Use var

fluid/neural_machine_translation/transformer/train.py

@@ -202,7 +202,7 @@ def main():
         tensor = position_encoding_init(ModelHyperParams.max_length + 1, ModelHyperParams.d_model)
         for place_id in xrange(dev_count):
             local_scope = train_exe.executor.local_scope(place_id)
-            local_scope.find_var(pos_enc_param_name).get_tensor().set(tensor, fluid.CUDAPlace(place_id))
+            local_scope.var(pos_enc_param_name).get_tensor().set(tensor, fluid.CUDAPlace(place_id))
 
     train_data = read_multiple(reader=train_data, count=dev_count)
     for pass_id in xrange(TrainTaskConfig.pass_num):
@@ -221,12 +221,12 @@ def main():
                     fluid.CUDAPlace(place_id))
 
                 for var_name in data_input_dict:
-                    local_scope.find_var(var_name).get_tensor().set(data_input_dict[var_name],
+                    local_scope.var(var_name).get_tensor().set(data_input_dict[var_name],
                                                                     fluid.CUDAPlace(place_id))
 
                 for var_name in util_input_dict:
                     print var_name, local_scope.find_var(var_name)
-                    local_scope.find_var(var_name).get_tensor().set(util_input_dict[var_name],
+                    local_scope.var(var_name).get_tensor().set(util_input_dict[var_name],
                                                                     fluid.CUDAPlace(place_id))
 
             outs = train_exe.run(fetch_list=[sum_cost.name, token_num.name])