dnn回归预测pytorch

对于使用PyTorch进行DNN回归预测的问题，你可以按照以下步骤进行操作：

1. 导入所需的库和模块：

   import torch
   import torch.nn as nn
   import torch.optim as optim

2. 定义DNN模型：

   class RegressionModel(nn.Module):
       def __init__(self, input_dim, hidden_dim, output_dim):
           super(RegressionModel, self).__init__()
           self.hidden_layer = nn.Linear(input_dim, hidden_dim)
           self.output_layer = nn.Linear(hidden_dim, output_dim)

       def forward(self, x):
           x = torch.relu(self.hidden_layer(x))
           x = self.output_layer(x)
           return x

3. 准备数据：

   # 假设有训练数据 train_x 和对应的标签 train_y
   train_x = ...
   train_y = ...

4. 定义模型超参数和优化器：

   input_dim = train_x.shape[1]
   hidden_dim = ...
   output_dim = ...
   
   model = RegressionModel(input_dim, hidden_dim, output_dim)
   criterion = nn.MSELoss()
   optimizer = optim.SGD(model.parameters(), lr=0.01)

5. 进行模型训练：

   num_epochs = ...