PyTorch LSTM模型训练中目标张量维度不匹配问题解决方案

在使用PyTorch训练LSTM模型时，可能会遇到以下错误：

UserWarning: Using a target size (torch.Size([3090])) that is different to the input size (torch.Size([1])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.

这个问题是由于目标张量的维度（target tensor dimension）与输入张量的维度不匹配导致的。目标张量应该与输出张量具有相同的维度。在这种情况下，可以使用unsqueeze方法来添加一个额外的维度，使目标张量变成二维张量（two-dimensional tensor）。

以下代码演示了如何解决这个问题：

inputs = torch.from_numpy(X_new).float().cuda()
targets = torch.from_numpy(data['Close_Amplitude'].values).float().cuda().unsqueeze(1)

# 定义模型参数
input_size = X_new.shape[1]
hidden_size = len(X_new)
num_layers = 2
output_size = 1
batch_size = 1

# 定义LSTM模型
class LSTM(nn.Module):
    def __init__(self, input_size, hidden_size, num_layers, output_size, batch_size):
        super(LSTM, self).__init__()
        self.hidden_size = hidden_size
        self.num_layers = num_layers
        self.batch_size = batch_size
        self.lstm = nn.LSTM(input_size, hidden_size, num_layers, batch_first=True)
        self.fc = nn.Linear(hidden_size, output_size)

    def forward(self, x):
        batch_size = self.batch_size # 获取输入数据的batch_size
        h0 = torch.zeros(self.num_layers, batch_size, self.hidden_size).cuda()
        c0 = torch.zeros(self.num_layers, batch_size, self.hidden_size).cuda()
        x = x.unsqueeze(0)
        out, _ = self.lstm(x, (h0, c0))
        out = out.squeeze(0)
        out = self.fc(out[:, -1, :])
        return out

现在，目标张量的维度与输入张量的维度匹配，代码应该可以正常工作了。