import torch import torch.nn as nn import torch.optim as optim from torch.utils.data import DataLoader, TensorDataset class LSTM(nn.Module): def __init__(self, inputDim, hiddenDim, layerNum, batchSize): super(LSTM, self).__init__() self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") self.inputDim = inputDim self.hiddenDim = hiddenDim self.layerNum = layerNum self.batchSize = batchSize self.lstm = nn.LSTM(inputDim, hiddenDim, layerNum, batch_first = True).to(self.device) self.fc = nn.Linear(hiddenDim, 1).to(self.device) def forward(self, inputData): h0 = torch.zeros(self.layerNum, inputData.size(0), self.hiddenDim, device = inputData.device) c0 = torch.zeros(self.layerNum, inputData.size(0), self.hiddenDim, device = inputData.device) out, hidden = self.lstm(inputData, (h0, c0)) out = self.fc(out[:, -1, :]) return out def SetCriterion(self, func): self.criterion = func def SetOptimizer(self, func): self.optimizer = func def SetLstmTrainData(self, inputData, labelData): data = TensorDataset(inputData.to(device), labelData.to(device)) self.dataloader = DataLoader(data, batch_size = self.batchSize, shuffle = True) def TrainLstmModule(self, epochNum, learnRate, statPeriod): for epoch in range(epochNum): for batch_x, batch_y in self.dataloader: self.optimizer.zero_grad() output = self.forward(batch_x) loss = self.criterion(output, batch_y) loss.backward() self.optimizer.step() if epoch % statPeriod == 0: print("Epoch[{}/{}], loss:{:.6f}".format(epoch + 1, epochNum, loss.item())) def GetLstmModuleTrainRst(self, verifyData): results = [] with torch.no_grad(): output = self.forward(verifyData) results = output.squeeze().tolist() # 将预测结果转换为 Python 列表 return results if __name__ == "__main__": inputDataNum = 100 timeStep = 5 inputDataDim = 10000 labelDataDim = 1 hiddenDataDim = 200 layerNum = 20 trainBatchSize = 100 epochNum = 1 learnRate = 0.01 statPeriod = 1 weightDecay = 0.001 device = torch.device("cuda" if torch.cuda.is_available() else "cpu") model = LSTM(inputDataDim, hiddenDataDim, layerNum, trainBatchSize).to(device) model.SetCriterion(nn.MSELoss()) model.SetOptimizer(torch.optim.Adam(model.parameters(), lr = learnRate, weight_decay = weightDecay)) inputData = torch.randn(inputDataNum, timeStep, inputDataDim) labelData = torch.randn(inputDataNum, labelDataDim) verifyData = inputData model.SetLstmTrainData(inputData, labelData) model.TrainLstmModule(epochNum, learnRate, statPeriod) torch.save(model.state_dict(), "lstm_model.pth") model.load_state_dict(torch.load("lstm_model.pth")) model.GetLstmModuleTrainRst(verifyData) 这段代码,为什么output = self.forward(batch_x)总是输出相同的值
时间: 2024-04-08 15:31:38 浏览: 18
在定的代码中,`output = self.forward(batch_x)`总是输出相同的值的原因可能是因为没有正确设置模型的训练参数和优化器。在`SetOptimizer`方法中,你需要传入一个优化器函数,并将其赋值给`self.optimizer`。同样,在`SetCriterion`方法中,你需要传入一个损失函数并将其赋值给`self.criterion`。
确保在使用`TrainLstmModule`方法之前,已经正确设置了损失函数和优化器。另外,检查一下学习率和权重衰减是否适当。
如果仍然存在问题,请检查数据集是否存在偏差或者模型的超参数是否需要更进一步调整
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import torch import torch.nn as nn import torch.optim as optim from torch.utils.data import DataLoader, TensorDataset class LSTM(nn.Module): def __init__(self, inputDim, hiddenDim, layerNum, batchSize): super(LSTM, self).__init__()
你在这段代码中定义了一个名为LSTM的类,继承自torch.nn.Module。这个类用于构建一个LSTM模型。在初始化方法中,你传入了inputDim(输入维度)、hiddenDim(隐藏层维度)、layerNum(LSTM层数)和batchSize(批次大小)等参数。super()函数用于调用父类的初始化方法。接下来的代码缺失了,请提供完整的代码。
import torch import torch.nn as nn import torch.optim as optim from torch.utils.data import DataLoader, TensorDataset class LSTM(nn.Module): def __init__(self, inputDim, hiddenDim, layerNum, batchSize): super(LSTM, self).__init__() self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") self.inputDim = inputDim self.hiddenDim = hiddenDim self.layerNum = layerNum self.batchSize = batchSize self.lstm = nn.LSTM(inputDim, hiddenDim, layerNum, batch_first = True).to(self.device) self.fc = nn.Linear(hiddenDim, 1).to(self.device) def forward(self, inputData): h0 = torch.zeros(self.layerNum, inputData.size(0), self.hiddenDim, device = inputData.device) c0 = torch.zeros(self.layerNum, inputData.size(0), self.hiddenDim, device = inputData.device) out, hidden = self.lstm(inputData, (h0, c0)) out = self.fc(out[:, -1, :]) return out def SetCriterion(self, func): self.criterion = func def SetOptimizer(self, func): self.optimizer = func def SetLstmTrainData(self, inputData, labelData): data = TensorDataset(inputData.to(device), labelData.to(device)) self.dataloader = DataLoader(data, batch_size = self.batchSize, shuffle = True) def TrainLstmModule(self, epochNum, learnRate, statPeriod): for epoch in range(epochNum): for batch_x, batch_y in self.dataloader: self.optimizer.zero_grad() output = self.forward(batch_x) loss = self.criterion(output, batch_y) loss.backward() self.optimizer.step() if epoch % statPeriod == 0: print("Epoch[{}/{}], loss:{:.6f}".format(epoch + 1, epochNum, loss.item())) def GetLstmModuleTrainRst(self, verifyData): results = [] with torch.no_grad(): output = self.forward(verifyData) results = output.squeeze().tolist() # 将预测结果转换为 Python 列表 return results if __name__ == "__main__": inputDataNum = 100 timeStep = 5 inputDataDim = 10000 labelDataDim = 1 hiddenDataDim = 200 layerNum = 20 trainBatchSize = 100 epochNum = 1 learnRate = 0.01 statPeriod = 1 weightDecay = 0.001 device = torch.device("cuda" if torch.cuda.is_available() else "cpu") model = LSTM(inputDataDim, hiddenDataDim, layerNum, trainBatchSize).to(device) model.SetCriterion(nn.MSELoss()) model.SetOptimizer(torch.optim.Adam(model.parameters(), lr = learnRate, weight_decay = weightDecay)) inputData = torch.randn(inputDataNum, timeStep, inputDataDim) labelData = torch.randn(inputDataNum, labelDataDim) verifyData = inputData model.SetLstmTrainData(inputData, labelData) model.TrainLstmModule(epochNum, learnRate, statPeriod) torch.save(model.state_dict(), "lstm_model.pth") model.load_state_dict(torch.load("lstm_model.pth")) model.GetLstmModuleTrainRst(verifyData)
这是一个使用 LSTM 模型进行训练和预测的代码。它使用PyTorch库实现了一个LSTM类,其中包含了LSTM模型的定义、前向传播、损失函数和优化器的设置,以及训练和预测的方法。
在代码中,LSTM模型接受输入数据的维度、隐藏层维度、层数和批次大小作为参数进行初始化。在前向传播中,它使用LSTM层和全连接层对输入数据进行处理,并返回预测结果。
模型可以通过SetCriterion方法设置损失函数,通过SetOptimizer方法设置优化器。使用SetLstmTrainData方法将训练数据和标签数据传入模型,并通过TrainLstmModule方法进行模型训练。
GetLstmModuleTrainRst方法用于获取模型在验证数据上的预测结果。
在主函数中,定义了输入数据的维度、时间步长、隐藏层维度、层数、训练批次大小、训练轮数、学习率和统计周期等参数。然后创建了一个LSTM模型对象,并设置了损失函数和优化器。接着生成了随机的输入数据和标签数据,并调用SetLstmTrainData方法将其传入模型。最后调用TrainLstmModule方法进行模型训练,并保存模型参数到文件中。
如果你有任何问题,请随时问我。