6/15/2023 0 Comments Image tuner![]() The names ‘learning_rate’ or ‘lr’ getĪutomatically detected. Update_attr ¶ ( bool) – Whether to update the learning rate attribute or not.Īttr_name ¶ ( str) – Name of the attribute which stores the learning rate. Loss at any point is larger than early_stop_threshold*best_loss 'linear': Increases the learning rate linearly.Įarly_stop_threshold ¶ ( float) – Threshold for stopping the search. 'exponential': Increases the learning rate exponentially. Search strategy to update learning rate after each batch: Num_training ¶ ( int) – number of learning rates to test Max_lr ¶ ( float) – maximum learning rate to investigate Min_lr ¶ ( float) – minimum learning rate to investigate It can be any of ("fit", "validate", "test", "predict"). Method ¶ ( Literal) – Method to run tuner on. Samples used for running tuner on validation/testing/prediction.ĭatamodule ¶ ( Optional) – An instance of LightningDataModule. Val_dataloaders ¶ ( Optional) – A or a sequence of them specifying validation samples.ĭataloaders ¶ ( Optional) – A or a sequence of them specifying val/test/predict In the case of multiple dataloaders, please see this section. LightningDataModule specifying training samples. Train_dataloaders ¶ ( Union) – A collection of or a Model ¶ ( LightningModule) – Model to tune. lr_find ( model, train_dataloaders = None, val_dataloaders = None, dataloaders = None, datamodule = None, method = 'fit', min_lr = 1e-08, max_lr = 1, num_training = 100, mode = 'exponential', early_stop_threshold = 4.0, update_attr = True, attr_name = '' ) ¶Įnables the user to do a range test of good initial learning rates, to reduce the amount of guesswork in Multi-agent Reinforcement Learning With WarpDrive.Finetune Transformers Models with PyTorch Lightning.PyTorch Lightning CIFAR10 ~94% Baseline Tutorial.GPU and batched data augmentation with Kornia and PyTorch-Lightning. ![]()
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