Question

我最近开始使用Ignite，发现它非常有趣。我想使用torch.optim模块中的LBFGS算法作为优化器来训练模型。

这是我的代码：

from ignite.engine import Events, Engine, create_supervised_trainer, create_supervised_evaluator
from ignite.metrics import RootMeanSquaredError, Loss
from ignite.handlers import EarlyStopping

 D_in, H, D_out = 5, 10, 1
 model = simpleNN(D_in, H, D_out) # a simple MLP with 1 Hidden Layer
 model.double()
 train_loader, val_loader = get_data_loaders(i)

 optimizer = torch.optim.LBFGS(model.parameters(), lr=1)
 loss_func = torch.nn.MSELoss()  

 #Ignite
 trainer = create_supervised_trainer(model, optimizer, loss_func)
 evaluator = create_supervised_evaluator(model, metrics={'RMSE': RootMeanSquaredError(),'LOSS': Loss(loss_func)})

 @trainer.on(Events.ITERATION_COMPLETED)
 def log_training_loss(engine):
     print("Epoch[{}] Loss: {:.5f}".format(engine.state.epoch, len(train_loader), engine.state.output))

def score_function(engine):
    val_loss = engine.state.metrics['RMSE']
    print("VAL_LOSS: {:.5f}".format(val_loss))
    return -val_loss

handler = EarlyStopping(patience=10, score_function=score_function, trainer=trainer)
evaluator.add_event_handler(Events.COMPLETED, handler)

trainer.run(train_loader, max_epochs=100)

引起的错误是： TypeError: step() missing 1 required positional argument: 'closure'

我知道为LBFGS的实现定义闭包是必需的，所以我的问题是如何使用ignite做到这一点？还是有其他方法可以做到这一点？

Answer 1

方法如下：

from ignite.engine import Engine

model = ...
optimizer = torch.optim.LBFGS(model.parameters(), lr=1)
criterion = 

def update_fn(engine, batch):
    model.train()
    x, y = batch
    # pass to device if needed as here: https://github.com/pytorch/ignite/blob/40d815930d7801b21acfecfa21cd2641a5a50249/ignite/engine/__init__.py#L45
    def closure():
        y_pred = model(x)
        loss = criterion(y_pred, y)
        optimizer.zero_grad()
        loss.backward()
        return loss

    optimizer.step(closure)

trainer = Engine(update_fn)

# everything else is the same

Source

Answer 2

你需要用 zero_grad 封装所有求值步骤，并在返回步骤

    for batch in loader():
        def closure():
          ...
          return loss
        optim.step(closure)

Pytorch docs for 'closure'

pytorch ignite如何使用LBFGS优化器？

2 个答案: