diff --git a/sequence_gan.py b/sequence_gan.py
index eca2ee50..b56ecb07 100644
--- a/sequence_gan.py
+++ b/sequence_gan.py
@@ -6,8 +6,9 @@
 from discriminator import Discriminator
 from rollout import ROLLOUT
 from target_lstm import TARGET_LSTM
-import cPickle
+import _pickle as pickle
 
+# encoding 
 #########################################################################################
 #  Generator  Hyper-parameters
 ######################################################################################
@@ -57,7 +58,7 @@ def target_loss(sess, target_lstm, data_loader):
     nll = []
     data_loader.reset_pointer()
 
-    for it in xrange(data_loader.num_batch):
+    for it in range(data_loader.num_batch):
         batch = data_loader.next_batch()
         g_loss = sess.run(target_lstm.pretrain_loss, {target_lstm.x: batch})
         nll.append(g_loss)
@@ -70,7 +71,7 @@ def pre_train_epoch(sess, trainable_model, data_loader):
     supervised_g_losses = []
     data_loader.reset_pointer()
 
-    for it in xrange(data_loader.num_batch):
+    for it in range(data_loader.num_batch):
         batch = data_loader.next_batch()
         _, g_loss = trainable_model.pretrain_step(sess, batch)
         supervised_g_losses.append(g_loss)
@@ -89,7 +90,7 @@ def main():
     dis_data_loader = Dis_dataloader(BATCH_SIZE)
 
     generator = Generator(vocab_size, BATCH_SIZE, EMB_DIM, HIDDEN_DIM, SEQ_LENGTH, START_TOKEN)
-    target_params = cPickle.load(open('save/target_params.pkl'))
+    target_params = pickle.load(open('save/target_params_py3.pkl', 'rb'))
     target_lstm = TARGET_LSTM(vocab_size, BATCH_SIZE, EMB_DIM, HIDDEN_DIM, SEQ_LENGTH, START_TOKEN, target_params) # The oracle model
 
     discriminator = Discriminator(sequence_length=20, num_classes=2, vocab_size=vocab_size, embedding_size=dis_embedding_dim, 
@@ -106,26 +107,26 @@ def main():
 
     log = open('save/experiment-log.txt', 'w')
     #  pre-train generator
-    print 'Start pre-training...'
+    print('Start pre-training...')
     log.write('pre-training...\n')
-    for epoch in xrange(PRE_EPOCH_NUM):
+    for epoch in range(PRE_EPOCH_NUM):
         loss = pre_train_epoch(sess, generator, gen_data_loader)
         if epoch % 5 == 0:
             generate_samples(sess, generator, BATCH_SIZE, generated_num, eval_file)
             likelihood_data_loader.create_batches(eval_file)
             test_loss = target_loss(sess, target_lstm, likelihood_data_loader)
-            print 'pre-train epoch ', epoch, 'test_loss ', test_loss
+            print('pre-train epoch ', epoch, 'test_loss ', test_loss)
             buffer = 'epoch:\t'+ str(epoch) + '\tnll:\t' + str(test_loss) + '\n'
             log.write(buffer)
 
-    print 'Start pre-training discriminator...'
+    print('Start pre-training discriminator...')
     # Train 3 epoch on the generated data and do this for 50 times
     for _ in range(50):
         generate_samples(sess, generator, BATCH_SIZE, generated_num, negative_file)
         dis_data_loader.load_train_data(positive_file, negative_file)
         for _ in range(3):
             dis_data_loader.reset_pointer()
-            for it in xrange(dis_data_loader.num_batch):
+            for it in range(dis_data_loader.num_batch):
                 x_batch, y_batch = dis_data_loader.next_batch()
                 feed = {
                     discriminator.input_x: x_batch,
@@ -136,8 +137,8 @@ def main():
 
     rollout = ROLLOUT(generator, 0.8)
 
-    print '#########################################################################'
-    print 'Start Adversarial Training...'
+    print('#########################################################################')
+    print('Start Adversarial Training...')
     log.write('adversarial training...\n')
     for total_batch in range(TOTAL_BATCH):
         # Train the generator for one step
@@ -153,7 +154,7 @@ def main():
             likelihood_data_loader.create_batches(eval_file)
             test_loss = target_loss(sess, target_lstm, likelihood_data_loader)
             buffer = 'epoch:\t' + str(total_batch) + '\tnll:\t' + str(test_loss) + '\n'
-            print 'total_batch: ', total_batch, 'test_loss: ', test_loss
+            print('total_batch: ', total_batch, 'test_loss: ', test_loss)
             log.write(buffer)
 
         # Update roll-out parameters
@@ -166,7 +167,7 @@ def main():
 
             for _ in range(3):
                 dis_data_loader.reset_pointer()
-                for it in xrange(dis_data_loader.num_batch):
+                for it in range(dis_data_loader.num_batch):
                     x_batch, y_batch = dis_data_loader.next_batch()
                     feed = {
                         discriminator.input_x: x_batch,