xavier uniform

src/main.py

@@ -90,6 +90,7 @@ def main(opt):
     cls = AuthorshipAttributionClassifier(
         phi, num_authors=A.size, pad_index=pad_index, pad_length=opt.pad, device=device
     )
+    cls.xavier_uniform()
     print(cls)
 
     if opt.name == 'auto':

src/model/classifiers.py

@@ -24,6 +24,11 @@ class AuthorshipAttributionClassifier(nn.Module):
         self.pad_length = pad_length
         self.device = device
 
+    def xavier_uniform(self):
+        for p in self.parameters():
+            if p.dim() > 1 and p.requires_grad:
+                nn.init.xavier_uniform_(p)
+
     def fit(self, X, y, batch_size, epochs, patience=10, lr=0.001, val_prop=0.1, alpha=1., log='../log/tmp.csv', checkpointpath='../checkpoint/model.dat'):
         assert 0 <= alpha <= 1, 'wrong range, alpha must be in [0,1]'
         early_stop = EarlyStop(patience)