LSTM算法接受三个输入:前一个隐藏状态、前一个单元状态和当前输入。hidden_cell变量包含以前的隐藏状态和单元格状态。lstm和linear layer变量用于创建lstm和linear layer。 在forward方法内部,input_seq作为参数传递,首先通过lstm层传递。lstm层的输出是当前时间步长的隐藏状态和单元状态,以及输出。lstm层的输出被传递到线...
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,...
LSTM算法接受三个输入:先前的隐藏状态,先前的单元状态和当前输入。 hidden_cell变量包含先前的hidden和cell状态。 lstm和线性层变量用于创建LSTM和线性层。 在forward方法内部,将input_seq作为参数传递,该参数首先通过lstm层传递。 lstm层的输出是当前时间步的隐藏状态和单元状态,以及输出。 lstm层的输出将传递到...
for each training example n_deep_layers: number of hidden dense layers after the lstm layer sequence_len: number of steps to look back at for prediction dropout: float (0 < dropout < 1) dropout ratio between dense layers ''' super().__init__() self.n_lstm_layers =...
Anomaly-Detection/TimeSeriesPrediction-lstm2 at main · ziwenhahaha/Anomaly-Detection (github.com)github.com/ziwenhahaha/Anomaly-Detection/tree/main/TimeSeriesPrediction-lstm2 效果 头文件 importwarningswarnings.filterwarnings("ignore")#忽略告警# 数学库importmathimportnumpyasnpimportpandasaspd# 读写数据...
1.PyTorch–用循环神经网络LSTM预测时间序列 主要看他的训练模型部分代码 epochs = 150 for i in range(epochs): for seq, labels in train_inout_seq: #主要是这句话! optimizer.zero_grad() model.hidden_cell = (torch.zeros(1, 1, model.hidden_layer_size), ...
n_deep_layers: number of hidden dense layers after the lstm layer sequence_len: number of steps to look back at for prediction dropout: float (0 < dropout < 1) dropout ratio between dense layers '''super().__init__() self.n_lstm_layers=n_lstm_layers ...
layern_outputs:numberofoutputs to predictforeach training examplen_deep_layers:numberofhidden dense layers after the lstm layersequence_len:numberofsteps to look back atforpredictiondropout:float(0<dropout<1)dropout ratio between dense layers'''super().__init__()self.n_lstm_layers=n_lstm_...
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 = 10,后跟一个nn.Linear(10, 1),该层输出股票...