深度学习打卡第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
目录
一、前期准备
1.1 导入数据
1.2 数据可视化
二、构建数据集
2.1 数据集预处理
2.2 设置X，y
2.3 划分数据集
三、模型训练
3.1 构建模型
3.2 定义训练函数
3.3 定义测试函数
3.4 正式训练模型
四、模型评估
4.1 LOSS图
4.2 调用模型进行预测
4.3 R2值评估
一、前期准备
import torch.nn.functional as F
import numpy as np
import pandas as pd
import torch
from torch import nn1.1 导入数据
data = pd.read_csv("woodpine2.csv")
data
1.2 数据可视化
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
二、构建数据集
2.1 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1
for i in ['CO 1', 'Soot 1', 'Tem1']:dataFrame[i] = sc.fit_transform(dataFrame[i].values.reshape(-1, 1))
dataFrame.shape2.2 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 = 0
for _, _ 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.shape2.3 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.shapefrom 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
3.1 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
3.2 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_loss3.3 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_loss3.4 正式训练模型
#设置GPU训练
device=torch.device("cuda" if torch.cuda.is_available() else "cpu")
device#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)
四、模型评估
4.1 LOSS图
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()
4.2 调用模型进行预测
predicted_y_lstm = sc.inverse_transform(model(X_test).detach().numpy().reshape(-1,1))
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()
4.3 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)