dnn回归预测pytorch
时间: 2023-08-30 11:13:12 浏览: 134
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:
```python
import torch
import torch.nn as nn
import torch.optim as optim
```
2. 定义DNN模型:
```python
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:
```python
# 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:
```python
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:
```python
num_epochs = ...
阅读全文
相关推荐


















