训练、评估和保存的代码
时间: 2024-06-11 09:10:35 浏览: 8
由于没有具体的任务或模型,因此无法提供完整的代码。以下是一个通用的训练、评估和保存代码框架,可以根据需要进行修改:
### 训练模型
```python
def train_model(model, train_data, valid_data, optimizer, criterion, epochs):
train_losses = []
valid_losses = []
best_valid_loss = float('inf')
best_model_state = None
for epoch in range(epochs):
train_loss = 0.0
valid_loss = 0.0
model.train()
for batch in train_data:
# 计算损失并更新模型
optimizer.zero_grad()
inputs, targets = batch
outputs = model(inputs)
loss = criterion(outputs, targets)
loss.backward()
optimizer.step()
train_loss += loss.item()
# 在验证集上计算损失
model.eval()
with torch.no_grad():
for batch in valid_data:
inputs, targets = batch
outputs = model(inputs)
loss = criterion(outputs, targets)
valid_loss += loss.item()
# 计算平均损失
train_loss /= len(train_data)
valid_loss /= len(valid_data)
train_losses.append(train_loss)
valid_losses.append(valid_loss)
print(f'Epoch {epoch + 1}/{epochs}: train_loss = {train_loss:.4f}, valid_loss = {valid_loss:.4f}')
# 如果验证集的损失更小,则保存模型
if valid_loss < best_valid_loss:
best_valid_loss = valid_loss
best_model_state = model.state_dict()
# 返回最好的模型参数和训练/验证损失
return best_model_state, train_losses, valid_losses
```
### 评估模型
```python
def eval_model(model, test_data, criterion):
test_loss = 0.0
model.eval()
with torch.no_grad():
for batch in test_data:
inputs, targets = batch
outputs = model(inputs)
loss = criterion(outputs, targets)
test_loss += loss.item()
test_loss /= len(test_data)
print(f'Test loss = {test_loss:.4f}')
```
### 保存模型
```python
def save_model(model_state, path):
torch.save(model_state, path)
print(f'Model saved to {path}')
```
使用示例:
```python
model = MyModel()
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
criterion = nn.CrossEntropyLoss()
train_data = DataLoader(...)
valid_data = DataLoader(...)
test_data = DataLoader(...)
best_model_state, train_losses, valid_losses = train_model(model, train_data, valid_data, optimizer, criterion, epochs=10)
eval_model(model, test_data, criterion)
save_model(best_model_state, 'model.pt')
```
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