configure_optimizers() 定义优化器和LR调度器3.1 Lightning 数据集加载数据集有两种实现方法:直接调用第三方公开数据集(如:MNIST等数据集) 自定义数据集(自己去继承torch.utils.data.dataset.Dataset,自定义类)3.1.1 使用公开数据集from torch.utils.data import DataLoader, random_split import pytorch_lightning as...
在这个示例中,我们定义了一个简单的MNIST分类模型CustomMNIST,它继承自LightningModule。我们实现了forward方法来定义模型的前向传播逻辑,training_step方法来定义训练步骤中的损失计算逻辑,以及configure_optimizers方法来配置模型优化器。最后,我们使用Trainer类来训练模型。
self.validation_step_outputs.append(auroc_res) return loss def on_validation_epoch_end(self): all_outs = torch.stack(self.validation_step_outputs) print(all_outs.sum()) self.validation_step_outputs.clear() def configure_optimizers(self): optimizer = torch.optim.Adam(self.parameters(), lr=1...
()returnlossdefconfigure_optimizers(self):# note: we are passing in all params, including the one that is not usedreturntorch.optim.SGD(self.parameters(),lr=0.1)train_data=DataLoader(RandomDataset(32,64),batch_size=2)model=BoringModel()trainer=Trainer(default_root_dir=os.getcwd(),max_...
Pretrain, finetune and deploy AI models on multiple GPUs, TPUs with zero code changes. - pytorch-lightning/src/lightning/pytorch/core/module.py at master · Lightning-AI/pytorch-lightning
importpytorch_lightningaspllogger=pl.logging.TensorBoardLogger(...) But I receive an error: AttributeError: module 'logging' has no attribute 'TensorBoardLogger' To Reproduce ubuntu@ip-172-31-41-72:~$ mkdir pltest ubuntu@ip-172-31-41-72:~$cdpltest/ ubuntu@ip-172-31-41-72:~/pltest$ ...