GraphClassifier

class flash.graph.classification.model.GraphClassifier(num_features, num_classes=None, labels=None, backbone='GCN', backbone_kwargs={}, pooling_fn='mean', head=None, loss_fn=torch.nn.functional.cross_entropy, learning_rate=None, optimizer='Adam', lr_scheduler=None, metrics=None)

The GraphClassifier is a Task for classifying graphs. For more details, see Graph Classification.

Parameters

• num_features (int) – The number of features in the input.

• num_classes (int) – Number of classes to classify.

• backbone (Union[str, Tuple[Module, int]]) – Name of the backbone to use.

• backbone_kwargs (Optional[Dict]) – Dictionary dependent on the backbone, containing for example in_channels, out_channels, hidden_channels or depth (number of layers).

• pooling_fn (Union[str, Callable, None]) – The global pooling operation to use (one of: “max”, “max”, “add” or a callable).

• head (Union[Callable, Module, None]) – The head to use.

• loss_fn (TypeVar(LOSS_FN_TYPE, Callable, Mapping, Sequence, None)) – Loss function for training, defaults to cross entropy.

• learning_rate (Optional[float]) – Learning rate to use for training.

• optimizer (TypeVar(OPTIMIZER_TYPE, str, Callable, Tuple[str, Dict[str, Any]], None)) – Optimizer to use for training.

• lr_scheduler (Optional[TypeVar(LR_SCHEDULER_TYPE, str, Callable, Tuple[str, Dict[str, Any]], Tuple[str, Dict[str, Any], Dict[str, Any]], None)]) – The LR scheduler to use during training.

• metrics (Optional[TypeVar(METRICS_TYPE, Metric, Mapping, Sequence, None)]) – Metrics to compute for training and evaluation.

classmethod available_finetuning_strategies(cls)

Returns a list containing the keys of the available Finetuning Strategies.

Return type

List[str]

classmethod available_lr_schedulers(cls)

Returns a list containing the keys of the available LR schedulers.

Return type

List[str]

classmethod available_optimizers(cls)

Returns a list containing the keys of the available Optimizers.

Return type

List[str]