ClassifierTrainer

class torcheeg.trainers.ClassifierTrainer(model: Module, num_classes: int, lr: float = 0.001, weight_decay: float = 0.0, devices: int = 1, accelerator: str = 'cpu', metrics: List[str] = ['accuracy'])

A generic trainer class for EEG classification.

trainer = ClassifierTrainer(model)
trainer.fit(train_loader, val_loader)
trainer.test(test_loader)

Parameters:

• model (nn.Module) – The classification model, and the dimension of its output should be equal to the number of categories in the dataset. The output layer does not need to have a softmax activation function.

• num_classes (int, optional) – The number of categories in the dataset. If None, the number of categories will be inferred from the attribute num_classes of the model. (defualt: None)

• lr (float) – The learning rate. (default: 0.001)

• weight_decay (float) – The weight decay. (default: 0.0)

• devices (int) – The number of devices to use. (default: 1)

• accelerator (str) – The accelerator to use. Available options are: ‘cpu’, ‘gpu’. (default: "cpu")

• metrics (list of str) – The metrics to use. Available options are: ‘precision’, ‘recall’, ‘f1score’, ‘accuracy’, ‘matthews’, ‘auroc’, and ‘kappa’. (default: ["accuracy"])

fit(train_loader: DataLoader, val_loader: DataLoader, max_epochs: int = 300, *args, **kwargs) → Any

Parameters:

• train_loader (DataLoader) – Iterable DataLoader for traversing the training data batch (torch.utils.data.dataloader.DataLoader, torch_geometric.loader.DataLoader, etc).

• val_loader (DataLoader) – Iterable DataLoader for traversing the validation data batch (torch.utils.data.dataloader.DataLoader, torch_geometric.loader.DataLoader, etc).

• max_epochs (int) – Maximum number of epochs to train the model. (default: 300)

test(test_loader: DataLoader, *args, **kwargs) → List[Dict[str, float]]

Parameters:

test_loader (DataLoader) – Iterable DataLoader for traversing the test data batch (torch.utils.data.dataloader.DataLoader, torch_geometric.loader.DataLoader, etc).