pytorch-lightning 是建立在pytorch之上的高层次模型接口,pytorch-lightning之于pytorch,就如同keras之于tensorflow。 关于pytorch-lightning的完整入门介绍,可以参考我的另外一篇文章。 使用pytorch-lightning漂亮地进行深度学习研究 我用了约80行代码对 pytorch-lightning 做了
best_model_wts = model.state_dict() torch.save(best_model_wts,os.path.join(parameter_address, experiment_name + '.pkl')) # 保存最好的数据模型 # 每个epoch的训练结果都保存下来 model_wts = model.state_dict() # 保存训练模型 torch.save(model_wts,os.path.join(parameter_address, experiment_...
classLitModel(pl.LightningModule):def__init__(...):defforward(...):deftraining_step(...)deftraining_step_end(...)deftraining_epoch_end(...)defvalidation_step(...)defvalidation_step_end(...)defvalidation_epoch_end(...)deftest_step(...)deftest_step_end(...)deftest_epoch_end(.....
Linear(32,10) ) class Model(pl.LightningModule): def __init__(self,net, learning_rate=1e-3, use_CyclicLR = False, epoch_size=500): super().__init__() self.save_hyperparameters() #自动创建self.hparams self.net = net self.train_acc = Accuracy() self.val_acc = Accuracy() self...
TorchOptimizer集成了PyTorch Lightning的日志记录和检查点功能: trainer_args = { "logger": TensorBoardLogger(save_dir="logs"), "callbacks": [ModelCheckpoint(monitor="val_loss")] } 总结 TorchOptimizer通过集成贝叶斯优化和并行计算技术,为PyTorch Lightning模型提供了高效的超参数优化解决方案。其与PyTorch Ligh...
data和modle两个文件夹中放入__init__.py文件,做成包。这样方便导入。两个init文件分别是:from .data_interface import DInterface和from .model_interface import MInterface 在data_interface中建立一个class DInterface(pl.LightningDataModule):用作所有数据集文件的接口。__...
(32,10))classModel(pl.LightningModule):def__init__(self,net,learning_rate=1e-3):super().__init__()self.save_hyperparameters()self.net=net self.train_acc=Accuracy()self.val_acc=Accuracy()self.test_acc=Accuracy()defforward(self,x):x=self.net(x)returnx #定义loss deftraining_step(...
pytorch_lightning/callbacks/model_checkpoint.pyOutdated @@ -265,7 +276,8 @@ def _do_check_save(self, filepath, current, epoch): self.kth_value=self.best_k_models[self.kth_best_model] _op=minifself.mode=='min'elsemax self.best=_op(self.best_k_models.values()) ...
PyTorch Lightning + Hydra. A very user-friendly template for ML experimentation. ⚡🔥⚡ configtemplatedeep-learningbest-practicespytorchhydrareproducibilityproject-structuremlopspytorch-lightning UpdatedAug 16, 2024 Python sktime/pytorch-forecasting ...
import pytorch_lightning as ptl class CoolModel(ptl.LightningModule): def __init__(self): super(CoolModel, self).__init__() # not the best model... self.l1 = torch.nn.Linear(28 * 28, 10) def forward(self, x): return torch.relu(self.l1(x.view(x.size(0), -1))) ...