Resolve issue mentioned in #242

[gpengzhi]

Resolve #242
Fix variable scope issue in dynamic decode function.

[ZhitingHu]

The beam_search_decode uses tf.contrib.seq2seq.dynamic_decode. Why the dynamic_decode inside texar would affect beam-search?

Please replicate seq2seq_attn, transformer, and text_style_transfer results first before merging

[gpengzhi]

The beam_search_decode uses tf.contrib.seq2seq.dynamic_decode. Why the dynamic_decode inside texar would affect beam-search?

Please replicate seq2seq_attn, transformer, and text_style_transfer results first before merging

We here make texar.tf.modules.decoders.dynamic_decode has the same variable scope as that in tensorflow.contrib.seq2seq.dynamic_decode so that everything learnt in the training procedure with texar.tf.modules.decoders.dynamic_decode can be reused in the inferencing procedure with tensorflow.contrib.seq2seq.dynamic_decode.

[gpengzhi]

python seq2seq_attn.py --config_model config_model --config_data config_toy_copy

step=0, loss=31.1855
step=50, loss=18.9741
step=100, loss=21.2726
step=150, loss=3.9846
step=200, loss=0.4235
step=250, loss=0.2036
step=300, loss=0.1700
val epoch=0, BLEU=91.9000; best-ever=91.9000
test epoch=0, BLEU=93.1400
==================================================
step=0, loss=1.2390
step=50, loss=7.9516
step=100, loss=0.6531
step=150, loss=1.1278
step=200, loss=0.4025
step=250, loss=0.2532
step=300, loss=0.2689
val epoch=1, BLEU=99.3900; best-ever=99.3900
test epoch=1, BLEU=99.5000
==================================================
step=0, loss=0.3637
step=50, loss=0.1413
step=100, loss=0.1402
step=150, loss=0.2444
step=200, loss=0.0238
step=250, loss=0.4120
step=300, loss=0.2562
val epoch=2, BLEU=98.8400; best-ever=99.3900
test epoch=2, BLEU=98.7600
==================================================
step=0, loss=0.2224
step=50, loss=0.3302
step=100, loss=0.0373
step=150, loss=0.0894
step=200, loss=0.0326
step=250, loss=1.4304
step=300, loss=0.4831
val epoch=3, BLEU=99.6100; best-ever=99.6100
test epoch=3, BLEU=99.2100

[gpengzhi]

python transformer_main.py --run_mode=train_and_evaluate --config_model=config_model --config_data=config_iwslt15

I1118 16:11:46.697821 140642838472448 transformer_main.py:269] step: 500, loss: 7.1598
step: 500, loss: 7.1598
I1118 16:37:26.853804 140642838472448 transformer_main.py:269] step: 1000, loss: 6.2243
step: 1000, loss: 6.2243
I1118 17:02:50.844758 140642838472448 transformer_main.py:269] step: 1500, loss: 5.5672
step: 1500, loss: 5.5672
I1118 17:28:42.947324 140642838472448 transformer_main.py:269] step: 2000, loss: 5.4850
step: 2000, loss: 5.4850
I1118 18:18:29.563245 140642838472448 transformer_main.py:213] epoch: 0, eval_bleu 4.4055
epoch: 0, eval_bleu 4.4055
I1118 18:18:29.563424 140642838472448 transformer_main.py:217] epoch: 0, best bleu: 4.4055
I1118 18:18:29.563504 140642838472448 transformer_main.py:221] saving model to ./outputs/best-model.ckpt
saving model to ./outputs/best-model.ckpt

[gpengzhi]

python main.py --config config

gamma: 1.0, lambda_g: 0.0
step: 1, loss_d: 0.6908 accu_d: 0.6250
step: 1, loss_g: 9.1450 loss_g_ae: 9.1450 loss_g_clas: 0.6957 accu_g: 0.4062 accu_g_gdy: 0.4531
step: 500, loss_d: 0.1236 accu_d: 0.9625
step: 500, loss_g: 4.4774 loss_g_ae: 4.4774 loss_g_clas: 0.1794 accu_g: 0.9406 accu_g_gdy: 0.8531
step: 1000, loss_d: 0.0985 accu_d: 0.9688
step: 1000, loss_g: 1.5501 loss_g_ae: 1.5501 loss_g_clas: 1.4881 accu_g: 0.5281 accu_g_gdy: 0.5172
step: 1500, loss_d: 0.0933 accu_d: 0.9672
step: 1500, loss_g: 0.9056 loss_g_ae: 0.9056 loss_g_clas: 2.9621 accu_g: 0.2875 accu_g_gdy: 0.3063
step: 2000, loss_d: 0.0964 accu_d: 0.9641
step: 2000, loss_g: 0.6762 loss_g_ae: 0.6762 loss_g_clas: 3.6231 accu_g: 0.2344 accu_g_gdy: 0.2141
step: 2500, loss_d: 0.0729 accu_d: 0.9766
step: 2500, loss_g: 0.5492 loss_g_ae: 0.5492 loss_g_clas: 4.2097 accu_g: 0.1797 accu_g_gdy: 0.1672
step: 3000, loss_d: 0.0684 accu_d: 0.9656
step: 3000, loss_g: 0.4170 loss_g_ae: 0.4170 loss_g_clas: 4.6914 accu_g: 0.1547 accu_g_gdy: 0.1375
step: 3500, loss_d: 0.0801 accu_d: 0.9781
step: 3500, loss_g: 0.4192 loss_g_ae: 0.4192 loss_g_clas: 4.9181 accu_g: 0.1625 accu_g_gdy: 0.1594
step: 4000, loss_d: 0.0625 accu_d: 0.9703
step: 4000, loss_g: 0.3330 loss_g_ae: 0.3330 loss_g_clas: 5.1321 accu_g: 0.1219 accu_g_gdy: 0.1047
step: 4500, loss_d: 0.0643 accu_d: 0.9734
step: 4500, loss_g: 0.2973 loss_g_ae: 0.2973 loss_g_clas: 5.0299 accu_g: 0.1156 accu_g_gdy: 0.1031
step: 5000, loss_d: 0.0873 accu_d: 0.9656
step: 5000, loss_g: 0.2669 loss_g_ae: 0.2669 loss_g_clas: 5.7881 accu_g: 0.1031 accu_g_gdy: 0.0844
step: 5500, loss_d: 0.0612 accu_d: 0.9766
step: 5500, loss_g: 0.2889 loss_g_ae: 0.2889 loss_g_clas: 5.2957 accu_g: 0.0922 accu_g_gdy: 0.1062
step: 6000, loss_d: 0.0820 accu_d: 0.9719
step: 6000, loss_g: 0.2769 loss_g_ae: 0.2769 loss_g_clas: 5.6069 accu_g: 0.1031 accu_g_gdy: 0.1156
step: 6500, loss_d: 0.0933 accu_d: 0.9672
step: 6500, loss_g: 0.2454 loss_g_ae: 0.2454 loss_g_clas: 6.1655 accu_g: 0.0750 accu_g_gdy: 0.0719
epoch: 1, loss_d: 0.0675 accu_d: 0.9859
epoch: 1, loss_g: 0.2187 loss_g_ae: 0.2187 loss_g_clas: 5.9780 accu_g: 0.0924 accu_g_gdy: 0.0924
val: loss_g: 0.1296 loss_g_ae: 0.1296 loss_g_clas: 6.4902 loss_d: 0.0660 accu_d: 0.9767 accu_g: 0.0523 accu_g_gdy: 0.0529 bleu: 90.5210

[ZhitingHu]

let's merge, and make a PR to update to v0.2.4