tw: int, pw: int, target_columns, drop_targets=False): ''' df: Pandas DataFrame of the univariate time-series tw: Training Window - Integer defining how many steps to look back pw: Prediction Window - Integer defining how many steps forward to predict returns...
hidden_size,batch_first=True)self.fc=nn.Linear(hidden_size,output_size)defforward(self,x):out,_=self.lstm(x)out=self.fc(out[:,-1,:])# 只取最后一个时间步的输出returnout# 创建模型实例input_size=
1model =LSTM()2loss_function =nn.MSELoss()3optimizer = torch.optim.Adam(model.parameters(),lr=0.001) 1#模型的训练2epochs = 1534foriinrange(epochs):5forseq, labelsintrain_inout_seq:6optimizer.zero_grad()7model.hidden_cell = (torch.zeros(1,1,model.hidden_layer_size),8torch.zeros(1,...
tw:int,pw:int,target_columns,drop_targets=False):'''df: Pandas DataFrame of the univariate time-seriestw: Training Window - Integer defining how many steps to look backpw: Prediction Window - Integer defining how many steps forward to predictreturns: dictionary of sequences and targets for al...
https://stackabuse.com/seaborn-library-for-data-visualization-in-python-part-1/ https://stackabuse.com/time-series-prediction-using-lstm-with-pytorch-in-python/ 顾名思义,时间序列数据是随时间变化的一种数据类型。例如,24小时内的温度,一个月内各种产品的价格,一年中特定公司的股票价格。诸如长期短期记...
df: Pandas DataFrame of the univariate time-series tw: Training Window - Integer defining how many steps to look back pw: Prediction Window - Integer defining how many steps forward to predict returns: dictionary of sequences and targets for all sequences ...
class LSTMDataset(Dataset): ''' x: Features. y: Targets, if none, do prediction. ''' def __init__(self, x, window_size, y=None): self.window_size = window_size if y is None: self.y = y else: self.y = torch.FloatTensor(y) self.x = torch.FloatTensor(x) def __getitem...
本案例使用真实的心电图 (ECG) 数据来检测患者心跳的异常情况。我们将一起构建一个 LSTM 自动编码器,使用来自单个心脏病患者的真实心电图数据对其进行训练,并将在新的样本中,使用训练好的模型对其进行预测分类为正常或异常来来检测异常心跳。
time_stamps[timestep_size:]).float() labels = torch.tensor(labels[timestep_size:]).float() return features, labels创建神经网络类 我们的网络类接收variantal_estimator装饰器，该装饰器可简化对贝叶斯神经网络损失的采样。我们的网络具有一个贝叶斯LSTM层，参数设置为in_features = 1以及out_features...
reinforcement-learning deep-learning generative-adversarial-network lstm-neural-networks bidirectional-rnn cnn-pytorch Updated Apr 17, 2024 Python srushtii-m / Jersey-City-CitiBike-Demand-Prediction Star 2 Code Issues Pull requests Analyzing and predicting the demand for bikes using a Spatio-Tempora...