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nn.LSTM() 函数详解
nn.LSTM 是 PyTorch 中用于创建长短期记忆（Long Short-Term Memory，LSTM）模型的类。LSTM 是一种循环神经网络（Recurrent Neural Network，RNN）的变体，用于处理序列数据，能够有效地捕捉长期依赖关系。
语法
torch.nn.LSTM(input_size, hidden_size, num_layers=1, bias=True, batch_first=False, dropout=0, bidirectional=False) ●input_size: 输入特征的维度。
●hidden_size: 隐藏状态的维度，也是输出特征的维度。
●num_layers（可选参数）: LSTM 层的数量，默认为 1。
●bias（可选参数）: 是否使用偏置，默认为 True。
●batch_first（可选参数）: 如果为 True，则输入和输出张量的形状为 (batch_size, seq_len, feature_size)，默认为 False。
●dropout（可选参数）: 如果非零，将在 LSTM 层的输出上应用 dropout，防止过拟合。默认为 0。
●bidirectional（可选参数）: 如果为 True，则使用双向 LSTM，输出维度将翻倍。默认为 False。
示例
import torch import torch.nn as nn # 定义一个单向 LSTM 模型 input_size = 10 hidden_size = 20 num_layers = 2 batch_size = 3 seq_len = 5 lstm = nn.LSTM(input_size, hidden_size, num_layers) # 构造一个输入张量 input_tensor = torch.randn(seq_len, batch_size, input_size) # 初始化隐藏状态和细胞状态 h0 = torch.randn(num_layers, batch_size, hidden_size) c0 = torch.randn(num_layers, batch_size, hidden_size) # 将输入传递给 LSTM 模型 output, (hn, cn) = lstm(input_tensor, (h0, c0)) print("Output shape:", output.shape) # 输出特征的形状 print("Hidden state shape:", hn.shape) # 最后一个时间步的隐藏状态的形状 print("Cell state shape:", cn.shape) # 最后一个时间步的细胞状态的形状 代码输出
Output shape: torch.Size([5, 3, 20]) Hidden state shape: torch.Size([2, 3, 20]) Cell state shape: torch.Size([2, 3, 20]) 注意事项
●input_size 指定了输入数据的特征维度，hidden_size 指定了 LSTM 层的隐藏状态维度，num_layers 指定了 LSTM 的层数。
●LSTM 的输入张量的形状通常是 (seq_len, batch_size, input_size)，但如果设置了 batch_first=True，则形状为 (batch_size, seq_len, input_size)。
●LSTM 的输出包括输出张量和最后一个时间步的隐藏状态和细胞状态。
●可以通过 bidirectional=True 参数创建双向 LSTM，它会将输入序列分别从前向和后向传播，并将两个方向的隐藏状态拼接在一起作为输出。
●在使用 LSTM 时，通常需要注意输入数据的序列长度，以及是否需要对输入数据进行填充或截断，以保证输入数据的长度是一致的。
要求：
1.了解LSTM是什么，并使用其构建一个完整的程序
2.R2达到0.83拔高：
1.使用第1 ~ 8个时刻的数据预测第9 ~ 10个时刻的温度数据
我的环境：
●语言环境：Python3.8
●编译器：Jupyter Lab
●深度学习框架：torch 1.10.2 (cpu)
●数据：火灾温度数据集
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 - 导入数据
data = pd.read_csv("./R2/woodpine2.csv") data 代码输出
| 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
- 数据集可视化
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() 代码输出
dataFrame = data.iloc[:,1:] dataFrame 代码输出
| 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
- 数据集预处理
from sklearn.preprocessing import MinMaxScaler dataFrame = 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.shape 代码输出
(5948, 3) - 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_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 代码输出
((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))) 代码输出
False False - 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 代码输出
(torch.Size([5000, 8, 3]), torch.Size([5000, 1, 1])) 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
- 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2个预测值,否则经过lstm会得到8*2个预测 model = model_lstm() model 代码输出
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) ) 让我们看看模型的输出数据集格式是什么
model(torch.rand(30,8,3)).shape 代码输出
torch.Size([30, 1, 1]) - 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loss train_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_batches return train_loss - 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loss y_pred = model(x) loss = loss_fn(y_pred, y) test_loss += loss.item() test_loss /= num_batches return test_loss - 正式训练模型
#设置GPU训练 device=torch.device("cuda" if torch.cuda.is_available() else "cpu") device 代码输出
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:0.00131, Test_loss:0.01248 learning rate = 0.09961 Epoch: 2, Train_loss:0.01448, Test_loss:0.01209 learning rate = 0.09911 Epoch: 3, Train_loss:0.01415, Test_loss:0.01168 learning rate = 0.09843 Epoch: 4, Train_loss:0.01377, Test_loss:0.01124 learning rate = 0.09755 Epoch: 5, Train_loss:0.01334, Test_loss:0.01074 learning rate = 0.09649 Epoch: 6, Train_loss:0.01283, Test_loss:0.01019 learning rate = 0.09524 Epoch: 7, Train_loss:0.01222, Test_loss:0.00956 learning rate = 0.09382 Epoch: 8, Train_loss:0.01150, Test_loss:0.00886 learning rate = 0.09222 Epoch: 9, Train_loss:0.01067, Test_loss:0.00810 learning rate = 0.09045 Epoch:10, Train_loss:0.00972, Test_loss:0.00727 learning rate = 0.08853 Epoch:11, Train_loss:0.00867, Test_loss:0.00640 learning rate = 0.08645 Epoch:12, Train_loss:0.00754, Test_loss:0.00552 learning rate = 0.08423 Epoch:13, Train_loss:0.00638, Test_loss:0.00467 learning rate = 0.08187 Epoch:14, Train_loss:0.00523, Test_loss:0.00387 learning rate = 0.07939 Epoch:15, Train_loss:0.00417, Test_loss:0.00316 learning rate = 0.07679 Epoch:16, Train_loss:0.00322, Test_loss:0.00255 learning rate = 0.07409 Epoch:17, Train_loss:0.00242, Test_loss:0.00205 learning rate = 0.07129 Epoch:18, Train_loss:0.00178, Test_loss:0.00166 learning rate = 0.06841 Epoch:19, Train_loss:0.00129, Test_loss:0.00136 learning rate = 0.06545 Epoch:20, Train_loss:0.00093, Test_loss:0.00113 learning rate = 0.06243 Epoch:21, Train_loss:0.00067, Test_loss:0.00097 learning rate = 0.05937 Epoch:22, Train_loss:0.00049, Test_loss:0.00085 learning rate = 0.05627 Epoch:23, Train_loss:0.00036, Test_loss:0.00077 learning rate = 0.05314 Epoch:24, Train_loss:0.00028, Test_loss:0.00071 learning rate = 0.05000 Epoch:25, Train_loss:0.00022, Test_loss:0.00066 learning rate = 0.04686 Epoch:26, Train_loss:0.00018, Test_loss:0.00063 learning rate = 0.04373 Epoch:27, Train_loss:0.00016, Test_loss:0.00060 learning rate = 0.04063 Epoch:28, Train_loss:0.00014, Test_loss:0.00058 learning rate = 0.03757 Epoch:29, Train_loss:0.00013, Test_loss:0.00057 learning rate = 0.03455 Epoch:30, Train_loss:0.00012, Test_loss:0.00056 learning rate = 0.03159 Epoch:31, Train_loss:0.00012, Test_loss:0.00055 learning rate = 0.02871 Epoch:32, Train_loss:0.00011, Test_loss:0.00054 learning rate = 0.02591 Epoch:33, Train_loss:0.00011, Test_loss:0.00054 learning rate = 0.02321 Epoch:34, Train_loss:0.00011, Test_loss:0.00053 learning rate = 0.02061 Epoch:35, Train_loss:0.00011, Test_loss:0.00053 learning rate = 0.01813 Epoch:36, Train_loss:0.00012, Test_loss:0.00053 learning rate = 0.01577 Epoch:37, Train_loss:0.00012, Test_loss:0.00053 learning rate = 0.01355 Epoch:38, Train_loss:0.00012, Test_loss:0.00054 learning rate = 0.01147 Epoch:39, Train_loss:0.00012, Test_loss:0.00054 learning rate = 0.00955 Epoch:40, Train_loss:0.00013, Test_loss:0.00055 learning rate = 0.00778 Epoch:41, Train_loss:0.00013, Test_loss:0.00055 learning rate = 0.00618 Epoch:42, Train_loss:0.00013, Test_loss:0.00056 learning rate = 0.00476 Epoch:43, Train_loss:0.00014, Test_loss:0.00056 learning rate = 0.00351 Epoch:44, Train_loss:0.00014, Test_loss:0.00057 learning rate = 0.00245 Epoch:45, Train_loss:0.00014, Test_loss:0.00057 learning rate = 0.00157 Epoch:46, Train_loss:0.00014, Test_loss:0.00057 learning rate = 0.00089 Epoch:47, Train_loss:0.00014, Test_loss:0.00057 learning rate = 0.00039 Epoch:48, Train_loss:0.00014, Test_loss:0.00057 learning rate = 0.00010 Epoch:49, Train_loss:0.00014, Test_loss:0.00057 learning rate = 0.00000 Epoch:50, Train_loss:0.00014, Test_loss:0.00057 ==================== Done ==================== 四、模型评估
- LOSS图
import matplotlib.pyplot as plt 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() 代码输出
- 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() 代码输出
- 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) 代码输出
均方根误差: 6.77961 R2: 0.84096 五、拔高尝试
使用第1 ~ 8个时刻的数据预测第 9 ~10个时刻的温度数据，实际上也是把原来的单步预测修改为多步预测，主要改动部分在模型输出部分，再添加预测代码。
将model_lstm的“return out[:, -1:, :]”修改为“return out[:, -2:, :]”，就修改一个地方。
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2个预测值,否则经过lstm会得到8*2个预测 #将return out[:, -1:, :]修改如下，就修改一个地方 return out[:, -2:, :] model = model_lstm() model 添加预测代码
#拔高练习 test_1 = torch.tensor(dataFrame.iloc[:8 , ].values,dtype=torch.float32).reshape(1,-1,3) pred_ = model(test_1) pred_ = np.round(sc.inverse_transform(pred_.detach().numpy().reshape(1,-1)).reshape(-1),2) #NumPy中的round函数将结果四舍五入保留两位小数 real_tem = data.Tem1.iloc[:2].values print(f"第9~10时刻的温度预测：", pred_) print("第9~10时刻的真实温度：", real_tem) 代码输出
第9~10时刻的温度预测： [31.4 29.51] 第9~10时刻的真实温度： [25. 25.] 