Source code for cogdl.models.nn.mlp

from .. import BaseModel
from cogdl.layers import MLP as MLPLayer
from import Graph

[docs]class MLP(BaseModel):
[docs] @staticmethod def add_args(parser): """Add model-specific arguments to the parser.""" # fmt: off parser.add_argument("--num-features", type=int) parser.add_argument("--num-classes", type=int) parser.add_argument("--hidden-size", type=int, default=16) parser.add_argument("--num-layers", type=int, default=2) parser.add_argument("--dropout", type=float, default=0.5) parser.add_argument("--norm", type=str, default=None) parser.add_argument("--activation", type=str, default="relu")
# fmt: on
[docs] @classmethod def build_model_from_args(cls, args): return cls( args.num_features, args.num_classes, args.hidden_size, args.num_layers, args.dropout, args.activation, args.norm, args.act_first if hasattr(args, "act_first") else False, )
def __init__( self, in_feats, out_feats, hidden_size, num_layers, dropout=0.0, activation="relu", norm=None, act_first=False, bias=True, ): super(MLP, self).__init__() self.nn = MLPLayer(in_feats, out_feats, hidden_size, num_layers, dropout, activation, norm, act_first, bias)
[docs] def forward(self, x): if isinstance(x, Graph): x = x.x return self.nn(x)
[docs] def predict(self, data): return self.forward(data.x)