pytorch+snn
时间: 2023-12-12 20:04:03 浏览: 32
以下是使用PyTorch框架搭建简单SNN模型的步骤:
1.导入所需的库:
```python
import torch
import torch.nn as nn
import torch.optim as optim
```
2.准备数据集:
```python
import torch.utils.data as data
# 生成数据样例
X = torch.randn(1000, 2)
y = (X[:, 0] + X[:, 1] > 0float()
# 创建数据加载器
dataset = data.TensorDataset(X, y)
data_loader = data.DataLoader(dataset, batch_size=10, shuffle=True)
```
3.定义SNN模型:
```python
class SNN(nn.Module):
def __init__(self):
super(SNN, self).__init__()
self.fc1 = nn.Linear(2, 10)
self.fc2 = nn.Linear(10, 1)
def forward(self, x):
x = torch.relu(self.fc1(x))
x = torch.sigmoid(self.fc2(x))
return x
```
4.定义损失函数和优化器:
```python
model = SNN()
criterion = nn.BCELoss()
optimizer = optim.SGD(model.parameters(), lr=0.1)
```
5.训练SNN模型:
```python
for epoch in range(100):
running_loss = 0.0
for i, data in enumerate(data_loader, 0):
inputs, labels = data
optimizer.zero_grad()
outputs = model(inputs)
loss = criterion(outputs, labels.unsqueeze(1))
loss.backward()
optimizer.step()
running_loss += loss.item()
print('[%d] loss: %.3f' % (epoch + 1, running_loss / len(data_loader)))
```