如何将 LBFGS 优化器与 pytorch ignite 一起使用?

Question

我最近开始使用Ignite，我发现它非常有趣。 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)

引发的错误是：TypeError: step() missing 1 required positional argument: 'closure'

我知道这是定义 LBFGS 实现闭包所必需的，所以我的问题是如何使用 ignite 做到这一点？ 还是有另一种方法可以做到这一点？

Answer 1

这样做的方法是这样的：

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

源

Answer 2

您需要封装所有评估步骤，其中包含zero_grad和返回步骤

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

用于"关闭"的 Pytorch 文档