解释这段代码class LSTM(nn.Module): def __init__(self,p,input_size, output_size, hidden_layer_size,past_history_size): """ :param input_size: 输入数据的维度 :param hidden_layer_size:隐层的数目 :param output_size: 输出的个数 """ super().__init__() # self.hidden_layer_size1 = hidden_layer_size*past_history_size self.hidden_layer_size1 = hidden_layer_size self.lstm = nn.LSTM(input_size, hidden_layer_size, batch_first=True) self.linear1 = nn.Linear(self.hidden_layer_size1, output_size) self.linear2 = nn.Linear(self.hidden_layer_size1, output_size) self.linear3 = nn.Linear(self.hidden_layer_size1, output_size) self.linear4 = nn.Linear(self.hidden_layer_size1, output_size) self.linear5 = nn.Linear(self.hidden_layer_size1, output_size) self.dropout = nn.Dropout(p=p) def forward(self, input_x): lstm_out, self.hidden_cell = self.lstm(input_x) lstm_out = self.dropout(lstm_out) # lstm_out = lstm_out.reshape(len(input_x),-1) linear_out1, linear_out2,linear_out3, linear_out4, linear_out5 = self.linear1(lstm_out), self.linear2(lstm_out),self.linear3(lstm_out), self.linear4(lstm_out),self.linear5(lstm_out) # print(linear_out1.shape) linear_out1, linear_out2,linear_out3, linear_out4, linear_out5 = linear_out1[:, -1, :], linear_out2[:, -1, :],linear_out3[:, -1, :], linear_out4[:, -1, :],linear_out5[:, -1, :] linear_out = torch.stack([linear_out1, linear_out2,linear_out3, linear_out4,linear_out5], dim=-1) linear_out = torch.squeeze(linear_out) return linear_out

这段代码定义了一个名为LSTM的神经网络类，继承自PyTorch中的nn.Module类。该类包含了一个LSTM层和5个全连接层，用于将LSTM层的输出映射到5个不同的输出。其中，输入数据的维度为input_size，隐层的数目为hidden_layer_size，输出的个数为output_size。在前向传播时，输入数据input_x首先被传入LSTM层中，得到LSTM层的输出lstm_out和隐藏状态self.hidden_cell。然后，lstm_out经过一个dropout层进行正则化处理，再分别经过5个全连接层进行线性变换，得到5个输出。最后，这5个输出通过torch.stack函数沿着最后一维进行拼接，得到一个形状为[batch_size, output_size, 5]的张量，再通过torch.squeeze函数将其压缩为形状为[batch_size, output_size]的张量，并作为前向传播的输出返回。