RuntimeRorr：对于打开的2D输入，HX和CX也应为2-D，但得到（3-D，3-D）张量

Question

嘿，我的LSTM有一些问题。我有6个功能，我一次在LSTM中发送所有数据（29002行）（这是个好主意吗？）。

的输入是大小：

训练形状火炬。

我 ，1]）

我的模型：

class LSTM(nn.Module):
def __init__(self, hidden_dim_LSTM, num_layers_LSTM, hidden1, drop):
    super(LSTM, self).__init__()
    
    self.hidden_dim_LSTM = hidden_dim_LSTM
    self.num_layers_LSTM = num_layers_LSTM
    self.hidden1=hidden1
    self.drop=drop
    
    final_output_dim = 1

    #self.lstm = nn.LSTM(self.input_dim, self.hidden_dim, self.num_layers, batch_first=True)
    self.lstm = nn.LSTM(6, hidden_size=hidden_dim_LSTM, num_layers=num_layers_LSTM, batch_first=True)
    self.fc1 = nn.Linear(in_features=hidden_dim_LSTM, out_features=hidden1)
    self.drop = nn.Dropout(drop)
    self.fc2 = nn.Linear(in_features=hidden1, out_features=final_output_dim) 

    
def forward(self, x):
    h_0 = Variable(torch.zeros(self.num_layers_LSTM, x.size(0), self.hidden_dim_LSTM)).requires_grad_().to(device) #hidden state
    c_0 = Variable(torch.zeros(self.num_layers_LSTM, x.size(0), self.hidden_dim_LSTM)).requires_grad_().to(device) #internal state
    # Propagate input through LSTM
    output, (hn, cn) = self.lstm(x, (h_0, c_0)) #lstm with input, hidden, and internal state
    hn = hn.view(-1, self.hidden_dim_LSTM) #reshaping the data for Dense layer next
    out = F.relu(hn)
    out = self.fc1(out) 
    out = self.drop(out)
    out = torch.relu(out) 
    #out = self.drop(out)
    out = self.fc2(out) 
    return out

当我开始培训时，我会收到此错误： RuntimeRorr：对于打开的2D输入，HX和CX也应为2-D，但得到（3-D，3-D）张量 在线：输出，（HN，CN）= self.lstm（x，（h_0，c_0））

我都非常感谢每一个帮助！