import torch
import torch.nn as nn
import torch.nn.functional as F
from .. import BaseModel, register_model
[docs]@register_model("mlp")
class MLP(BaseModel):
@staticmethod
[docs] 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)
# fmt: on
@classmethod
[docs] def build_model_from_args(cls, args):
return cls(
args.num_features,
args.num_classes,
args.hidden_size,
args.num_layers,
args.dropout,
)
def __init__(self, num_features, num_classes, hidden_size, num_layers, dropout):
super(MLP, self).__init__()
self.num_features = num_features
self.num_classes = num_classes
self.hidden_size = hidden_size
self.num_layers = num_layers
self.dropout = dropout
shapes = [num_features] + [hidden_size] * (num_layers - 1) + [num_classes]
self.mlp = nn.ModuleList(
[nn.Linear(shapes[layer], shapes[layer + 1]) for layer in range(num_layers)]
)
[docs] def forward(self, x, edge_index):
for fc in self.mlp[:-1]:
x = F.relu(fc(x))
x = F.dropout(x, p=self.dropout, training=self.training)
x = self.mlp[-1](x)
return F.log_softmax(x, dim=1)
[docs] def loss(self, data):
return F.nll_loss(
self.forward(data.x, data.edge_index)[data.train_mask],
data.y[data.train_mask],
)
[docs] def predict(self, data):
return self.forward(data.x, data.edge_index)