WCELossTrainer

class torcheeg.trainers.WCELossTrainer(model: Module, num_classes: int, class_frequency: List[int] | DataLoader, rule: str = 'reweight', beta_reweight: float = 0.9999, drw_epochs: int = 160, lr: float = 0.001, weight_decay: float = 0.0, devices: int = 1, accelerator: str = 'cpu', metrics: List[str] = ['accuracy'])

A trainer class for EEG classification with Weighted Cross Entropy (WCE) loss for imbalanced datasets.

from torcheeg.models import CCNN
from torcheeg.trainers import WCELossTrainer

model = CCNN(in_channels=5, num_classes=2)
trainer = WCELossTrainer(model, num_classes=2, class_frequency=[10, 20])

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) – The number of classes in the dataset.

• class_frequency (List[int] or Dataloader) – The frequency of each class in the dataset. It can be a list of integers or a dataloader to calculate the frequency of each class in the dataset, traversing the data batch (torch.utils.data.dataloader.DataLoader, torch_geometric.loader.DataLoader, etc). (default: None)

• gamma (float) – The gamma parameter. (default: 1.0)

• rule (str) – The rule to adjust the weight of each class. Availabel options are: ‘reweight’, ‘drw’ (deferred re-balancing optimization schedule). (default: reweight)

• beta_reweight (float) – The beta parameter for reweighting. It is only used when rule is ‘reweight’ or ‘drw’. (default: 0.9999)

• drw_epochs (int) – The number of epochs to use DRW. It is only used when rule is ‘drw’. (default: 160)

• 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. Availabel options are: ‘cpu’, ‘gpu’. (default: "cpu")