Lightning allows the user to test their models with any compatible test dataloaders. This can be done before/after training and is completely agnostic to fit() ...

The test set is NOT used during training, it is ONLY used once the model has been trained to see how the model will do in the real-world.

Lightning forces the user to run the test set separately to make sure it isn't evaluated by mistake. Testing is performed using the trainer object's .test() ...

To test models that require GPU make sure to run the above command on a GPU machine. The GPU machine must have at least 2 GPUs to run distributed tests.

Running the training, validation and test dataloaders. Calling the Callbacks at the appropriate times. Putting batches and computations on the correct devices.

12 янв. 2022 г. · Test is used for a holdout section of your dataset, used to evaluate a model after you've done hyperparameter tuning for fair comparison.

Add a validation and test loop to avoid overfitting. basic. Intermediate. Learn about more complex validation and test workflows. intermediate.

Test set. Lightning forces the user to run the test set separately to make sure it isn't evaluated by mistake. Test after ...

1 авг. 2022 г. · I can use torchmetrics.Accuracy to accumulate accuracy. But what is the proper way to combine that and get the total accuracy out?

To evaluate the performance of your model after training, you can utilize the test() method provided by the Trainer class in PyTorch Lightning.