cogdl.models.nn.pyg_gcnmix

Module Contents

Classes

GCNConv
BaseGNNMix
GCNMix

Functions

mix_hidden_state(feat, target, train_index, alpha)
sharpen(prob, temperature)
get_one_hot_label(labels, index)
get_current_consistency_weight(final_consistency_weight, rampup_starts, rampup_ends, epoch)

cogdl.models.nn.pyg_gcnmix.mix_hidden_state(feat, target, train_index, alpha)

cogdl.models.nn.pyg_gcnmix.sharpen(prob, temperature)

cogdl.models.nn.pyg_gcnmix.get_one_hot_label(labels, index)

cogdl.models.nn.pyg_gcnmix.get_current_consistency_weight(final_consistency_weight, rampup_starts, rampup_ends, epoch)

class cogdl.models.nn.pyg_gcnmix.GCNConv(in_feats, out_feats)

Bases: torch.nn.Module

forward(self, x, edge_index, edge_attr=None)

forward_aux(self, x)

class cogdl.models.nn.pyg_gcnmix.BaseGNNMix(in_feat, hidden_size, num_classes, k, temperature, alpha, dropout)

Bases: cogdl.models.BaseModel

forward(self, x, edge_index)

forward_aux(self, x, label, train_index, mix_hidden=True, layer_mix=1)

update_aux(self, data, vector_labels, train_index, opt)

update_soft(self, data, labels, train_index)

loss(self, data, opt)

predict_noise(self, data, tau=1)

class cogdl.models.nn.pyg_gcnmix.GCNMix(in_feat, hidden_size, num_classes, k, temperature, alpha, rampup_starts, rampup_ends, final_consistency_weight, ema_decay, dropout)

Bases: cogdl.models.BaseModel

static add_args(parser)

Add model-specific arguments to the parser.

classmethod build_model_from_args(cls, args)

Build a new model instance.

forward(self, x, edge_index)

forward_ema(self, x, edge_index)

loss(self, data)

predict(self, data)