Merge pull request tkipf#15 from amar-iastate/master

Small Modifications to make it compatible with python 3.6.3

pygcn/models.py

@@ -15,4 +15,4 @@ def forward(self, x, adj):
         x = F.relu(self.gc1(x, adj))
         x = F.dropout(x, self.dropout, training=self.training)
         x = self.gc2(x, adj)
-        return F.log_softmax(x)
+        return F.log_softmax(x, dim=1)

pygcn/train.py

@@ -78,10 +78,10 @@ def train(epoch):
     loss_val = F.nll_loss(output[idx_val], labels[idx_val])
     acc_val = accuracy(output[idx_val], labels[idx_val])
     print('Epoch: {:04d}'.format(epoch+1),
-          'loss_train: {:.4f}'.format(loss_train.data[0]),
-          'acc_train: {:.4f}'.format(acc_train.data[0]),
-          'loss_val: {:.4f}'.format(loss_val.data[0]),
-          'acc_val: {:.4f}'.format(acc_val.data[0]),
+          'loss_train: {:.4f}'.format(loss_train.item()),
+          'acc_train: {:.4f}'.format(acc_train.item()),
+          'loss_val: {:.4f}'.format(loss_val.item()),
+          'acc_val: {:.4f}'.format(acc_val.item()),
           'time: {:.4f}s'.format(time.time() - t))
 
 

pygcn/utils.py

@@ -73,8 +73,8 @@ def accuracy(output, labels):
 def sparse_mx_to_torch_sparse_tensor(sparse_mx):
     """Convert a scipy sparse matrix to a torch sparse tensor."""
     sparse_mx = sparse_mx.tocoo().astype(np.float32)
-    indices = torch.from_numpy(np.vstack((sparse_mx.row,
-                                          sparse_mx.col))).long()
+    indices = torch.from_numpy(
+        np.vstack((sparse_mx.row, sparse_mx.col)).astype(np.int64))
     values = torch.from_numpy(sparse_mx.data)
     shape = torch.Size(sparse_mx.shape)
     return torch.sparse.FloatTensor(indices, values, shape)