GeniePath model add a Tanh. (#3269)

* Update model.py

* Update README.md
Co-authored-by: Zihao Ye <expye@outlook.com>

examples/pytorch/geniepath/README.md

@@ -41,7 +41,14 @@ python ppi.py
 
 Performance
 -------------------------
-|Dataset | Pubmed (ACC)| PPI (micro-F1)|
-| ------ | ----------- | ------------- |
-| Paper  | 78.5%       | 0.952         |
-| DGL    | 73.0%       | 0.959         |
\ No newline at end of file
+Dataset: Pubmed (ACC)
+|Method | GeniePath|
+| ------ | ----------- |
+| Paper  | 78.5%       |
+| DGL    | 73.0%       |
+
+Dataset: PPI (micro-F1)
+|Method | GeniePath| GeniePath-lazy| GeniePath-lazy-residual|
+| ------ | ----------- | ------------- | ------------------ |
+| Paper  | 0.9520      | 0.9790        | 0.9850        |
+| DGL    | 0.9729      | 0.9802        | 0.9798        |

examples/pytorch/geniepath/model.py

@@ -12,6 +12,7 @@ class GeniePathConv(nn.Module):
 
     def forward(self, graph, x, h, c):
         x = self.breadth_func(graph, x)
+        x = th.tanh(x)
         x = th.mean(x, dim=1)
         x, (h, c) = self.depth_func(x.unsqueeze(0), (h, c))
         x = x[0]
@@ -59,7 +60,7 @@ class GeniePathLazy(nn.Module):
         x = self.linear1(x)
         h_tmps = []
         for layer in self.breaths:
-            h_tmps.append(th.mean(layer(graph, x), dim=1))
+            h_tmps.append(th.mean(th.tanh(layer(graph, x)), dim=1))
         x = x.unsqueeze(0)
         for h_tmp, layer in zip(h_tmps, self.depths):
             in_cat = th.cat((h_tmp.unsqueeze(0), x), -1)