diff --git a/src/model/seq2seq.py b/src/model/seq2seq.py
index bbff9c05..80cb0141 100644
--- a/src/model/seq2seq.py
+++ b/src/model/seq2seq.py
@@ -70,7 +70,8 @@
 from tensorflow.python.ops import nn_ops
 from tensorflow.contrib.rnn.python.ops import rnn, rnn_cell
 from tensorflow.python.ops import variable_scope
-linear = rnn_cell._linear # pylint: disable=protected-access
+from tensorflow.contrib.rnn.python.ops import core_rnn_cell
+linear = core_rnn_cell._linear # pylint: disable=protected-access
 
 def _extract_argmax_and_embed(embedding, output_projection=None,
                               update_embedding=True):
diff --git a/src/model/seq2seq_model.py b/src/model/seq2seq_model.py
index 98b2bea0..cf9ebc06 100644
--- a/src/model/seq2seq_model.py
+++ b/src/model/seq2seq_model.py
@@ -84,22 +84,25 @@ def __init__(self, encoder_masks, encoder_inputs_tensor,
         self.encoder_masks = encoder_masks
 
         # Create the internal multi-layer cell for our RNN.
-        single_cell = tf.contrib.rnn.core_rnn_cell.BasicLSTMCell(attn_num_hidden, forget_bias=0.0, state_is_tuple=False)
+
         if use_gru:
             print("using GRU CELL in decoder")
-            single_cell = tf.contrib.rnn.core_rnn_cell.GRUCell(attn_num_hidden)
-        cell = single_cell
+            single_cell = tf.contrib.rnn.GRUCell(attn_num_hidden)
+        else:
+            single_cell = tf.contrib.rnn.BasicLSTMCell(attn_num_hidden, forget_bias=0.0, state_is_tuple=False)
 
         if attn_num_layers > 1:
-            cell = tf.contrib.rnn.core_rnn_cell.MultiRNNCell([single_cell] * attn_num_layers, state_is_tuple=False)
+            cell = tf.contrib.rnn.MultiRNNCell([single_cell] * attn_num_layers, state_is_tuple=False)
+        else:
+            cell = single_cell
 
         # The seq2seq function: we use embedding for the input and attention.
         def seq2seq_f(lstm_inputs, decoder_inputs, seq_length, do_decode):
 
             num_hidden = attn_num_layers * attn_num_hidden
-            lstm_fw_cell = tf.contrib.rnn.core_rnn_cell.BasicLSTMCell(num_hidden, forget_bias=0.0, state_is_tuple=False)
+            lstm_fw_cell = tf.contrib.rnn.BasicLSTMCell(num_hidden, forget_bias=0.0, state_is_tuple=False)
             # Backward direction cell
-            lstm_bw_cell = tf.contrib.rnn.core_rnn_cell.BasicLSTMCell(num_hidden, forget_bias=0.0, state_is_tuple=False)
+            lstm_bw_cell = tf.contrib.rnn.BasicLSTMCell(num_hidden, forget_bias=0.0, state_is_tuple=False)
 
             pre_encoder_inputs, output_state_fw, output_state_bw = tf.contrib.rnn.static_bidirectional_rnn(lstm_fw_cell, lstm_bw_cell, lstm_inputs,
                 initial_state_fw=None, initial_state_bw=None,