Fix numbers (dmlc#2641)

Co-authored-by: Ubuntu <[email protected]>
rxy007 · Feb 8, 2021 · 48d357b · 48d357b
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diff --git a/examples/pytorch/rgcn/README.md b/examples/pytorch/rgcn/README.md
@@ -43,14 +43,14 @@ AIFB: accuracy avg(5 runs) 90.56%, best 94.44% (DGL)
 python3 entity_classify_mp.py -d aifb --testing --gpu 0 --fanout='20,20' --batch-size 128
 ```
 
-MUTAG: accuracy avg(5 runs) 70.00%, best 73.53% (DGL)
+MUTAG: accuracy avg(10 runs) 69.41%, best 76.47% (DGL)
 ```
-python3 entity_classify_mp.py -d mutag --l2norm 5e-4 --n-bases 30 --testing --gpu 0 --batch-size 64 --fanout '50,40' --use-self-loop --dgl-sparse --n-epochs 30 --sparse-lr 0.01 --dropout 0.7
+python3 entity_classify_mp.py -d mutag --l2norm 5e-4 --n-bases 30 --testing --gpu 0 --batch-size 64 --fanout "-1, -1" --use-self-loop --dgl-sparse --n-epochs 20 --sparse-lr 0.01 --dropout 0.5
 ```
 
-BGS: accuracy avg(5 runs) 84.83%, best 89.66% (DGL)
+BGS: accuracy avg(5 runs) 85.52%, best 93.10% (DGL)
 ```
-python3 entity_classify_mp.py -d bgs --l2norm 5e-4 --n-bases 40 --testing --gpu 0 --fanout '50,40' --n-epochs=20 --batch-size=32 --dgl-sparse  --lr 0.01 --sparse-lr 0.01 --dropout 0.3
+python3 entity_classify_mp.py -d bgs --l2norm 5e-4 --n-bases 40 --testing --gpu 0 --fanout "-1, -1"  --n-epochs=16 --batch-size=16 --dgl-sparse  --lr 0.01 --sparse-lr 0.05 --dropout 0.3
 ```
 
 AM: accuracy avg(5 runs) 88.59%, best 88.89% (DGL)

diff --git a/examples/pytorch/rgcn/entity_classify_mp.py b/examples/pytorch/rgcn/entity_classify_mp.py
@@ -432,7 +432,7 @@ def collect_eval():
     print("{}/{} Mean backward time: {:4f}".format(proc_id, n_gpus,
                                                    np.mean(backward_time[len(backward_time) // 4:])))
     if proc_id == 0:
-        print("Test Accuracy: {:.4f} | Test loss: {:.4f}".format(test_acc, test_loss))
+        print("Final Test Accuracy: {:.4f} | Test loss: {:.4f}".format(test_acc, test_loss))
         print("Train {}s, valid {}s, test {}s".format(train_time, validation_time, test_time))
 
 def main(args, devices):