标签: pytorch-ignite

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

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'] …