diff --git a/graphsage/model.py b/graphsage/model.py
index aeca282..0c3446f 100644
--- a/graphsage/model.py
+++ b/graphsage/model.py
@@ -46,7 +46,7 @@ def load_cora():
     with open("cora/cora.content") as fp:
         for i,line in enumerate(fp):
             info = line.strip().split()
-            feat_data[i,:] = map(float, info[1:-1])
+            feat_data[i,:] = list(map(float, info[1:-1]))
             node_map[info[0]] = i
             if not info[-1] in label_map:
                 label_map[info[-1]] = len(label_map)
@@ -99,11 +99,11 @@ def run_cora():
         optimizer.step()
         end_time = time.time()
         times.append(end_time-start_time)
-        print batch, loss.data[0]
+        print(batch, loss.data.item())
 
     val_output = graphsage.forward(val) 
-    print "Validation F1:", f1_score(labels[val], val_output.data.numpy().argmax(axis=1), average="micro")
-    print "Average batch time:", np.mean(times)
+    print("Validation F1:", f1_score(labels[val], val_output.data.numpy().argmax(axis=1), average="micro"))
+    print("Average batch time:", np.mean(times))
 
 def load_pubmed():
     #hardcoded for simplicity...
@@ -171,11 +171,11 @@ def run_pubmed():
         optimizer.step()
         end_time = time.time()
         times.append(end_time-start_time)
-        print batch, loss.data[0]
+        print(batch, loss.data.item())
 
     val_output = graphsage.forward(val) 
-    print "Validation F1:", f1_score(labels[val], val_output.data.numpy().argmax(axis=1), average="micro")
-    print "Average batch time:", np.mean(times)
+    print("Validation F1:", f1_score(labels[val], val_output.data.numpy().argmax(axis=1), average="micro"))
+    print("Average batch time:", np.mean(times))
 
 if __name__ == "__main__":
     run_cora()