diff --git a/.ipynb_checkpoints/OVERVIEW-checkpoint.md b/.ipynb_checkpoints/OVERVIEW-checkpoint.md
index c5146b6..c0db861 100644
--- a/.ipynb_checkpoints/OVERVIEW-checkpoint.md
+++ b/.ipynb_checkpoints/OVERVIEW-checkpoint.md
@@ -1,20 +1,6 @@
 
-## Intro
+## 介绍
 
-Provide a short overview of your algorithm that explains the value and primary use cases.
-
-## Usage
-
-```
-def function_name(conf={})
-```
-
-## Example
-
-Provide and explain examples of input and output for your algorithm.
-
-`Code`
-
-```
-please input your example code
-```
+利用 LSTM 神经网络，输入提供的词语，输出对应的藏头诗或藏字诗。
+例如，在输入框中输入 “小莫最棒” ，选择需要输出的诗词类型，点击提交，生成的古诗词为：“小田高枕声清晓，莫学石榴垆里中。最有雁中琴乍坛，棒中无事不相逢。”
+每次提交生成的结果都不一样，你可以从中选择一个最好的，赶快来体验吧～
\ No newline at end of file
diff --git a/.ipynb_checkpoints/Untitled-checkpoint.ipynb b/.ipynb_checkpoints/Untitled-checkpoint.ipynb
deleted file mode 100644
index 2fd6442..0000000
--- a/.ipynb_checkpoints/Untitled-checkpoint.ipynb
+++ /dev/null
@@ -1,6 +0,0 @@
-{
- "cells": [],
- "metadata": {},
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/.ipynb_checkpoints/handler-checkpoint.py b/.ipynb_checkpoints/handler-checkpoint.py
new file mode 100644
index 0000000..5249003
--- /dev/null
+++ b/.ipynb_checkpoints/handler-checkpoint.py
@@ -0,0 +1,40 @@
+# coding: utf-8
+import os
+import sys
+
+# Import necessary packages
+from modules import json_parser
+from modules import Client
+from write_poem import start_model
+
+# Initialise Client object
+client = Client(api_key='33799e1c5d6fa05fdd7dec3aa7aad868445d1c737edcf9c37fa89cb3b39cb2d9',
+                project_id='5bfd118f1afd942b66b36b30', user_ID='yangsaisai',
+                project_type='app', source_file_path='main.ipynb', 
+                silent=True)
+
+# Make run/train/predict command alias for further use
+run = client.run
+train = client.train
+predict = client.predict
+
+# Make controller alias for further use
+controller = client.controller
+writer = start_model()
+
+
+def handle(conf):
+    # paste your code here
+    if conf['style'] == '藏头诗':
+        poetry = writer.cangtou(conf['Chinese_word'])
+    elif conf['style'] == '藏字诗':
+        poetry = writer.hide_words(conf['Chinese_word'])
+    else:
+        poetry = writer.rhyme_verse()
+    return {"Poetry": poetry}
+    # return {"Poetry": 'asadd'}
+
+# if __name__ == '__main__':
+#     conf = {"style": ['藏头诗'], "Chinese_word": '杨赛赛'}
+#     aa = handle(conf)
+#     print(aa)
diff --git a/.ipynb_checkpoints/main-checkpoint.py b/.ipynb_checkpoints/main-checkpoint.py
deleted file mode 100644
index 5249003..0000000
--- a/.ipynb_checkpoints/main-checkpoint.py
+++ /dev/null
@@ -1,40 +0,0 @@
-# coding: utf-8
-import os
-import sys
-
-# Import necessary packages
-from modules import json_parser
-from modules import Client
-from write_poem import start_model
-
-# Initialise Client object
-client = Client(api_key='33799e1c5d6fa05fdd7dec3aa7aad868445d1c737edcf9c37fa89cb3b39cb2d9',
-                project_id='5bfd118f1afd942b66b36b30', user_ID='yangsaisai',
-                project_type='app', source_file_path='main.ipynb', 
-                silent=True)
-
-# Make run/train/predict command alias for further use
-run = client.run
-train = client.train
-predict = client.predict
-
-# Make controller alias for further use
-controller = client.controller
-writer = start_model()
-
-
-def handle(conf):
-    # paste your code here
-    if conf['style'] == '藏头诗':
-        poetry = writer.cangtou(conf['Chinese_word'])
-    elif conf['style'] == '藏字诗':
-        poetry = writer.hide_words(conf['Chinese_word'])
-    else:
-        poetry = writer.rhyme_verse()
-    return {"Poetry": poetry}
-    # return {"Poetry": 'asadd'}
-
-# if __name__ == '__main__':
-#     conf = {"style": ['藏头诗'], "Chinese_word": '杨赛赛'}
-#     aa = handle(conf)
-#     print(aa)
diff --git a/.ipynb_checkpoints/train-checkpoint.py b/.ipynb_checkpoints/train-checkpoint.py
index adfeb79..e69de29 100644
--- a/.ipynb_checkpoints/train-checkpoint.py
+++ b/.ipynb_checkpoints/train-checkpoint.py
@@ -1,197 +0,0 @@
-import codecs
-import json
-import logging
-import os
-import shutil
-import sys
-import time
-import numpy as np
-import tensorflow as tf
-from char_rnn_model import CharRNNLM
-from config_poem import config_poem_train
-from data_loader import DataLoader
-from word2vec_helper import Word2Vec
-
-sys.path.insert(0, os.path.dirname(__file__))
-TF_VERSION = int(tf.__version__.split('.')[1])
-
-
-def main(args=''):
-    args = config_poem_train(args)
-    # Specifying location to store model, best model and tensorboard log.
-    args.save_model = os.path.join(args.output_dir, 'save_model/model')
-    args.save_best_model = os.path.join(args.output_dir, 'best_model/model')
-    # args.tb_log_dir = os.path.join(args.output_dir, 'tensorboard_log/')
-    timestamp = str(int(time.time()))
-    # args.tb_log_dir = os.path.abspath(os.path.join(args.output_dir, "tensorboard_log", timestamp))
-    args.tb_log_dir = os.path.abspath(os.path.join('./results/tb_results', "tensorboard_log", timestamp))
-    print("Writing to {}\n".format(args.tb_log_dir))
-
-    # Create necessary directories.
-    if len(args.init_dir) != 0:
-        args.output_dir = args.init_dir
-
-    # else:
-        # if os.path.exists(args.output_dir):
-        #     shutil.rmtree(args.output_dir)
-        # for paths in [args.save_model, args.save_best_model, args.tb_log_dir]:
-        #     os.makedirs(os.path.dirname(paths))
-
-    logging.basicConfig(stream=sys.stdout,
-                        format='%(asctime)s %(levelname)s:%(message)s',
-                        level=logging.INFO, datefmt='%I:%M:%S')
-
-    print('=' * 60)
-    print('All final and intermediate outputs will be stored in %s/' % args.output_dir)
-    print('=' * 60 + '\n')
-
-    logging.info('args are:\n%s', args)
-
-    if len(args.init_dir) != 0:
-        with open(os.path.join(args.init_dir, 'result.json'), 'r') as f:
-            result = json.load(f)
-        params = result['params']
-        args.init_model = result['latest_model']
-        best_model = result['best_model']
-        best_valid_ppl = result['best_valid_ppl']
-        if 'encoding' in result:
-            args.encoding = result['encoding']
-        else:
-            args.encoding = 'utf-8'
-
-    else:
-        params = {'batch_size': args.batch_size,
-                  'num_unrollings': args.num_unrollings,
-                  'hidden_size': args.hidden_size,
-                  'max_grad_norm': args.max_grad_norm,
-                  'embedding_size': args.embedding_size,
-                  'num_layers': args.num_layers,
-                  'learning_rate': args.learning_rate,
-                  'cell_type': args.cell_type,
-                  'dropout': args.dropout,
-                  'input_dropout': args.input_dropout}
-        best_model = ''
-    logging.info('Parameters are:\n%s\n', json.dumps(params, sort_keys=True, indent=4))
-
-    # Create batch generators.
-    batch_size = params['batch_size']
-    num_unrollings = params['num_unrollings']
-
-    base_path = args.data_path
-    w2v_file = os.path.join(base_path, "vectors_poem.bin")
-    w2v = Word2Vec(w2v_file)
-
-    train_data_loader = DataLoader(base_path, batch_size, num_unrollings, w2v.model, 'train')
-    test1_data_loader = DataLoader(base_path, batch_size, num_unrollings, w2v.model, 'test')
-    valid_data_loader = DataLoader(base_path, batch_size, num_unrollings, w2v.model, 'valid')
-
-    # Create graphs
-    logging.info('Creating graph')
-    graph = tf.Graph()
-    with graph.as_default():
-        w2v_vocab_size = len(w2v.model.vocab)
-        with tf.name_scope('training'):
-            train_model = CharRNNLM(is_training=True, w2v_model = w2v.model, vocab_size=w2v_vocab_size, infer=False, **params)
-            tf.get_variable_scope().reuse_variables()
-
-        with tf.name_scope('validation'):
-            valid_model = CharRNNLM(is_training=False, w2v_model = w2v.model, vocab_size=w2v_vocab_size, infer=False, **params)
-
-        with tf.name_scope('evaluation'):
-            test_model = CharRNNLM(is_training=False, w2v_model = w2v.model,vocab_size=w2v_vocab_size,  infer=False, **params)
-            saver = tf.train.Saver(name='model_saver')
-            best_model_saver = tf.train.Saver(name='best_model_saver')
-
-    logging.info('Start training\n')
-
-    result = {}
-    result['params'] = params
-
-    try:
-        with tf.Session(graph=graph) as session:
-            # Version 8 changed the api of summary writer to use
-            # graph instead of graph_def.
-            if TF_VERSION >= 8:
-                graph_info = session.graph
-            else:
-                graph_info = session.graph_def
-
-            train_summary_dir = os.path.join(args.tb_log_dir, "summaries", "train")
-            train_writer = tf.summary.FileWriter(train_summary_dir, graph_info)
-            valid_summary_dir = os.path.join(args.tb_log_dir, "summaries", "valid")
-            valid_writer = tf.summary.FileWriter(valid_summary_dir, graph_info)
-
-            # load a saved model or start from random initialization.
-            if len(args.init_model) != 0:
-                saver.restore(session, args.init_model)
-            else:
-                tf.global_variables_initializer().run()
-
-            learning_rate = args.learning_rate
-            for epoch in range(args.num_epochs):
-                logging.info('=' * 19 + ' Epoch %d ' + '=' * 19 + '\n', epoch)
-                logging.info('Training on training set')
-                # training step
-                ppl, train_summary_str, global_step = train_model.run_epoch(session, train_data_loader, is_training=True,
-                                     learning_rate=learning_rate, verbose=args.verbose, freq=args.progress_freq)
-                # record the summary
-                train_writer.add_summary(train_summary_str, global_step)
-                train_writer.flush()
-                # save model
-                saved_path = saver.save(session, args.save_model,
-                                        global_step=train_model.global_step)
-
-                logging.info('Latest model saved in %s\n', saved_path)
-                logging.info('Evaluate on validation set')
-
-                valid_ppl, valid_summary_str, _ = valid_model.run_epoch(session, valid_data_loader, is_training=False,
-                                     learning_rate=learning_rate, verbose=args.verbose, freq=args.progress_freq)
-
-                # save and update best model
-                if (len(best_model) == 0) or (valid_ppl < best_valid_ppl):
-                    best_model = best_model_saver.save(session, args.save_best_model,
-                                                       global_step=train_model.global_step)
-                    best_valid_ppl = valid_ppl
-                else:
-                    learning_rate /= 2.0
-                    logging.info('Decay the learning rate: ' + str(learning_rate))
-
-                valid_writer.add_summary(valid_summary_str, global_step)
-                valid_writer.flush()
-
-                logging.info('Best model is saved in %s', best_model)
-                logging.info('Best validation ppl is %f\n', best_valid_ppl)
-
-                result['latest_model'] = saved_path
-                result['best_model'] = best_model
-                # Convert to float because numpy.float is not json serializable.
-                result['best_valid_ppl'] = float(best_valid_ppl)
-
-                result_path = os.path.join(args.output_dir, 'result.json')
-                if os.path.exists(result_path):
-                    os.remove(result_path)
-                with open(result_path, 'w') as f:
-                    json.dump(result, f, indent=2, sort_keys=True)
-
-            logging.info('Latest model is saved in %s', saved_path)
-            logging.info('Best model is saved in %s', best_model)
-            logging.info('Best validation ppl is %f\n', best_valid_ppl)
-
-            logging.info('Evaluate the best model on test set')
-            saver.restore(session, best_model)
-            test_ppl, _, _ = test_model.run_epoch(session, test1_data_loader, is_training=False,
-                                     learning_rate=learning_rate, verbose=args.verbose, freq=args.progress_freq)
-            result['test_ppl'] = float(test_ppl)
-    except Exception as e:
-        print('err :{}'.format(e))
-    finally:
-        result_path = os.path.join(args.output_dir, 'result.json')
-        if os.path.exists(result_path):
-            os.remove(result_path)
-        with open(result_path, 'w', encoding='utf-8', errors='ignore') as f:
-            json.dump(result, f, indent=2, sort_keys=True)
-
-
-if __name__ == '__main__':
-    args = '--output_dir ./results/output_poem --data_path ./datasets/yangsaisai-poetrydatasets-0_0_1/  --hidden_size 128 --embedding_size 128 --cell_type lstm'
-    main(args)
diff --git a/.ipynb_checkpoints/write_poem-checkpoint.py b/.ipynb_checkpoints/write_poem-checkpoint.py
index 5848dac..06c45cb 100644
--- a/.ipynb_checkpoints/write_poem-checkpoint.py
+++ b/.ipynb_checkpoints/write_poem-checkpoint.py
@@ -25,7 +25,7 @@
 
         params = result['params']
         best_model = result['best_model']
-        best_valid_ppl = result['best_valid_ppl']
+        # best_valid_ppl = result['best_valid_ppl']
         if 'encoding' in result:
             self.args.encoding = result['encoding']
         else:
@@ -216,5 +216,6 @@
     writer = WritePoem(args)
     return writer
 
+
 if __name__ == '__main__':
     writer = start_model()
diff --git a/OVERVIEW.md b/OVERVIEW.md
index c5146b6..c0db861 100644
--- a/OVERVIEW.md
+++ b/OVERVIEW.md
@@ -1,20 +1,6 @@
 
-## Intro
+## 介绍
 
-Provide a short overview of your algorithm that explains the value and primary use cases.
-
-## Usage
-
-```
-def function_name(conf={})
-```
-
-## Example
-
-Provide and explain examples of input and output for your algorithm.
-
-`Code`
-
-```
-please input your example code
-```
+利用 LSTM 神经网络，输入提供的词语，输出对应的藏头诗或藏字诗。
+例如，在输入框中输入 “小莫最棒” ，选择需要输出的诗词类型，点击提交，生成的古诗词为：“小田高枕声清晓，莫学石榴垆里中。最有雁中琴乍坛，棒中无事不相逢。”
+每次提交生成的结果都不一样，你可以从中选择一个最好的，赶快来体验吧～
\ No newline at end of file
diff --git a/Untitled.ipynb b/Untitled.ipynb
deleted file mode 100644
index bb1eab5..0000000
--- a/Untitled.ipynb
+++ /dev/null
@@ -1,85 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# You can use other public modules via our Client object with module's identifier\n",
-    "# and parameters.\n",
-    "# For more detailes, please see our online document - https://momodel.github.io/docs/#\n",
-    "\n",
-    "import os\n",
-    "import sys\n",
-    "\n",
-    "# Define root path\n",
-    "sys.path.append('../')\n",
-    "\n",
-    "# Import necessary packages\n",
-    "from modules import json_parser\n",
-    "from modules import Client\n",
-    "\n",
-    "# Initialise Client object\n",
-    "client = Client(api_key='33799e1c5d6fa05fdd7dec3aa7aad868445d1c737edcf9c37fa89cb3b39cb2d9',\n",
-    "                project_id='5bfd118f1afd942b66b36b30', user_ID='yangsaisai',\n",
-    "                project_type='app', source_file_path='Untitled.ipynb')\n",
-    "\n",
-    "# Make run/train/predict command alias for further use\n",
-    "run = client.run\n",
-    "train = client.train\n",
-    "predict = client.predict\n",
-    "\n",
-    "# Make controller alias for further use\n",
-    "controller = client.controller\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "OVERVIEW.md   poems_edge_split.txt  rhyme_words.txt\n",
-      "poem_ids.txt  \u001b[0m\u001b[01;34mresults\u001b[0m/              vectors_poem.bin\n"
-     ]
-    }
-   ],
-   "source": [
-    "ls /home/jovyan/work/datasets/yangsaisai-poetrydatasets-0_0_1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.5.2"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/handler.py b/handler.py
new file mode 100644
index 0000000..5249003
--- /dev/null
+++ b/handler.py
@@ -0,0 +1,40 @@
+# coding: utf-8
+import os
+import sys
+
+# Import necessary packages
+from modules import json_parser
+from modules import Client
+from write_poem import start_model
+
+# Initialise Client object
+client = Client(api_key='33799e1c5d6fa05fdd7dec3aa7aad868445d1c737edcf9c37fa89cb3b39cb2d9',
+                project_id='5bfd118f1afd942b66b36b30', user_ID='yangsaisai',
+                project_type='app', source_file_path='main.ipynb', 
+                silent=True)
+
+# Make run/train/predict command alias for further use
+run = client.run
+train = client.train
+predict = client.predict
+
+# Make controller alias for further use
+controller = client.controller
+writer = start_model()
+
+
+def handle(conf):
+    # paste your code here
+    if conf['style'] == '藏头诗':
+        poetry = writer.cangtou(conf['Chinese_word'])
+    elif conf['style'] == '藏字诗':
+        poetry = writer.hide_words(conf['Chinese_word'])
+    else:
+        poetry = writer.rhyme_verse()
+    return {"Poetry": poetry}
+    # return {"Poetry": 'asadd'}
+
+# if __name__ == '__main__':
+#     conf = {"style": ['藏头诗'], "Chinese_word": '杨赛赛'}
+#     aa = handle(conf)
+#     print(aa)
diff --git a/job_logs/job-gpu-5bfd16971afd943016b451b7.log b/job_logs/job-gpu-5bfd16971afd943016b451b7.log
new file mode 100644
index 0000000..a9ca909
--- /dev/null
+++ b/job_logs/job-gpu-5bfd16971afd943016b451b7.log
@@ -0,0 +1,12 @@
+2018-11-27T10:04:22.659993645Z SYSTEM: Preparing env...
+2018-11-27T10:04:22.976307578Z SYSTEM: Running...
+2018-11-27T10:04:25.913548121Z Traceback (most recent call last):
+2018-11-27T10:04:25.913592229Z   File "train.py", line 12, in <module>
+2018-11-27T10:04:25.913626672Z     from data_loader import DataLoader
+2018-11-27T10:04:25.913659904Z   File "/home/jovyan/work/data_loader.py", line 5, in <module>
+2018-11-27T10:04:25.913871435Z     from word2vec_helper import Word2Vec
+2018-11-27T10:04:25.913883837Z   File "/home/jovyan/work/word2vec_helper.py", line 2, in <module>
+2018-11-27T10:04:25.921915312Z     import word2vec
+2018-11-27T10:04:25.921941434Z ImportError: No module named 'word2vec'
+2018-11-27T10:04:26.261989876Z SYSTEM: Finishing...
+2018-11-27T10:04:26.469404368Z SYSTEM: Error Exists!
diff --git a/job_logs/job-gpu-5bfd16f91afd942b66b36b34.log b/job_logs/job-gpu-5bfd16f91afd942b66b36b34.log
new file mode 100644
index 0000000..1d2533d
--- /dev/null
+++ b/job_logs/job-gpu-5bfd16f91afd942b66b36b34.log
@@ -0,0 +1,38 @@
+2018-11-27T10:07:01.321228212Z SYSTEM: Preparing env...
+2018-11-27T10:07:01.578526766Z SYSTEM: Running...
+2018-11-27T10:07:04.466118802Z Writing to /home/jovyan/work/results/output_poem/tensorboard_log/1543313224
+2018-11-27T10:07:04.466172666Z 
+2018-11-27T10:07:04.588355555Z ============================================================
+2018-11-27T10:07:04.588401989Z All final and intermediate outputs will be stored in /home/jovyan/work/results/output_poem/
+2018-11-27T10:07:04.588410821Z ============================================================
+2018-11-27T10:07:04.588415325Z 
+2018-11-27T10:07:04.588419611Z 06:07:04 INFO:args are:
+2018-11-27T10:07:04.588433779Z Namespace(batch_size=16, best_model='', best_valid_ppl=inf, cell_type='lstm', data_path='/home/jovyan/work/src/data/poem/', debug=False, dropout=0.0, embedding_size=128, encoding='utf-8', hidden_size=128, init_dir='', init_model='', input_dropout=0.0, learning_rate=0.005, max_grad_norm=5.0, num_epochs=5, num_layers=2, num_unrollings=64, output_dir='/home/jovyan/work/results/output_poem', progress_freq=100, save_best_model='/home/jovyan/work/results/output_poem/best_model/model', save_model='/home/jovyan/work/results/output_poem/save_model/model', tb_log_dir='/home/jovyan/work/results/output_poem/tensorboard_log/1543313224', test=False, train_frac=0.9, valid_frac=0.05, verbose=0)
+2018-11-27T10:07:04.588534657Z 06:07:04 INFO:Parameters are:
+2018-11-27T10:07:04.58854925Z {
+2018-11-27T10:07:04.588554542Z     "batch_size": 16,
+2018-11-27T10:07:04.588559Z     "cell_type": "lstm",
+2018-11-27T10:07:04.588563781Z     "dropout": 0.0,
+2018-11-27T10:07:04.588568162Z     "embedding_size": 128,
+2018-11-27T10:07:04.588572675Z     "hidden_size": 128,
+2018-11-27T10:07:04.588577034Z     "input_dropout": 0.0,
+2018-11-27T10:07:04.588581659Z     "learning_rate": 0.005,
+2018-11-27T10:07:04.588585927Z     "max_grad_norm": 5.0,
+2018-11-27T10:07:04.588590309Z     "num_layers": 2,
+2018-11-27T10:07:04.58859518Z     "num_unrollings": 64
+2018-11-27T10:07:04.588599829Z }
+2018-11-27T10:07:04.588604427Z 
+2018-11-27T10:07:04.588939576Z Traceback (most recent call last):
+2018-11-27T10:07:04.588951533Z   File "train.py", line 196, in <module>
+2018-11-27T10:07:04.589352881Z     main(args)
+2018-11-27T10:07:04.589365786Z   File "train.py", line 80, in main
+2018-11-27T10:07:04.589569032Z     w2v = Word2Vec(w2v_file)
+2018-11-27T10:07:04.589579072Z   File "/home/jovyan/work/word2vec_helper.py", line 7, in __init__
+2018-11-27T10:07:04.589921048Z     self.model = word2vec.load(file_path)
+2018-11-27T10:07:04.589969692Z   File "/home/jovyan/work/.localenv/lib/python3.5/site-packages/word2vec/io.py", line 16, in load
+2018-11-27T10:07:04.590576088Z     return word2vec.WordVectors.from_binary(fname, *args, **kwargs)
+2018-11-27T10:07:04.590602769Z   File "/home/jovyan/work/.localenv/lib/python3.5/site-packages/word2vec/wordvectors.py", line 200, in from_binary
+2018-11-27T10:07:04.592977346Z     with open(fname, "rb") as fin:
+2018-11-27T10:07:04.5929906Z FileNotFoundError: [Errno 2] No such file or directory: '/home/jovyan/work/src/data/poem/vectors_poem.bin'
+2018-11-27T10:07:04.896550412Z SYSTEM: Finishing...
+2018-11-27T10:07:05.127609348Z SYSTEM: Error Exists!
diff --git a/job_logs/job-gpu-5bfd17d51afd943016b451b9.log b/job_logs/job-gpu-5bfd17d51afd943016b451b9.log
new file mode 100644
index 0000000..e0b1285
--- /dev/null
+++ b/job_logs/job-gpu-5bfd17d51afd943016b451b9.log
@@ -0,0 +1,327 @@
+2018-11-27T10:09:39.680843461Z SYSTEM: Preparing env...
+2018-11-27T10:09:39.97799912Z SYSTEM: Running...
+2018-11-27T10:09:42.917073854Z Writing to /home/jovyan/work/results/output_poem/tensorboard_log/1543313382
+2018-11-27T10:09:42.917115651Z 
+2018-11-27T10:09:42.921576408Z ============================================================
+2018-11-27T10:09:42.921590985Z All final and intermediate outputs will be stored in /home/jovyan/work/results/output_poem/
+2018-11-27T10:09:42.92160796Z ============================================================
+2018-11-27T10:09:42.921612797Z 
+2018-11-27T10:09:42.921615725Z 06:09:42 INFO:args are:
+2018-11-27T10:09:42.921620075Z Namespace(batch_size=16, best_model='', best_valid_ppl=inf, cell_type='lstm', data_path='/home/jovyan/work/data/poem/', debug=False, dropout=0.0, embedding_size=128, encoding='utf-8', hidden_size=128, init_dir='', init_model='', input_dropout=0.0, learning_rate=0.005, max_grad_norm=5.0, num_epochs=5, num_layers=2, num_unrollings=64, output_dir='/home/jovyan/work/results/output_poem', progress_freq=100, save_best_model='/home/jovyan/work/results/output_poem/best_model/model', save_model='/home/jovyan/work/results/output_poem/save_model/model', tb_log_dir='/home/jovyan/work/results/output_poem/tensorboard_log/1543313382', test=False, train_frac=0.9, valid_frac=0.05, verbose=0)
+2018-11-27T10:09:42.921625405Z 06:09:42 INFO:Parameters are:
+2018-11-27T10:09:42.92162832Z {
+2018-11-27T10:09:42.921631027Z     "batch_size": 16,
+2018-11-27T10:09:42.921634151Z     "cell_type": "lstm",
+2018-11-27T10:09:42.92163743Z     "dropout": 0.0,
+2018-11-27T10:09:42.921640321Z     "embedding_size": 128,
+2018-11-27T10:09:42.921643287Z     "hidden_size": 128,
+2018-11-27T10:09:42.92164597Z     "input_dropout": 0.0,
+2018-11-27T10:09:42.921648802Z     "learning_rate": 0.005,
+2018-11-27T10:09:42.921651533Z     "max_grad_norm": 5.0,
+2018-11-27T10:09:42.921654202Z     "num_layers": 2,
+2018-11-27T10:09:42.921656955Z     "num_unrollings": 64
+2018-11-27T10:09:42.921659795Z }
+2018-11-27T10:09:42.921662383Z 
+2018-11-27T10:09:43.077707805Z tensor_file:/home/jovyan/work/data/poem/poem_ids.txt
+2018-11-27T10:09:43.077982531Z Loading dataset from /home/jovyan/work/data/poem/poem_ids.txt
+2018-11-27T10:09:43.388716364Z file maxSeqLen = 64
+2018-11-27T10:09:43.390863888Z Loaded /home/jovyan/work/data/poem/:  training  samples:65235 ,validationSamples:3837,testingSamples:7676
+2018-11-27T10:09:43.899007416Z tensor_file:/home/jovyan/work/data/poem/poem_ids.txt
+2018-11-27T10:09:43.89904835Z Loading dataset from /home/jovyan/work/data/poem/poem_ids.txt
+2018-11-27T10:09:44.240973562Z file maxSeqLen = 64
+2018-11-27T10:09:44.241007275Z Loaded /home/jovyan/work/data/poem/:  training  samples:65235 ,validationSamples:3837,testingSamples:7676
+2018-11-27T10:09:44.299236281Z tensor_file:/home/jovyan/work/data/poem/poem_ids.txt
+2018-11-27T10:09:44.299265026Z Loading dataset from /home/jovyan/work/data/poem/poem_ids.txt
+2018-11-27T10:09:44.770941043Z file maxSeqLen = 64
+2018-11-27T10:09:44.770973167Z Loaded /home/jovyan/work/data/poem/:  training  samples:65235 ,validationSamples:3837,testingSamples:7676
+2018-11-27T10:09:44.791900509Z 06:09:44 INFO:Creating graph
+2018-11-27T10:09:55.183177953Z 06:09:55 INFO:Start training
+2018-11-27T10:09:55.183218136Z 
+2018-11-27T10:09:55.184259003Z 2018-11-27 18:09:55.183876: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
+2018-11-27T10:09:55.373253059Z 2018-11-27 18:09:55.373049: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:964] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
+2018-11-27T10:09:55.375447405Z 2018-11-27 18:09:55.375214: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1432] Found device 0 with properties: 
+2018-11-27T10:09:55.375461183Z name: Tesla P100-PCIE-16GB major: 6 minor: 0 memoryClockRate(GHz): 1.3285
+2018-11-27T10:09:55.37546483Z pciBusID: 0000:00:07.0
+2018-11-27T10:09:55.375467846Z totalMemory: 15.90GiB freeMemory: 15.61GiB
+2018-11-27T10:09:55.375470809Z 2018-11-27 18:09:55.375257: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1511] Adding visible gpu devices: 0
+2018-11-27T10:09:56.360845496Z 2018-11-27 18:09:56.360051: I tensorflow/core/common_runtime/gpu/gpu_device.cc:982] Device interconnect StreamExecutor with strength 1 edge matrix:
+2018-11-27T10:09:56.360894668Z 2018-11-27 18:09:56.360088: I tensorflow/core/common_runtime/gpu/gpu_device.cc:988]      0 
+2018-11-27T10:09:56.360903616Z 2018-11-27 18:09:56.360094: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1001] 0:   N 
+2018-11-27T10:09:56.360909021Z 2018-11-27 18:09:56.360448: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 15129 MB memory) -> physical GPU (device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:07.0, compute capability: 6.0)
+2018-11-27T10:10:01.217951495Z 06:10:01 INFO:=================== Epoch 0 ===================
+2018-11-27T10:10:01.217992526Z 
+2018-11-27T10:10:01.217997421Z 06:10:01 INFO:Training on training set
+2018-11-27T10:10:11.032344661Z 06:10:11 INFO:2.5%, step:99, perplexity: 579.997, speed: 10555 words
+2018-11-27T10:10:15.540573754Z 06:10:15 INFO:4.9%, step:199, perplexity: 365.581, speed: 14412 words
+2018-11-27T10:10:19.868017238Z 06:10:19 INFO:7.4%, step:299, perplexity: 280.805, speed: 16569 words
+2018-11-27T10:10:24.319889409Z 06:10:24 INFO:9.8%, step:399, perplexity: 234.879, speed: 17815 words
+2018-11-27T10:10:28.914214196Z 06:10:28 INFO:12.3%, step:499, perplexity: 205.698, speed: 18560 words
+2018-11-27T10:10:33.221910427Z 06:10:33 INFO:14.7%, step:599, perplexity: 184.924, speed: 19264 words
+2018-11-27T10:10:37.499999909Z 06:10:37 INFO:17.2%, step:699, perplexity: 169.067, speed: 19817 words
+2018-11-27T10:10:41.782939554Z 06:10:41 INFO:19.6%, step:799, perplexity: 156.408, speed: 20250 words
+2018-11-27T10:10:46.086558656Z 06:10:46 INFO:22.1%, step:899, perplexity: 146.158, speed: 20591 words
+2018-11-27T10:10:50.391412226Z 06:10:50 INFO:24.5%, step:999, perplexity: 137.768, speed: 20871 words
+2018-11-27T10:10:54.836464541Z 06:10:54 INFO:27.0%, step:1099, perplexity: 130.760, speed: 21052 words
+2018-11-27T10:10:59.273939872Z 06:10:59 INFO:29.4%, step:1199, perplexity: 124.744, speed: 21206 words
+2018-11-27T10:11:03.674085815Z 06:11:03 INFO:31.9%, step:1299, perplexity: 119.542, speed: 21352 words
+2018-11-27T10:11:08.11125808Z 06:11:08 INFO:34.3%, step:1399, perplexity: 114.989, speed: 21466 words
+2018-11-27T10:11:12.379560338Z 06:11:12 INFO:36.8%, step:1499, perplexity: 110.970, speed: 21618 words
+2018-11-27T10:11:16.762358631Z 06:11:16 INFO:39.2%, step:1599, perplexity: 107.408, speed: 21720 words
+2018-11-27T10:11:21.049873647Z 06:11:21 INFO:41.7%, step:1699, perplexity: 104.224, speed: 21836 words
+2018-11-27T10:11:25.449693668Z 06:11:25 INFO:44.1%, step:1799, perplexity: 101.359, speed: 21911 words
+2018-11-27T10:11:29.832817937Z 06:11:29 INFO:46.6%, step:1899, perplexity: 98.749, speed: 21983 words
+2018-11-27T10:11:34.45785477Z 06:11:34 INFO:49.0%, step:1999, perplexity: 96.351, speed: 21991 words
+2018-11-27T10:11:38.701768996Z 06:11:38 INFO:51.5%, step:2099, perplexity: 94.168, speed: 22084 words
+2018-11-27T10:11:42.84874719Z 06:11:42 INFO:53.9%, step:2199, perplexity: 92.176, speed: 22190 words
+2018-11-27T10:11:47.237563225Z 06:11:47 INFO:56.4%, step:2299, perplexity: 90.341, speed: 22238 words
+2018-11-27T10:11:51.334678139Z 06:11:51 INFO:58.9%, step:2399, perplexity: 88.632, speed: 22340 words
+2018-11-27T10:11:55.887346818Z 06:11:55 INFO:61.3%, step:2499, perplexity: 87.042, speed: 22347 words
+2018-11-27T10:12:00.221511695Z 06:12:00 INFO:63.8%, step:2599, perplexity: 85.542, speed: 22393 words
+2018-11-27T10:12:04.577282729Z 06:12:04 INFO:66.2%, step:2699, perplexity: 84.125, speed: 22433 words
+2018-11-27T10:12:09.192723567Z 06:12:09 INFO:68.7%, step:2799, perplexity: 82.786, speed: 22424 words
+2018-11-27T10:12:13.340539137Z 06:12:13 INFO:71.1%, step:2899, perplexity: 81.517, speed: 22495 words
+2018-11-27T10:12:17.551625495Z 06:12:17 INFO:73.6%, step:2999, perplexity: 80.310, speed: 22551 words
+2018-11-27T10:12:21.821009645Z 06:12:21 INFO:76.0%, step:3099, perplexity: 79.170, speed: 22595 words
+2018-11-27T10:12:26.07197127Z 06:12:26 INFO:78.5%, step:3199, perplexity: 78.088, speed: 22638 words
+2018-11-27T10:12:30.401157218Z 06:12:30 INFO:80.9%, step:3299, perplexity: 77.054, speed: 22668 words
+2018-11-27T10:12:34.649919317Z 06:12:34 INFO:83.4%, step:3399, perplexity: 76.065, speed: 22708 words
+2018-11-27T10:12:39.034718148Z 06:12:39 INFO:85.8%, step:3499, perplexity: 75.120, speed: 22726 words
+2018-11-27T10:12:43.378686867Z 06:12:43 INFO:88.3%, step:3599, perplexity: 74.220, speed: 22749 words
+2018-11-27T10:12:47.9802102Z 06:12:47 INFO:90.7%, step:3699, perplexity: 73.360, speed: 22735 words
+2018-11-27T10:12:52.460013383Z 06:12:52 INFO:93.2%, step:3799, perplexity: 72.535, speed: 22738 words
+2018-11-27T10:12:56.782595052Z 06:12:56 INFO:95.6%, step:3899, perplexity: 71.743, speed: 22761 words
+2018-11-27T10:13:01.210315439Z 06:13:01 INFO:98.1%, step:3999, perplexity: 70.985, speed: 22771 words
+2018-11-27T10:13:04.515673325Z 06:13:04 INFO:Perplexity: 41.065, speed: 22795 words per sec
+2018-11-27T10:13:06.383265041Z 06:13:06 INFO:Latest model saved in /home/jovyan/work/results/output_poem/save_model/model-4078
+2018-11-27T10:13:06.38335135Z 
+2018-11-27T10:13:06.383361623Z 06:13:06 INFO:Evaluate on validation set
+2018-11-27T10:13:10.150923351Z 06:13:10 INFO:41.7%, step:99, perplexity: 27.212, speed: 31566 words
+2018-11-27T10:13:12.141522115Z 06:13:12 INFO:83.3%, step:199, perplexity: 27.008, speed: 39128 words
+2018-11-27T10:13:12.852132512Z 06:13:12 INFO:Perplexity: 27.770, speed: 41338 words per sec
+2018-11-27T10:13:14.134856897Z 06:13:14 INFO:Best model is saved in /home/jovyan/work/results/output_poem/best_model/model-4078
+2018-11-27T10:13:14.134886734Z 06:13:14 INFO:Best validation ppl is 27.769833
+2018-11-27T10:13:14.134891404Z 
+2018-11-27T10:13:14.136491627Z 06:13:14 INFO:=================== Epoch 1 ===================
+2018-11-27T10:13:14.136507328Z 
+2018-11-27T10:13:14.136512125Z 06:13:14 INFO:Training on training set
+2018-11-27T10:13:18.37093865Z 06:13:18 INFO:2.5%, step:99, perplexity: 31.367, speed: 24195 words
+2018-11-27T10:13:22.67200999Z 06:13:22 INFO:4.9%, step:199, perplexity: 30.462, speed: 23999 words
+2018-11-27T10:13:27.100723019Z 06:13:27 INFO:7.4%, step:299, perplexity: 30.253, speed: 23701 words
+2018-11-27T10:13:31.506242791Z 06:13:31 INFO:9.8%, step:399, perplexity: 30.191, speed: 23585 words
+2018-11-27T10:13:35.754251214Z 06:13:35 INFO:12.3%, step:499, perplexity: 30.156, speed: 23686 words
+2018-11-27T10:13:40.123424997Z 06:13:40 INFO:14.7%, step:599, perplexity: 30.072, speed: 23644 words
+2018-11-27T10:13:44.805115792Z 06:13:44 INFO:17.2%, step:699, perplexity: 29.913, speed: 23374 words
+2018-11-27T10:13:49.109015968Z 06:13:49 INFO:19.6%, step:799, perplexity: 29.684, speed: 23425 words
+2018-11-27T10:13:53.444719904Z 06:13:53 INFO:22.1%, step:899, perplexity: 29.479, speed: 23447 words
+2018-11-27T10:13:57.70057894Z 06:13:57 INFO:24.5%, step:999, perplexity: 29.336, speed: 23507 words
+2018-11-27T10:14:02.035195139Z 06:14:02 INFO:27.0%, step:1099, perplexity: 29.224, speed: 23517 words
+2018-11-27T10:14:06.513941916Z 06:14:06 INFO:29.4%, step:1199, perplexity: 29.109, speed: 23461 words
+2018-11-27T10:14:10.843937135Z 06:14:10 INFO:31.9%, step:1299, perplexity: 29.007, speed: 23476 words
+2018-11-27T10:14:15.100460237Z 06:14:15 INFO:34.3%, step:1399, perplexity: 28.916, speed: 23517 words
+2018-11-27T10:14:19.328567505Z 06:14:19 INFO:36.8%, step:1499, perplexity: 28.837, speed: 23562 words
+2018-11-27T10:14:23.602516105Z 06:14:23 INFO:39.2%, step:1599, perplexity: 28.775, speed: 23587 words
+2018-11-27T10:14:27.891736465Z 06:14:27 INFO:41.7%, step:1699, perplexity: 28.724, speed: 23603 words
+2018-11-27T10:14:32.264798343Z 06:14:32 INFO:44.1%, step:1799, perplexity: 28.683, speed: 23593 words
+2018-11-27T10:14:36.751389471Z 06:14:36 INFO:46.6%, step:1899, perplexity: 28.640, speed: 23551 words
+2018-11-27T10:14:40.963914224Z 06:14:40 INFO:49.0%, step:1999, perplexity: 28.594, speed: 23588 words
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+2018-11-27T10:15:03.431963114Z 06:15:03 INFO:61.3%, step:2499, perplexity: 28.493, speed: 23423 words
+2018-11-27T10:15:07.994242671Z 06:15:07 INFO:63.8%, step:2599, perplexity: 28.469, speed: 23384 words
+2018-11-27T10:15:12.421086797Z 06:15:12 INFO:66.2%, step:2699, perplexity: 28.439, speed: 23375 words
+2018-11-27T10:15:16.630874054Z 06:15:16 INFO:68.7%, step:2799, perplexity: 28.404, speed: 23407 words
+2018-11-27T10:15:20.951036643Z 06:15:20 INFO:71.1%, step:2899, perplexity: 28.367, speed: 23417 words
+2018-11-27T10:15:25.27899812Z 06:15:25 INFO:73.6%, step:2999, perplexity: 28.325, speed: 23425 words
+2018-11-27T10:15:29.775674058Z 06:15:29 INFO:76.0%, step:3099, perplexity: 28.286, speed: 23404 words
+2018-11-27T10:15:34.126985091Z 06:15:34 INFO:78.5%, step:3199, perplexity: 28.248, speed: 23408 words
+2018-11-27T10:15:38.464845561Z 06:15:38 INFO:80.9%, step:3299, perplexity: 28.207, speed: 23415 words
+2018-11-27T10:15:42.990394552Z 06:15:42 INFO:83.4%, step:3399, perplexity: 28.165, speed: 23390 words
+2018-11-27T10:15:47.425927234Z 06:15:47 INFO:85.8%, step:3499, perplexity: 28.123, speed: 23381 words
+2018-11-27T10:15:51.87598534Z 06:15:51 INFO:88.3%, step:3599, perplexity: 28.083, speed: 23371 words
+2018-11-27T10:15:56.338438287Z 06:15:56 INFO:90.7%, step:3699, perplexity: 28.046, speed: 23359 words
+2018-11-27T10:16:00.900162713Z 06:16:00 INFO:93.2%, step:3799, perplexity: 28.009, speed: 23334 words
+2018-11-27T10:16:05.255897951Z 06:16:05 INFO:95.6%, step:3899, perplexity: 27.972, speed: 23339 words
+2018-11-27T10:16:09.663934234Z 06:16:09 INFO:98.1%, step:3999, perplexity: 27.937, speed: 23336 words
+2018-11-27T10:16:13.21823541Z 06:16:13 INFO:Perplexity: 26.581, speed: 23319 words per sec
+2018-11-27T10:16:14.410917929Z 06:16:14 INFO:Latest model saved in /home/jovyan/work/results/output_poem/save_model/model-8156
+2018-11-27T10:16:14.410963935Z 
+2018-11-27T10:16:14.410971269Z 06:16:14 INFO:Evaluate on validation set
+2018-11-27T10:16:17.491999462Z 06:16:17 INFO:41.7%, step:99, perplexity: 23.190, speed: 37610 words
+2018-11-27T10:16:19.430069711Z 06:16:19 INFO:83.3%, step:199, perplexity: 23.023, speed: 43960 words
+2018-11-27T10:16:20.172559347Z 06:16:20 INFO:Perplexity: 23.695, speed: 45476 words per sec
+2018-11-27T10:16:21.32888922Z 06:16:21 INFO:Best model is saved in /home/jovyan/work/results/output_poem/best_model/model-8156
+2018-11-27T10:16:21.328933688Z 06:16:21 INFO:Best validation ppl is 23.695059
+2018-11-27T10:16:21.328941622Z 
+2018-11-27T10:16:21.331146965Z 06:16:21 INFO:=================== Epoch 2 ===================
+2018-11-27T10:16:21.331195602Z 
+2018-11-27T10:16:21.33120213Z 06:16:21 INFO:Training on training set
+2018-11-27T10:16:25.822634972Z 06:16:25 INFO:2.5%, step:99, perplexity: 26.108, speed: 22809 words
+2018-11-27T10:16:30.154094196Z 06:16:30 INFO:4.9%, step:199, perplexity: 25.357, speed: 23218 words
+2018-11-27T10:16:34.519838458Z 06:16:34 INFO:7.4%, step:299, perplexity: 25.189, speed: 23296 words
+2018-11-27T10:16:38.863471994Z 06:16:38 INFO:9.8%, step:399, perplexity: 25.155, speed: 23365 words
+2018-11-27T10:16:43.276922221Z 06:16:43 INFO:12.3%, step:499, perplexity: 25.147, speed: 23338 words
+2018-11-27T10:16:47.667144852Z 06:16:47 INFO:14.7%, step:599, perplexity: 25.098, speed: 23331 words
+2018-11-27T10:16:51.989385556Z 06:16:51 INFO:17.2%, step:699, perplexity: 24.986, speed: 23383 words
+2018-11-27T10:16:56.648716698Z 06:16:56 INFO:19.6%, step:799, perplexity: 24.814, speed: 23197 words
+2018-11-27T10:17:00.896572508Z 06:17:00 INFO:22.1%, step:899, perplexity: 24.662, speed: 23294 words
+2018-11-27T10:17:05.339943441Z 06:17:05 INFO:24.5%, step:999, perplexity: 24.559, speed: 23270 words
+2018-11-27T10:17:09.829883397Z 06:17:09 INFO:27.0%, step:1099, perplexity: 24.483, speed: 23227 words
+2018-11-27T10:17:14.132195825Z 06:17:14 INFO:29.4%, step:1199, perplexity: 24.404, speed: 23274 words
+2018-11-27T10:17:18.893229243Z 06:17:18 INFO:31.9%, step:1299, perplexity: 24.337, speed: 23127 words
+2018-11-27T10:17:23.198893748Z 06:17:23 INFO:34.3%, step:1399, perplexity: 24.279, speed: 23173 words
+2018-11-27T10:17:27.691213117Z 06:17:27 INFO:36.8%, step:1499, perplexity: 24.230, speed: 23147 words
+2018-11-27T10:17:32.023904568Z 06:17:32 INFO:39.2%, step:1599, perplexity: 24.197, speed: 23177 words
+2018-11-27T10:17:36.622163391Z 06:17:36 INFO:41.7%, step:1699, perplexity: 24.174, speed: 23122 words
+2018-11-27T10:17:40.874052641Z 06:17:40 INFO:44.1%, step:1799, perplexity: 24.158, speed: 23173 words
+2018-11-27T10:17:45.210869425Z 06:17:45 INFO:46.6%, step:1899, perplexity: 24.142, speed: 23196 words
+2018-11-27T10:17:49.66595633Z 06:17:49 INFO:49.0%, step:1999, perplexity: 24.121, speed: 23185 words
+2018-11-27T10:17:54.114666252Z 06:17:54 INFO:51.5%, step:2099, perplexity: 24.112, speed: 23177 words
+2018-11-27T10:17:58.506224862Z 06:17:58 INFO:53.9%, step:2199, perplexity: 24.113, speed: 23183 words
+2018-11-27T10:18:02.778089366Z 06:18:02 INFO:56.4%, step:2299, perplexity: 24.118, speed: 23217 words
+2018-11-27T10:18:07.230983119Z 06:18:07 INFO:58.9%, step:2399, perplexity: 24.121, speed: 23209 words
+2018-11-27T10:18:12.027530948Z 06:18:12 INFO:61.3%, step:2499, perplexity: 24.125, speed: 23127 words
+2018-11-27T10:18:16.407152069Z 06:18:16 INFO:63.8%, step:2599, perplexity: 24.122, speed: 23137 words
+2018-11-27T10:18:20.835552933Z 06:18:20 INFO:66.2%, step:2699, perplexity: 24.114, speed: 23136 words
+2018-11-27T10:18:25.445355239Z 06:18:25 INFO:68.7%, step:2799, perplexity: 24.103, speed: 23102 words
+2018-11-27T10:18:29.688021836Z 06:18:29 INFO:71.1%, step:2899, perplexity: 24.088, speed: 23136 words
+2018-11-27T10:18:33.953928626Z 06:18:33 INFO:73.6%, step:2999, perplexity: 24.070, speed: 23164 words
+2018-11-27T10:18:38.383252436Z 06:18:38 INFO:76.0%, step:3099, perplexity: 24.054, speed: 23163 words
+2018-11-27T10:18:42.893777871Z 06:18:42 INFO:78.5%, step:3199, perplexity: 24.038, speed: 23149 words
+2018-11-27T10:18:47.31285914Z 06:18:47 INFO:80.9%, step:3299, perplexity: 24.020, speed: 23149 words
+2018-11-27T10:18:51.576226913Z 06:18:51 INFO:83.4%, step:3399, perplexity: 24.001, speed: 23173 words
+2018-11-27T10:18:55.943989507Z 06:18:55 INFO:85.8%, step:3499, perplexity: 23.981, speed: 23181 words
+2018-11-27T10:19:00.411905354Z 06:19:00 INFO:88.3%, step:3599, perplexity: 23.964, speed: 23174 words
+2018-11-27T10:19:04.863901015Z 06:19:04 INFO:90.7%, step:3699, perplexity: 23.948, speed: 23169 words
+2018-11-27T10:19:09.275138043Z 06:19:09 INFO:93.2%, step:3799, perplexity: 23.932, speed: 23170 words
+2018-11-27T10:19:13.679228985Z 06:19:13 INFO:95.6%, step:3899, perplexity: 23.917, speed: 23172 words
+2018-11-27T10:19:18.070953998Z 06:19:18 INFO:98.1%, step:3999, perplexity: 23.902, speed: 23176 words
+2018-11-27T10:19:21.512945327Z 06:19:21 INFO:Perplexity: 23.384, speed: 23176 words per sec
+2018-11-27T10:19:22.751208491Z 06:19:22 INFO:Latest model saved in /home/jovyan/work/results/output_poem/save_model/model-12234
+2018-11-27T10:19:22.751259163Z 
+2018-11-27T10:19:22.751265813Z 06:19:22 INFO:Evaluate on validation set
+2018-11-27T10:19:26.027680038Z 06:19:26 INFO:41.7%, step:99, perplexity: 22.205, speed: 35042 words
+2018-11-27T10:19:27.900094306Z 06:19:27 INFO:83.3%, step:199, perplexity: 22.039, speed: 42714 words
+2018-11-27T10:19:28.716925585Z 06:19:28 INFO:Perplexity: 22.719, speed: 43791 words per sec
+2018-11-27T10:19:30.073269798Z 06:19:30 INFO:Best model is saved in /home/jovyan/work/results/output_poem/best_model/model-12234
+2018-11-27T10:19:30.073333517Z 06:19:30 INFO:Best validation ppl is 22.719276
+2018-11-27T10:19:30.073345188Z 
+2018-11-27T10:19:30.07483804Z 06:19:30 INFO:=================== Epoch 3 ===================
+2018-11-27T10:19:30.074854818Z 
+2018-11-27T10:19:30.075604575Z 06:19:30 INFO:Training on training set
+2018-11-27T10:19:34.542630855Z 06:19:34 INFO:2.5%, step:99, perplexity: 23.989, speed: 22953 words
+2018-11-27T10:19:38.991967833Z 06:19:38 INFO:4.9%, step:199, perplexity: 23.323, speed: 22973 words
+2018-11-27T10:19:43.373945257Z 06:19:43 INFO:7.4%, step:299, perplexity: 23.167, speed: 23105 words
+2018-11-27T10:19:47.800022625Z 06:19:47 INFO:9.8%, step:399, perplexity: 23.135, speed: 23111 words
+2018-11-27T10:19:52.116562199Z 06:19:52 INFO:12.3%, step:499, perplexity: 23.130, speed: 23231 words
+2018-11-27T10:19:56.633976252Z 06:19:56 INFO:14.7%, step:599, perplexity: 23.090, speed: 23136 words
+2018-11-27T10:20:00.949289863Z 06:20:00 INFO:17.2%, step:699, perplexity: 22.991, speed: 23219 words
+2018-11-27T10:20:05.290965539Z 06:20:05 INFO:19.6%, step:799, perplexity: 22.838, speed: 23264 words
+2018-11-27T10:20:09.612679003Z 06:20:09 INFO:22.1%, step:899, perplexity: 22.705, speed: 23311 words
+2018-11-27T10:20:13.943285394Z 06:20:13 INFO:24.5%, step:999, perplexity: 22.619, speed: 23344 words
+2018-11-27T10:20:18.344987697Z 06:20:18 INFO:27.0%, step:1099, perplexity: 22.557, speed: 23338 words
+2018-11-27T10:20:22.570522059Z 06:20:22 INFO:29.4%, step:1199, perplexity: 22.492, speed: 23409 words
+2018-11-27T10:20:26.901527245Z 06:20:26 INFO:31.9%, step:1299, perplexity: 22.437, speed: 23427 words
+2018-11-27T10:20:31.059737324Z 06:20:31 INFO:34.3%, step:1399, perplexity: 22.391, speed: 23509 words
+2018-11-27T10:20:35.343045386Z 06:20:35 INFO:36.8%, step:1499, perplexity: 22.354, speed: 23539 words
+2018-11-27T10:20:40.22345844Z 06:20:40 INFO:39.2%, step:1599, perplexity: 22.329, speed: 23357 words
+2018-11-27T10:20:44.557509946Z 06:20:44 INFO:41.7%, step:1699, perplexity: 22.314, speed: 23373 words
+2018-11-27T10:20:48.708852989Z 06:20:48 INFO:44.1%, step:1799, perplexity: 22.306, speed: 23441 words
+2018-11-27T10:20:53.022344152Z 06:20:53 INFO:46.6%, step:1899, perplexity: 22.297, speed: 23456 words
+2018-11-27T10:20:57.37546736Z 06:20:57 INFO:49.0%, step:1999, perplexity: 22.285, speed: 23460 words
+2018-11-27T10:21:01.833109767Z 06:21:01 INFO:51.5%, step:2099, perplexity: 22.282, speed: 23436 words
+2018-11-27T10:21:06.151597571Z 06:21:06 INFO:53.9%, step:2199, perplexity: 22.289, speed: 23449 words
+2018-11-27T10:21:10.828731892Z 06:21:10 INFO:56.4%, step:2299, perplexity: 22.299, speed: 23376 words
+2018-11-27T10:21:15.126494472Z 06:21:15 INFO:58.9%, step:2399, perplexity: 22.308, speed: 23395 words
+2018-11-27T10:21:19.461160547Z 06:21:19 INFO:61.3%, step:2499, perplexity: 22.317, speed: 23404 words
+2018-11-27T10:21:23.793901164Z 06:21:23 INFO:63.8%, step:2599, perplexity: 22.320, speed: 23413 words
+2018-11-27T10:21:27.997158767Z 06:21:27 INFO:66.2%, step:2699, perplexity: 22.319, speed: 23447 words
+2018-11-27T10:21:32.29960135Z 06:21:32 INFO:68.7%, step:2799, perplexity: 22.314, speed: 23459 words
+2018-11-27T10:21:36.655561044Z 06:21:36 INFO:71.1%, step:2899, perplexity: 22.306, speed: 23461 words
+2018-11-27T10:21:41.143006311Z 06:21:41 INFO:73.6%, step:2999, perplexity: 22.295, speed: 23439 words
+2018-11-27T10:21:45.557130333Z 06:21:45 INFO:76.0%, step:3099, perplexity: 22.286, speed: 23431 words
+2018-11-27T10:21:49.887959316Z 06:21:49 INFO:78.5%, step:3199, perplexity: 22.277, speed: 23437 words
+2018-11-27T10:21:54.829782389Z 06:21:54 INFO:80.9%, step:3299, perplexity: 22.266, speed: 23345 words
+2018-11-27T10:21:59.329112412Z 06:21:59 INFO:83.4%, step:3399, perplexity: 22.254, speed: 23327 words
+2018-11-27T10:22:04.015500912Z 06:22:04 INFO:85.8%, step:3499, perplexity: 22.241, speed: 23282 words
+2018-11-27T10:22:08.357942505Z 06:22:08 INFO:88.3%, step:3599, perplexity: 22.231, speed: 23290 words
+2018-11-27T10:22:13.066890435Z 06:22:13 INFO:90.7%, step:3699, perplexity: 22.222, speed: 23246 words
+2018-11-27T10:22:17.645017158Z 06:22:17 INFO:93.2%, step:3799, perplexity: 22.212, speed: 23222 words
+2018-11-27T10:22:22.107816071Z 06:22:22 INFO:95.6%, step:3899, perplexity: 22.204, speed: 23215 words
+2018-11-27T10:22:26.560310365Z 06:22:26 INFO:98.1%, step:3999, perplexity: 22.196, speed: 23209 words
+2018-11-27T10:22:29.903176124Z 06:22:29 INFO:Perplexity: 21.945, speed: 23222 words per sec
+2018-11-27T10:22:31.075931565Z 06:22:31 INFO:Latest model saved in /home/jovyan/work/results/output_poem/save_model/model-16312
+2018-11-27T10:22:31.075967919Z 
+2018-11-27T10:22:31.075972504Z 06:22:31 INFO:Evaluate on validation set
+2018-11-27T10:22:34.245962411Z 06:22:34 INFO:41.7%, step:99, perplexity: 22.034, speed: 36666 words
+2018-11-27T10:22:36.0676778Z 06:22:36 INFO:83.3%, step:199, perplexity: 21.854, speed: 44384 words
+2018-11-27T10:22:36.77789724Z 06:22:36 INFO:Perplexity: 22.599, speed: 46162 words per sec
+2018-11-27T10:22:37.860643715Z 06:22:37 INFO:Best model is saved in /home/jovyan/work/results/output_poem/best_model/model-16312
+2018-11-27T10:22:37.860691125Z 06:22:37 INFO:Best validation ppl is 22.599243
+2018-11-27T10:22:37.860699352Z 
+2018-11-27T10:22:37.862282085Z 06:22:37 INFO:=================== Epoch 4 ===================
+2018-11-27T10:22:37.862332248Z 
+2018-11-27T10:22:37.862339865Z 06:22:37 INFO:Training on training set
+2018-11-27T10:22:42.37426273Z 06:22:42 INFO:2.5%, step:99, perplexity: 23.107, speed: 22705 words
+2018-11-27T10:22:46.680374852Z 06:22:46 INFO:4.9%, step:199, perplexity: 22.428, speed: 23232 words
+2018-11-27T10:22:51.03296342Z 06:22:51 INFO:7.4%, step:299, perplexity: 22.259, speed: 23334 words
+2018-11-27T10:22:55.426832231Z 06:22:55 INFO:9.8%, step:399, perplexity: 22.217, speed: 23327 words
+2018-11-27T10:22:59.777773075Z 06:22:59 INFO:12.3%, step:499, perplexity: 22.202, speed: 23365 words
+2018-11-27T10:23:04.145072125Z 06:23:04 INFO:14.7%, step:599, perplexity: 22.157, speed: 23378 words
+2018-11-27T10:23:09.1136806Z 06:23:09 INFO:17.2%, step:699, perplexity: 22.057, speed: 22977 words
+2018-11-27T10:23:13.648830834Z 06:23:13 INFO:19.6%, step:799, perplexity: 21.906, speed: 22893 words
+2018-11-27T10:23:18.025400022Z 06:23:18 INFO:22.1%, step:899, perplexity: 21.775, speed: 22948 words
+2018-11-27T10:23:22.348901926Z 06:23:22 INFO:24.5%, step:999, perplexity: 21.688, speed: 23019 words
+2018-11-27T10:23:26.676677066Z 06:23:26 INFO:27.0%, step:1099, perplexity: 21.626, speed: 23076 words
+2018-11-27T10:23:30.951963306Z 06:23:30 INFO:29.4%, step:1199, perplexity: 21.561, speed: 23146 words
+2018-11-27T10:23:35.232935959Z 06:23:35 INFO:31.9%, step:1299, perplexity: 21.507, speed: 23204 words
+2018-11-27T10:23:39.603813529Z 06:23:39 INFO:34.3%, step:1399, perplexity: 21.461, speed: 23221 words
+2018-11-27T10:23:44.017493496Z 06:23:44 INFO:36.8%, step:1499, perplexity: 21.424, speed: 23219 words
+2018-11-27T10:23:48.326467059Z 06:23:48 INFO:39.2%, step:1599, perplexity: 21.399, speed: 23252 words
+2018-11-27T10:23:52.585672907Z 06:23:52 INFO:41.7%, step:1699, perplexity: 21.384, speed: 23298 words
+2018-11-27T10:23:56.989893971Z 06:23:56 INFO:44.1%, step:1799, perplexity: 21.376, speed: 23295 words
+2018-11-27T10:24:01.393023201Z 06:24:01 INFO:46.6%, step:1899, perplexity: 21.367, speed: 23293 words
+2018-11-27T10:24:05.58477811Z 06:24:05 INFO:49.0%, step:1999, perplexity: 21.356, speed: 23347 words
+2018-11-27T10:24:09.947020057Z 06:24:09 INFO:51.5%, step:2099, perplexity: 21.354, speed: 23353 words
+2018-11-27T10:24:14.412994293Z 06:24:14 INFO:53.9%, step:2199, perplexity: 21.361, speed: 23333 words
+2018-11-27T10:24:18.954771216Z 06:24:18 INFO:56.4%, step:2299, perplexity: 21.371, speed: 23298 words
+2018-11-27T10:24:23.433249285Z 06:24:23 INFO:58.9%, step:2399, perplexity: 21.380, speed: 23280 words
+2018-11-27T10:24:27.670924005Z 06:24:27 INFO:61.3%, step:2499, perplexity: 21.389, speed: 23314 words
+2018-11-27T10:24:32.040370971Z 06:24:32 INFO:63.8%, step:2599, perplexity: 21.393, speed: 23318 words
+2018-11-27T10:24:36.300870634Z 06:24:36 INFO:66.2%, step:2699, perplexity: 21.393, speed: 23344 words
+2018-11-27T10:24:40.600139039Z 06:24:40 INFO:68.7%, step:2799, perplexity: 21.389, speed: 23361 words
+2018-11-27T10:24:45.049495157Z 06:24:45 INFO:71.1%, step:2899, perplexity: 21.383, speed: 23349 words
+2018-11-27T10:24:49.439408318Z 06:24:49 INFO:73.6%, step:2999, perplexity: 21.374, speed: 23348 words
+2018-11-27T10:24:53.802195957Z 06:24:53 INFO:76.0%, step:3099, perplexity: 21.366, speed: 23353 words
+2018-11-27T10:24:58.262550854Z 06:24:58 INFO:78.5%, step:3199, perplexity: 21.359, speed: 23339 words
+2018-11-27T10:25:02.636673549Z 06:25:02 INFO:80.9%, step:3299, perplexity: 21.350, speed: 23342 words
+2018-11-27T10:25:06.982697089Z 06:25:06 INFO:83.4%, step:3399, perplexity: 21.340, speed: 23348 words
+2018-11-27T10:25:11.487683521Z 06:25:11 INFO:85.8%, step:3499, perplexity: 21.329, speed: 23330 words
+2018-11-27T10:25:15.850716369Z 06:25:15 INFO:88.3%, step:3599, perplexity: 21.321, speed: 23334 words
+2018-11-27T10:25:20.217657971Z 06:25:20 INFO:90.7%, step:3699, perplexity: 21.314, speed: 23337 words
+2018-11-27T10:25:24.900127389Z 06:25:24 INFO:93.2%, step:3799, perplexity: 21.307, speed: 23296 words
+2018-11-27T10:25:29.231327973Z 06:25:29 INFO:95.6%, step:3899, perplexity: 21.300, speed: 23304 words
+2018-11-27T10:25:33.590834675Z 06:25:33 INFO:98.1%, step:3999, perplexity: 21.294, speed: 23309 words
+2018-11-27T10:25:36.874541841Z 06:25:36 INFO:Perplexity: 21.121, speed: 23327 words per sec
+2018-11-27T10:25:37.997983144Z 06:25:37 INFO:Latest model saved in /home/jovyan/work/results/output_poem/save_model/model-20390
+2018-11-27T10:25:37.998028676Z 
+2018-11-27T10:25:37.99803331Z 06:25:37 INFO:Evaluate on validation set
+2018-11-27T10:25:41.034447802Z 06:25:41 INFO:41.7%, step:99, perplexity: 21.705, speed: 38149 words
+2018-11-27T10:25:42.95159539Z 06:25:42 INFO:83.3%, step:199, perplexity: 21.544, speed: 44505 words
+2018-11-27T10:25:43.688599759Z 06:25:43 INFO:Perplexity: 22.314, speed: 46031 words per sec
+2018-11-27T10:25:44.848164248Z 06:25:44 INFO:Best model is saved in /home/jovyan/work/results/output_poem/best_model/model-20390
+2018-11-27T10:25:44.848221175Z 06:25:44 INFO:Best validation ppl is 22.313692
+2018-11-27T10:25:44.848228199Z 
+2018-11-27T10:25:44.849321317Z 06:25:44 INFO:Latest model is saved in /home/jovyan/work/results/output_poem/save_model/model-20390
+2018-11-27T10:25:44.849368157Z 06:25:44 INFO:Best model is saved in /home/jovyan/work/results/output_poem/best_model/model-20390
+2018-11-27T10:25:44.849375611Z 06:25:44 INFO:Best validation ppl is 22.313692
+2018-11-27T10:25:44.849378949Z 
+2018-11-27T10:25:44.849382077Z 06:25:44 INFO:Evaluate the best model on test set
+2018-11-27T10:25:48.459385356Z 06:25:48 INFO:20.8%, step:99, perplexity: 25.941, speed: 34469 words
+2018-11-27T10:25:50.191089303Z 06:25:50 INFO:41.7%, step:199, perplexity: 27.051, speed: 43544 words
+2018-11-27T10:25:52.06291597Z 06:25:52 INFO:62.5%, step:299, perplexity: 27.151, speed: 46722 words
+2018-11-27T10:25:53.799448645Z 06:25:53 INFO:83.3%, step:399, perplexity: 27.173, speed: 49281 words
+2018-11-27T10:25:55.447932056Z 06:25:55 INFO:Perplexity: 27.004, speed: 49355 words per sec
+2018-11-27T10:25:56.815541581Z SYSTEM: Finishing...
+2018-11-27T10:25:57.032365081Z SYSTEM: Done!
diff --git a/job_logs/job-gpu-5bfe24501afd942eee5c5020.log b/job_logs/job-gpu-5bfe24501afd942eee5c5020.log
index 8a075fa..24a2a1f 100644
--- a/job_logs/job-gpu-5bfe24501afd942eee5c5020.log
+++ b/job_logs/job-gpu-5bfe24501afd942eee5c5020.log
@@ -485,3 +485,4 @@
 2018-11-28T05:43:09.502860421Z 01:43:09 INFO:83.3%, step:399, perplexity: 25.702, speed: 46441 words
 2018-11-28T05:43:11.366013365Z 01:43:11 INFO:Perplexity: 25.562, speed: 46007 words per sec
 2018-11-28T05:43:12.680105735Z SYSTEM: Finishing...
+2018-11-28T05:43:12.909020997Z SYSTEM: Done!
diff --git a/job_logs/job-gpu-5c00ae391afd942b83265f05.log b/job_logs/job-gpu-5c00ae391afd942b83265f05.log
index efe6c7d..7b158d6 100644
--- a/job_logs/job-gpu-5c00ae391afd942b83265f05.log
+++ b/job_logs/job-gpu-5c00ae391afd942b83265f05.log
@@ -11,3 +11,4 @@
 2018-11-30T03:28:33.43192371Z     mkdir(name, mode)
 2018-11-30T03:28:33.431928773Z FileExistsError: [Errno 17] File exists: '/home/jovyan/work/results/tb_results/tensorboard_log'
 2018-11-30T03:28:33.993854401Z SYSTEM: Finishing...
+2018-11-30T03:28:34.319930803Z SYSTEM: Error Exists!
diff --git a/job_logs/job-gpu-5c00bbe61afd942cd74d7c6e.log b/job_logs/job-gpu-5c00bbe61afd942cd74d7c6e.log
index 01a8478..3d34c7d 100644
--- a/job_logs/job-gpu-5c00bbe61afd942cd74d7c6e.log
+++ b/job_logs/job-gpu-5c00bbe61afd942cd74d7c6e.log
@@ -11,3 +11,4 @@
 2018-11-30T04:26:45.569333699Z     mkdir(name, mode)
 2018-11-30T04:26:45.569338847Z FileExistsError: [Errno 17] File exists: '/home/jovyan/work/results/tb_results/tensorboard_log'
 2018-11-30T04:26:46.164187505Z SYSTEM: Finishing...
+2018-11-30T04:26:46.500529602Z SYSTEM: Error Exists!
diff --git a/job_logs/job-gpu-5c00be431afd942b66b36bad.log b/job_logs/job-gpu-5c00be431afd942b66b36bad.log
index cd484ba..e05340b 100644
--- a/job_logs/job-gpu-5c00be431afd942b66b36bad.log
+++ b/job_logs/job-gpu-5c00be431afd942b66b36bad.log
@@ -11,3 +11,4 @@
 2018-11-30T04:36:51.276884231Z     mkdir(name, mode)
 2018-11-30T04:36:51.276888696Z FileExistsError: [Errno 17] File exists: '/home/jovyan/work/results/tb_results/tensorboard_log'
 2018-11-30T04:36:51.975847222Z SYSTEM: Finishing...
+2018-11-30T04:36:52.34015789Z SYSTEM: Error Exists!
diff --git a/job_logs/job-gpu-5c00c00d1afd942b54c88504.log b/job_logs/job-gpu-5c00c00d1afd942b54c88504.log
index e5b0457..70bfef2 100644
--- a/job_logs/job-gpu-5c00c00d1afd942b54c88504.log
+++ b/job_logs/job-gpu-5c00c00d1afd942b54c88504.log
@@ -860,3 +860,4 @@
 2018-11-30T05:59:38.355844634Z 01:59:38 INFO:83.3%, step:399, perplexity: 25.498, speed: 31709 words
 2018-11-30T05:59:40.519902775Z 01:59:40 INFO:Perplexity: 25.508, speed: 32599 words per sec
 2018-11-30T05:59:42.862057327Z SYSTEM: Finishing...
+2018-11-30T05:59:43.22784752Z SYSTEM: Done!
diff --git a/main.ipynb b/main.ipynb
index 9b906ed..3062ab9 100644
--- a/main.ipynb
+++ b/main.ipynb
@@ -2,21 +2,9 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "ename": "ImportError",
-     "evalue": "No module named 'modules'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-1-b6ce2ceab38e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[0;31m# Import necessary packages\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 13\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mmodules\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mjson_parser\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     14\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mmodules\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mClient\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mImportError\u001b[0m: No module named 'modules'"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# coding = utf8\n",
     "# You can use other public modules via our Client object with module's identifier\n",
@@ -53,25 +41,6 @@
    "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "/home/jovyan/work\n",
-      "07:26:17 INFO:best_model: /home/jovyan/work/results/output_poem/best_model/model-20390\n",
-      "\n",
-      "result：\n",
-      "大妃非是列，志事满黄枢。始喜封阳礼，开摩舞罢歌。\n"
-     ]
-    }
-   ],
    "source": [
     "from write_poem import start_model\n",
     "\n",
@@ -102,21 +71,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "ename": "ImportError",
-     "evalue": "No module named 'modules'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-2-3bc0b5b2f564>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;31m# Import necessary packages\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;31m# from modules import json_parser\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mmodules\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mClient\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mwrite_poem\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mstart_model\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mImportError\u001b[0m: No module named 'modules'"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# coding: utf-8\n",
     "import os\n",
diff --git a/main.py b/main.py
deleted file mode 100644
index 5249003..0000000
--- a/main.py
+++ /dev/null
@@ -1,40 +0,0 @@
-# coding: utf-8
-import os
-import sys
-
-# Import necessary packages
-from modules import json_parser
-from modules import Client
-from write_poem import start_model
-
-# Initialise Client object
-client = Client(api_key='33799e1c5d6fa05fdd7dec3aa7aad868445d1c737edcf9c37fa89cb3b39cb2d9',
-                project_id='5bfd118f1afd942b66b36b30', user_ID='yangsaisai',
-                project_type='app', source_file_path='main.ipynb', 
-                silent=True)
-
-# Make run/train/predict command alias for further use
-run = client.run
-train = client.train
-predict = client.predict
-
-# Make controller alias for further use
-controller = client.controller
-writer = start_model()
-
-
-def handle(conf):
-    # paste your code here
-    if conf['style'] == '藏头诗':
-        poetry = writer.cangtou(conf['Chinese_word'])
-    elif conf['style'] == '藏字诗':
-        poetry = writer.hide_words(conf['Chinese_word'])
-    else:
-        poetry = writer.rhyme_verse()
-    return {"Poetry": poetry}
-    # return {"Poetry": 'asadd'}
-
-# if __name__ == '__main__':
-#     conf = {"style": ['藏头诗'], "Chinese_word": '杨赛赛'}
-#     aa = handle(conf)
-#     print(aa)
diff --git a/results/output_poem/.ipynb_checkpoints/result-checkpoint.json b/results/output_poem/.ipynb_checkpoints/result-checkpoint.json
new file mode 100644
index 0000000..fe65849
--- /dev/null
+++ b/results/output_poem/.ipynb_checkpoints/result-checkpoint.json
@@ -0,0 +1,18 @@
+{
+  "best_model": "./results/output_poem/best_model/model-61170",
+  "best_valid_ppl": 21.06464385986328,
+  "latest_model": "./results/output_poem/save_model/model-61170",
+  "params": {
+    "batch_size": 16,
+    "cell_type": "lstm",
+    "dropout": 0.0,
+    "embedding_size": 128,
+    "hidden_size": 128,
+    "input_dropout": 0.0,
+    "learning_rate": 0.005,
+    "max_grad_norm": 5.0,
+    "num_layers": 2,
+    "num_unrollings": 64
+  },
+  "test_ppl": 25.508337020874023
+}
\ No newline at end of file
diff --git a/results/tb_results/tensorboard_log/1543547440/summaries/train/.nfs00000000011509b40000001c b/results/tb_results/tensorboard_log/1543547440/summaries/train/.nfs00000000011509b40000001c
deleted file mode 100644
index 8c5a5a8..0000000
Binary files a/results/tb_results/tensorboard_log/1543547440/summaries/train/.nfs00000000011509b40000001c and /dev/null differ
diff --git a/results/tb_results/tensorboard_log/1543547440/summaries/valid/.nfs00000000011509b60000001d b/results/tb_results/tensorboard_log/1543547440/summaries/valid/.nfs00000000011509b60000001d
deleted file mode 100644
index e11200d..0000000
Binary files a/results/tb_results/tensorboard_log/1543547440/summaries/valid/.nfs00000000011509b60000001d and /dev/null differ
diff --git a/train.py b/train.py
index adfeb79..2269d16 100644
--- a/train.py
+++ b/train.py
@@ -1,11 +1,8 @@
-import codecs
 import json
 import logging
 import os
-import shutil
 import sys
 import time
-import numpy as np
 import tensorflow as tf
 from char_rnn_model import CharRNNLM
 from config_poem import config_poem_train
diff --git a/write_poem.py b/write_poem.py
index 5848dac..06c45cb 100644
--- a/write_poem.py
+++ b/write_poem.py
@@ -25,7 +25,7 @@
 
         params = result['params']
         best_model = result['best_model']
-        best_valid_ppl = result['best_valid_ppl']
+        # best_valid_ppl = result['best_valid_ppl']
         if 'encoding' in result:
             self.args.encoding = result['encoding']
         else:
@@ -216,5 +216,6 @@
     writer = WritePoem(args)
     return writer
 
+
 if __name__ == '__main__':
     writer = start_model()