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import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch_geometric.data import Data
from torch_geometric.nn import GATConv
from torch_geometric.datasets import Planetoid
import torch_geometric.transforms as T
import warnings
warnings.filterwarnings("ignore")
# Seed for reproducible numbers
torch.manual_seed(2020)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# Dataset used Cora
name_data = 'Cora'
dataset = Planetoid(root= '/tmp/' + name_data, name = name_data)
dataset.transform = T.NormalizeFeatures()
print(f"Number of Classes in {name_data}:", dataset.num_classes)
print(f"Number of Node Features in {name_data}:", dataset.num_node_features)
# Model Definition
class GAT(torch.nn.Module):
def __init__(self):
super(GAT, self).__init__()
self.hid = 8
self.in_head = 8
self.out_head = 1
self.conv1 = GATConv(dataset.num_features, self.hid, heads=self.in_head, dropout=0.6)
self.conv2 = GATConv(self.hid*self.in_head, dataset.num_classes, concat=False, heads=self.out_head, dropout=0.6)
def forward(self, data):
x, edge_index = data.x, data.edge_index
x = F.dropout(x, p=0.6, training=self.training)
x = self.conv1(x, edge_index)
x = F.elu(x)
x = F.dropout(x, p=0.6, training=self.training)
x = self.conv2(x, edge_index)
return F.log_softmax(x, dim=1)
# Train
model = GAT().to(device)
data = dataset[0].to(device)
# Adam Optimizer
optimizer = torch.optim.Adam(model.parameters(), lr=0.005, weight_decay=5e-4)
# Training Loop
model.train()
for epoch in range(1000):
model.train()
optimizer.zero_grad()
out = model(data)
loss = F.nll_loss(out[data.train_mask], data.y[data.train_mask])
if epoch%200 == 0:
print(loss)
loss.backward()
optimizer.step()
# Evaluation
model.eval()
_, pred = model(data).max(dim=1)
correct = float (pred[data.test_mask].eq(data.y[data.test_mask]).sum().item())
acc = correct / data.test_mask.sum().item()
print('Accuracy: {:.4f}'.format(acc)) |