LSTM 模型参数定义及常见错误解决

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

常见错误：目标尺寸与输入尺寸不匹配

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.

这个警告提示我们，目标尺寸和输入尺寸不匹配，可能会导致错误的结果。我们需要确保它们具有相同的尺寸，可以通过调整代码来解决这个问题，例如使用正确的批量大小。

解决方法：

检查输入数据的维度: 确保输入数据的维度与模型的预期输入维度一致。
调整批量大小: 使用与目标尺寸匹配的批量大小。
使用 torch.broadcast_to 函数: 将输入数据广播到与目标尺寸相同的维度。

通过以上方法，您可以解决目标尺寸与输入尺寸不匹配的错误，并构建更准确的 LSTM 模型。