PyTorch LSTM 模型参数定义与常见错误解决

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

常见错误解决

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，在输入数据和目标数据的大小上存在不匹配。具体来说，在模型的输出层中，我们将hidden state作为输入，然后将其压缩到output size维度。因此，我们需要确保目标数据的大小与output size相同。在这里，我们可以将目标数据调整为一个1维张量，大小为[batch_size]，其中batch_size为1。可以使用以下代码解决这个问题：

target = torch.tensor(y_new).view(-1) # 将target调整为1维张量