class LSTMForecaster(nn.Module): def __init__(self, n_features, n_hidden, n_outputs, sequence_len, n_lstm_layers=1, n_deep_layers=10, use_cuda=False, dropout=0.2): ''' n_features: number of input features (1 for univariate forecasting) n_hidden: number of neurons in ...
num_layers=n_lstm_layers, batch_first=True)# As we have transformed our data in this way# first dense after lstmself.fc1=nn.Linear(n_hidden*sequence_len, n_hidden)# Dropout layerself.dropout=nn.Dropout(p=dropout)# Create fully connected layers (n_hidden x n_deep_layers)dnn_layers= [...
接下来,我们展示一个简单的LSTM时间序列预测模型的代码示例。 importtorchimporttorch.nnasnnimportnumpyasnpimportpandasaspdfromsklearn.preprocessingimportMinMaxScalerfromtorch.utils.dataimportDataLoader,Dataset# 数据集类classTimeSeriesDataset(Dataset):def__init__(self,data,seq_length):self.data=data self.seq_...
importtorchimporttorch.nnasnnclassLSTMModel(nn.Module):def__init__(self,input_size,hidden_size,output_size):super(LSTMModel,self).__init__()self.lstm=nn.LSTM(input_size,hidden_size,batch_first=True)self.fc=nn.Linear(hidden_size,output_size)defforward(self,x):_,(h_n,_)=self.lstm(x...
model = LSTM(input_size,hidden_size,num_layers,num_output) optimizer = torch.optim.SGD(model.parameters(), lr=0.01) loss_func = nn.MSELoss() train_loss_all = [] for epoch in range(max_epoch): train_loss = 0 train_num = 0 ...
model.py:importtorch.nnasnnclassLSTMModel(nn.Module):def__init__(self,input_size=1,hidden_size...
Time Series Prediction using LSTM with PyTorch in Pythonstackabuse.com/time-series-prediction-using-lstm-with-pytorch-in-python/ 时间序列数据,顾名思义,是一种随时间变化的数据类型。例如,24小时时间段内的温度,一个月内各种产品的价格,某一特定公司一年内的股票价格。先进的深度学习模型,如Long Short Term...
https://stackabuse.com/time-series-prediction-using-lstm-with-pytorch-in-python/ 时间序列数据,顾名思义是一种随时间变化的数据类型。例如,24小时时间段内的温度,一个月内各种产品的价格,一个特定公司一年的股票价格。高级的深度学习模型,如长短期记忆网络(LSTM),能够捕捉时间序列数据中的模式,因此可以用来预测...
https://zh.gluon.ai/chapter_recurrent-neural-networks/lang-model.html 翻译自: https://stackabuse.com/seaborn-library-for-data-visualization-in-python-part-1/ https://stackabuse.com/time-series-prediction-using-lstm-with-pytorch-in-python/ ...
在PyTorch中处理时间序列数据任务通常需要使用torch.nn.RNN, torch.nn.LSTM, torch.nn.GRU等递归神经网络模块,以及torch.utils.data.Dataset和torch.utils.data.DataLoader等数据加载工具。 以下是一个简单的示例,演示如何使用PyTorch处理一个时间序列数据任务: 创建一个自定义的Dataset类,用于加载时间序列数据: import...