0.005249, Test_loss:0.004352
learning rate =  0.09755282581475769
Epoch:15, Train_loss:0.004237, Test_loss:0.003654
learning rate =  0.09721881851187407
Epoch:16, Train_loss:0.003325, Test_loss:0.003036
learning rate =  0.0968640994745946
Epoch:17, Train_loss:0.002540, Test_loss:0.002509
learning rate =  0.09648882429441258
Epoch:18, Train_loss:0.001895, Test_loss:0.002082
learning rate =  0.09609315757942503
Epoch:19, Train_loss:0.001388, Test_loss:0.001740
learning rate =  0.09567727288213004
Epoch:20, Train_loss:0.001005, Test_loss:0.001479
learning rate =  0.09524135262330098
Epoch:21, Train_loss:0.000725, Test_loss:0.001283
learning rate =  0.09478558801197065
Epoch:22, Train_loss:0.000526, Test_loss:0.001138
learning rate =  0.09431017896156073
Epoch:23, Train_loss:0.000388, Test_loss:0.001033
learning rate =  0.09381533400219318
Epoch:24, Train_loss:0.000294, Test_loss:0.000955
learning rate =  0.09330127018922194
Epoch:25, Train_loss:0.000230, Test_loss:0.000900
learning rate =  0.09276821300802535
Epoch:26, Train_loss:0.000188, Test_loss:0.000859
learning rate =  0.09221639627510077
Epoch:27, Train_loss:0.000159, Test_loss:0.000829
learning rate =  0.09164606203550499
Epoch:28, Train_loss:0.000139, Test_loss:0.000806
learning rate =  0.09105746045668521
Epoch:29, Train_loss:0.000126, Test_loss:0.000789
learning rate =  0.09045084971874738
Epoch:30, Train_loss:0.000117, Test_loss:0.000775
learning rate =  0.08982649590120982
Epoch:31, Train_loss:0.000110, Test_loss:0.000765
learning rate =  0.089184672866292
Epoch:32, Train_loss:0.000105, Test_loss:0.000755
learning rate =  0.08852566213878947
Epoch:33, Train_loss:0.000102, Test_loss:0.000749
learning rate =  0.08784975278258783
Epoch:34, Train_loss:0.000100, Test_loss:0.000743
learning rate =  0.08715724127386973
Epoch:35, Train_loss:0.000098, Test_loss:0.000738
learning rate =  0.08644843137107058
Epoch:36, Train_loss:0.000096, Test_loss:0.000731
learning rate =  0.08572363398164018
Epoch:37, Train_loss:0.000095, Test_loss:0.000727
learning rate =  0.0849831670256683
Epoch:38, Train_loss:0.000094, Test_loss:0.000724
learning rate =  0.08422735529643445
Epoch:39, Train_loss:0.000094, Test_loss:0.000720
learning rate =  0.08345653031794292
Epoch:40, Train_loss:0.000093, Test_loss:0.000717
learning rate =  0.0826710301995053
Epoch:41, Train_loss:0.000093, Test_loss:0.000712
learning rate =  0.0818711994874345
Epoch:42, Train_loss:0.000092, Test_loss:0.000708
learning rate =  0.08105738901391554
Epoch:43, Train_loss:0.000092, Test_loss:0.000705
learning rate =  0.08022995574311877
Epoch:44, Train_loss:0.000092, Test_loss:0.000701
learning rate =  0.07938926261462367
Epoch:45, Train_loss:0.000092, Test_loss:0.000698
learning rate =  0.07853567838422161
Epoch:46, Train_loss:0.000091, Test_loss:0.000694
learning rate =  0.07766957746216722
Epoch:47, Train_loss:0.000091, Test_loss:0.000692
learning rate =  0.07679133974894985
Epoch:48, Train_loss:0.000091, Test_loss:0.000688
learning rate =  0.07590135046865654
Epoch:49, Train_loss:0.000091, Test_loss:0.000683
learning rate =  0.07500000000000002
Epoch:50, Train_loss:0.000091, Test_loss:0.000681
learning rate =  0.07408768370508578
Epoch:51, Train_loss:0.000091, Test_loss:0.000676
learning rate =  0.07316480175599312
Epoch:52, Train_loss:0.000090, Test_loss:0.000672
learning rate =  0.0722317589592464
Epoch:53, Train_loss:0.000090, Test_loss:0.000668
learning rate =  0.07128896457825366
Epoch:54, Train_loss:0.000090, Test_loss:0.000666
learning rate =  0.07033683215379004
Epoch:55, Train_loss:0.000090, Test_loss:0.000662
learning rate =  0.06937577932260518
Epoch:56, Train_loss:0.000090, Test_loss:0.000658
learning rate =  0.06840622763423394
Epoch:57, Train_loss:0.000090, Test_loss:0.000654
learning rate =  0.0674286023660908
Epoch:58, Train_loss:0.000090, Test_loss:0.000650
learning rate =  0.0664433323369292
Epoch:59, Train_loss:0.000090, Test_loss:0.000646
learning rate =  0.06545084971874741
Epoch:60, Train_loss:0.000090, Test_loss:0.000643
learning rate =  0.06445158984722361
Epoch:61, Train_loss:0.000090, Test_loss:0.000639
learning rate =  0.06344599103076332
Epoch:62, Train_loss:0.000090, Test_loss:0.000635
learning rate =  0.06243449435824276
Epoch:63, Train_loss:0.000089, Test_loss:0.000631
learning rate =  0.06141754350553282
Epoch:64, Train_loss:0.000089, Test_loss:0.000627
learning rate =  0.060395584540888
Epoch:65, Train_loss:0.000089, Test_loss:0.000623
learning rate =  0.05936906572928627
Epoch:66, Train_loss:0.000089, Test_loss:0.000620
learning rate =  0.05833843733580514
Epoch:67, Train_loss:0.000089, Test_loss:0.000615
learning rate =  0.05730415142812061
Epoch:68, Train_loss:0.000089, Test_loss:0.000612
learning rate =  0.056266661678215237
Epoch:69, Train_loss:0.000089, Test_loss:0.000608
learning rate =  0.055226423163382714
Epoch:70, Train_loss:0.000089, Test_loss:0.000604
learning rate =  0.0541838921666158
Epoch:71, Train_loss:0.000089, Test_loss:0.000601
learning rate =  0.0531395259764657
Epoch:72, Train_loss:0.000090, Test_loss:0.000597
learning rate =  0.05209378268646001
Epoch:73, Train_loss:0.000090, Test_loss:0.000593
learning rate =  0.051047120994167874
Epoch:74, Train_loss:0.000090, Test_loss:0.000590
learning rate =  0.050000000000000024
Epoch:75, Train_loss:0.000090, Test_loss:0.000587
learning rate =  0.048952879005832194
Epoch:76, Train_loss:0.000090, Test_loss:0.000583
learning rate =  0.04790621731354004
Epoch:77, Train_loss:0.000090, Test_loss:0.000579
learning rate =  0.04686047402353437
Epoch:78, Train_loss:0.000090, Test_loss:0.000576
learning rate =  0.045816107833384245
Epoch:79, Train_loss:0.000090, Test_loss:0.000573
learning rate =  0.044773576836617354
Epoch:80, Train_loss:0.000091, Test_loss:0.000569
learning rate =  0.04373333832178481
Epoch:81, Train_loss:0.000091, Test_loss:0.000567
learning rate =  0.042695848571879455
Epoch:82, Train_loss:0.000091, Test_loss:0.000563
learning rate =  0.0416615626641949
Epoch:83, Train_loss:0.000091, Test_loss:0.000561
learning rate =  0.040630934270713785
Epoch:84, Train_loss:0.000091, Test_loss:0.000558
learning rate =  0.039604415459112044
Epoch:85, Train_loss:0.000092, Test_loss:0.000555
learning rate =  0.03858245649446723
Epoch:86, Train_loss:0.000092, Test_loss:0.000552
learning rate =  0.03756550564175728
Epoch:87, Train_loss:0.000092, Test_loss:0.000550
learning rate =  0.03655400896923674
Epoch:88, Train_loss:0.000093, Test_loss:0.000548
learning rate =  0.03554841015277642
Epoch:89, Train_loss:0.000093, Test_loss:0.000545
learning rate =  0.03454915028125264
Epoch:90, Train_loss:0.000094, Test_loss:0.000543
learning rate =  0.03355666766307085
Epoch:91, Train_loss:0.000094, Test_loss:0.000541
learning rate =  0.03257139763390926
Epoch:92, Train_loss:0.000095, Test_loss:0.000539
learning rate =  0.031593772365766125
Epoch:93, Train_loss:0.000095, Test_loss:0.000538
learning rate =  0.030624220677394863
Epoch:94, Train_loss:0.000096, Test_loss:0.000536
learning rate =  0.02966316784621
Epoch:95, Train_loss:0.000096, Test_loss:0.000535
learning rate =  0.02871103542174638
Epoch:96, Train_loss:0.000097, Test_loss:0.000534
learning rate =  0.02776824104075365
Epoch:97, Train_loss:0.000098, Test_loss:0.000533
learning rate =  0.026835198244006937
Epoch:98, Train_loss:0.000098, Test_loss:0.000532
learning rate =  0.02591231629491424
Epoch:99, Train_loss:0.000099, Test_loss:0.000531
learning rate =  0.024999999999999998
Epoch:100, Train_loss:0.000100, Test_loss:0.000531
learning rate =  0.024098649531343507
Epoch:101, Train_loss:0.000101, Test_loss:0.000530
learning rate =  0.023208660251050187
Epoch:102, Train_loss:0.000102, Test_loss:0.000530
learning rate =  0.02233042253783281
Epoch:103, Train_loss:0.000103, Test_loss:0.000530
learning rate =  0.021464321615778426
Epoch:104, Train_loss:0.000104, Test_loss:0.000530
learning rate =  0.020610737385376356
Epoch:105, Train_loss:0.000105, Test_loss:0.000531
learning rate =  0.019770044256881267
Epoch:106, Train_loss:0.000106, Test_loss:0.000531
learning rate =  0.018942610986084494
Epoch:107, Train_loss:0.000107, Test_loss:0.000532
learning rate =  0.018128800512565522
Epoch:108, Train_loss:0.000108, Test_loss:0.000533
learning rate =  0.01732896980049475
Epoch:109, Train_loss:0.000110, Test_loss:0.000535
learning rate =  0.016543469682057093
Epoch:110, Train_loss:0.000111, Test_loss:0.000536
learning rate =  0.01577264470356557
Epoch:111, Train_loss:0.000112, Test_loss:0.000537
learning rate =  0.015016832974331734
Epoch:112, Train_loss:0.000113, Test_loss:0.000539
learning rate =  0.014276366018359849
Epoch:113, Train_loss:0.000115, Test_loss:0.000541
learning rate =  0.01355156862892944
Epoch:114, Train_loss:0.000116, Test_loss:0.000544
learning rate =  0.012842758726130289
Epoch:115, Train_loss:0.000118, Test_loss:0.000546
learning rate =  0.012150247217412192
Epoch:116, Train_loss:0.000119, Test_loss:0.000549
learning rate =  0.011474337861210548
Epoch:117, Train_loss:0.000120, Test_loss:0.000552
learning rate =  0.010815327133708018
Epoch:118, Train_loss:0.000122, Test_loss:0.000555
learning rate =  0.010173504098790198
Epoch:119, Train_loss:0.000123, Test_loss:0.000557
learning rate =  0.009549150281252639
Epoch:120, Train_loss:0.000124, Test_loss:0.000561
learning rate =  0.008942539543314802
Epoch:121, Train_loss:0.000126, Test_loss:0.000564
learning rate =  0.008353937964495033
Epoch:122, Train_loss:0.000127, Test_loss:0.000567
learning rate =  0.00778360372489925
Epoch:123, Train_loss:0.000128, Test_loss:0.000570
learning rate =  0.007231786991974674
Epoch:124, Train_loss:0.000129, Test_loss:0.000572
learning rate =  0.006698729810778079
Epoch:125, Train_loss:0.000130, Test_loss:0.000575
learning rate =  0.006184665997806824
Epoch:126, Train_loss:0.000131, Test_loss:0.000578
learning rate =  0.005689821038439266
Epoch:127, Train_loss:0.000132, Test_loss:0.000580
learning rate =  0.005214411988029369
Epoch:128, Train_loss:0.000132, Test_loss:0.000582
learning rate =  0.004758647376699034
Epoch:129, Train_loss:0.000133, Test_loss:0.000585
learning rate =  0.004322727117869964
Epoch:130, Train_loss:0.000133, Test_loss:0.000587
learning rate =  0.00390684242057497
Epoch:131, Train_loss:0.000134, Test_loss:0.000588
learning rate =  0.0035111757055874336
Epoch:132, Train_loss:0.000134, Test_loss:0.000590
learning rate =  0.003135900525405428
Epoch:133, Train_loss:0.000135, Test_loss:0.000591
learning rate =  0.002781181488125951
Epoch:134, Train_loss:0.000135, Test_loss:0.000592
learning rate =  0.002447174185242324
Epoch:135, Train_loss:0.000135, Test_loss:0.000593
learning rate =  0.0021340251233966383
Epoch:136, Train_loss:0.000135, Test_loss:0.000594
learning rate =  0.001841871660117095
Epoch:137, Train_loss:0.000135, Test_loss:0.000594
learning rate =  0.0015708419435684464
Epoch:138, Train_loss:0.000135, Test_loss:0.000595
learning rate =  0.0013210548563419857
Epoch:139, Train_loss:0.000135, Test_loss:0.000595
learning rate =  0.0010926199633097212
Epoch:140, Train_loss:0.000135, Test_loss:0.000596
learning rate =  0.0008856374635655696
Epoch:141, Train_loss:0.000135, Test_loss:0.000596
learning rate =  0.0007001981464747509
Epoch:142, Train_loss:0.000135, Test_loss:0.000596
learning rate =  0.0005363833518505835
Epoch:143, Train_loss:0.000135, Test_loss:0.000596
learning rate =  0.0003942649342761118
Epoch:144, Train_loss:0.000135, Test_loss:0.000596
learning rate =  0.0002739052315863355
Epoch:145, Train_loss:0.000135, Test_loss:0.000597
learning rate =  0.0001753570375247815
Epoch:146, Train_loss:0.000135, Test_loss:0.000597
learning rate =  9.866357858642206e-05
Epoch:147, Train_loss:0.000135, Test_loss:0.000597
learning rate =  4.3858495057080846e-05
Epoch:148, Train_loss:0.000135, Test_loss:0.000597
learning rate =  1.0965826257725021e-05
Epoch:149, Train_loss:0.000135, Test_loss:0.000597
learning rate =  0.0
Epoch:150, Train_loss:0.000135, Test_loss:0.000597
******************** Done ********************

四、模型评估

1. LOSS图

python 复制代码 
#LOSS图
# 支持中文
import matplotlib.pyplot as plt
plt.rcParams['font.sans-serif'] = ['SimHei']  # 用来正常显示中文标签
plt.rcParams['axes.unicode_minus'] = False  # 用来正常显示负号
 
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.legend()
plt.show()

2. 调用模型进行预测

python 复制代码 
model.load_state_dict(best_model_wst)
model.to("cpu")
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

python 复制代码 
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)

输出：

python 复制代码 
均方根误差: 6.53662
R2: 0.85083

五、总结

在数据预测前，数据预处理极为关键，包含数据去重、去空值。在设置迭代次数时，可适量缩小迭代次数。

相关推荐
weixin_437497774 小时前
读书笔记：Context Engineering 2.0 （上）
人工智能·nlp
喝拿铁写前端4 小时前
前端开发者使用 AI 的能力层级——从表面使用到工程化能力的真正分水岭
前端·人工智能·程序员
goodfat4 小时前
Win11如何关闭自动更新 Win11暂停系统更新的设置方法【教程】
人工智能·禁止windows更新·win11优化工具
北京领雁科技4 小时前
领雁科技反洗钱案例白皮书暨人工智能在反洗钱系统中的深度应用
人工智能·科技·安全
落叶，听雪4 小时前
河南建站系统哪个好
大数据·人工智能·python
清月电子4 小时前
杰理AC109N系列AC1082 AC1074 AC1090 芯片停产替代及资料说明
人工智能·单片机·嵌入式硬件·物联网
Dev7z4 小时前
非线性MPC在自动驾驶路径跟踪与避障控制中的应用及Matlab实现
人工智能·matlab·自动驾驶
七月shi人5 小时前
AI浪潮下，前端路在何方
前端·人工智能·ai编程
橙汁味的风5 小时前
1隐马尔科夫模型HMM与条件随机场CRF
人工智能·深度学习·机器学习
itwangyang5205 小时前
AIDD-人工智能药物设计-AI 制药编码之战：预测癌症反应，选对方法是关键
人工智能
热门推荐
01GitHub 镜像站点02从快手“12·22”直播攻击事件看：一次教科书式的业务层饱和攻击03电脑检测软件—图吧工具箱04Linux下V2Ray安装配置指南05Web安全中SQL注入绕过WAF的具体手法和实战案例063D 圣诞树网页代码07UV安装并设置国内源08jdk21下载、安装（Windows、Linux、macOS）09SQLmap 完整使用指南：环境搭建 + 命令详解 + 实操案例10在VSCode配置Java开发环境的保姆级教程（适配各类AI编程IDE）