out, _ = self.lstm(x, (h0_lstm, c0_lstm))

在这段代码中，self.lstm是一个LSTM模型，x是输入的数据，h0_lstm和c0_lstm是LSTM模型的初始隐藏状态和记忆状态。LSTM模型会根据输入数据x和初始状态(h0_lstm, c0_lstm)进行计算，得到输出out和最终隐藏状态和记忆状态，由于我们在这里只需要out这个输出，因此使用 _ 来代表最终的隐藏状态和记忆状态。因为在Python中 _ 表示一个临时变量，用于存储不需要的变量，这个变量可以直接被忽略不做任何处理。

相关问题

以上问题报错的地方是冒号后面的代码，请求给出修改方法： def forward(self, x): h0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(device) c0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(device) x = x.view(-1, self.look_back, 1) out, _ = self.lstm1(x, (h0, c0)) out, _ = self.lstm2(out, (h0, c0)) out = self.fc(out[:, -1, :]) return out

从你提供的代码来看，问题依旧出现在 `to()` 方法的调用上。为了解决这个问题，需要确保 `device` 变量在 `forward()` 方法中得以定义，并且能够正确地传递给 `to()` 方法。

一种简单的解决方法是将 `device` 作为 `forward()` 方法的参数传递进来，例如：

def forward(self, x, device):
    h0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(device)
    c0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(device)
    
    x = x.view(-1, self.look_back, 1)
    
    out, _ = self.lstm1(x, (h0, c0))
    out, _ = self.lstm2(out, (h0, c0))
    
    out = self.fc(out[:, -1, :])
    
    return out

这样，在调用 `forward()` 方法时，可以将 `device` 变量传递进去，例如：

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = MyModel(...)
x = torch.randn(10, 5, 1)
y_pred = model.forward(x, device=device)

这样就可以避免 `'int' object is not callable` 的错误了。

class LSTM(nn.Module): def __init__(self, input_dim, hidden_dim, output_dim, num_layers): super(LSTM, self).__init__() self.hidden_dim = hidden_dim self.num_layers = num_layers self.lstm = nn.LSTM(input_dim, hidden_dim, num_layers, batch_first=True) self.fc = nn.Linear(hidden_dim, output_dim) def forward(self, x): h0 = torch.zeros(self.num_layers, x.size(0), self.hidden_dim).to(device) c0 = torch.zeros(self.num_layers, x.size(0), self.hidden_dim).to(device) out, _ = self.lstm(x, (h0, c0)) out = self.fc(out) return out[:, -1, :]为我的这段代码添加一个dropout层

可以在LSTM类的__init__方法中添加一个nn.Dropout层，如下所示：

class LSTM(nn.Module):
def __init__(self, input_dim, hidden_dim, output_dim, num_layers):
super(LSTM, self).__init__()
self.hidden_dim = hidden_dim
self.num_layers = num_layers
self.lstm = nn.LSTM(input_dim, hidden_dim, num_layers, batch_first=True)
self.dropout = nn.Dropout(p=.5) # 添加dropout层
self.fc = nn.Linear(hidden_dim, output_dim)

def forward(self, x):
h = torch.zeros(self.num_layers, x.size(), self.hidden_dim).to(device)
c = torch.zeros(self.num_layers, x.size(), self.hidden_dim).to(device)
out, _ = self.lstm(x, (h, c))
out = self.dropout(out) # 在LSTM输出后添加dropout层
out = self.fc(out)
return out[:, -1, :]