Commit 549e5c863635023da9825ec0b31f9095ff2e98df - poetry

yangsaisai-overview yangsaisai 7 years ago

24 changed file(s) with 496 addition(s) and 456 deletion(s). Raw diff Collapse all Expand all

-18

.ipynb_checkpoints/OVERVIEW-checkpoint.md less more

0	0
1		## Intro
	1	## 介绍
2	2
3		Provide a short overview of your algorithm that explains the value and primary use cases.
4
5		## Usage
6
7		```
8		def function_name(conf={})
9		```
10
11		## Example
12
13		Provide and explain examples of input and output for your algorithm.
14
15		`Code`
16
17		```
18		please input your example code
19		```
	3	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。
	4	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。”
	5	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～⏎

-6

~~.ipynb_checkpoints/Untitled-checkpoint.ipynb~~ less more

0		{
1		"cells": [],
2		"metadata": {},
3		"nbformat": 4,
4		"nbformat_minor": 2
5		}

+40

-0

.ipynb_checkpoints/handler-checkpoint.py less more

	0	# coding: utf-8
	1	import os
	2	import sys
	3
	4	# Import necessary packages
	5	from modules import json_parser
	6	from modules import Client
	7	from write_poem import start_model
	8
	9	# Initialise Client object
	10	client = Client(api_key='33799e1c5d6fa05fdd7dec3aa7aad868445d1c737edcf9c37fa89cb3b39cb2d9',
	11	project_id='5bfd118f1afd942b66b36b30', user_ID='yangsaisai',
	12	project_type='app', source_file_path='main.ipynb',
	13	silent=True)
	14
	15	# Make run/train/predict command alias for further use
	16	run = client.run
	17	train = client.train
	18	predict = client.predict
	19
	20	# Make controller alias for further use
	21	controller = client.controller
	22	writer = start_model()
	23
	24
	25	def handle(conf):
	26	# paste your code here
	27	if conf['style'] == '藏头诗':
	28	poetry = writer.cangtou(conf['Chinese_word'])
	29	elif conf['style'] == '藏字诗':
	30	poetry = writer.hide_words(conf['Chinese_word'])
	31	else:
	32	poetry = writer.rhyme_verse()
	33	return {"Poetry": poetry}
	34	# return {"Poetry": 'asadd'}
	35
	36	# if __name__ == '__main__':
	37	# conf = {"style": ['藏头诗'], "Chinese_word": '杨赛赛'}
	38	# aa = handle(conf)
	39	# print(aa)

-40

~~.ipynb_checkpoints/main-checkpoint.py~~ less more

0		# coding: utf-8
1		import os
2		import sys
3
4		# Import necessary packages
5		from modules import json_parser
6		from modules import Client
7		from write_poem import start_model
8
9		# Initialise Client object
10		client = Client(api_key='33799e1c5d6fa05fdd7dec3aa7aad868445d1c737edcf9c37fa89cb3b39cb2d9',
11		project_id='5bfd118f1afd942b66b36b30', user_ID='yangsaisai',
12		project_type='app', source_file_path='main.ipynb',
13		silent=True)
14
15		# Make run/train/predict command alias for further use
16		run = client.run
17		train = client.train
18		predict = client.predict
19
20		# Make controller alias for further use
21		controller = client.controller
22		writer = start_model()
23
24
25		def handle(conf):
26		# paste your code here
27		if conf['style'] == '藏头诗':
28		poetry = writer.cangtou(conf['Chinese_word'])
29		elif conf['style'] == '藏字诗':
30		poetry = writer.hide_words(conf['Chinese_word'])
31		else:
32		poetry = writer.rhyme_verse()
33		return {"Poetry": poetry}
34		# return {"Poetry": 'asadd'}
35
36		# if __name__ == '__main__':
37		# conf = {"style": ['藏头诗'], "Chinese_word": '杨赛赛'}
38		# aa = handle(conf)
39		# print(aa)

-197

.ipynb_checkpoints/train-checkpoint.py less more

0		import codecs
1		import json
2		import logging
3		import os
4		import shutil
5		import sys
6		import time
7		import numpy as np
8		import tensorflow as tf
9		from char_rnn_model import CharRNNLM
10		from config_poem import config_poem_train
11		from data_loader import DataLoader
12		from word2vec_helper import Word2Vec
13
14		sys.path.insert(0, os.path.dirname(__file__))
15		TF_VERSION = int(tf.__version__.split('.')[1])
16
17
18		def main(args=''):
19		args = config_poem_train(args)
20		# Specifying location to store model, best model and tensorboard log.
21		args.save_model = os.path.join(args.output_dir, 'save_model/model')
22		args.save_best_model = os.path.join(args.output_dir, 'best_model/model')
23		# args.tb_log_dir = os.path.join(args.output_dir, 'tensorboard_log/')
24		timestamp = str(int(time.time()))
25		# args.tb_log_dir = os.path.abspath(os.path.join(args.output_dir, "tensorboard_log", timestamp))
26		args.tb_log_dir = os.path.abspath(os.path.join('./results/tb_results', "tensorboard_log", timestamp))
27		print("Writing to {}\n".format(args.tb_log_dir))
28
29		# Create necessary directories.
30		if len(args.init_dir) != 0:
31		args.output_dir = args.init_dir
32
33		# else:
34		# if os.path.exists(args.output_dir):
35		# shutil.rmtree(args.output_dir)
36		# for paths in [args.save_model, args.save_best_model, args.tb_log_dir]:
37		# os.makedirs(os.path.dirname(paths))
38
39		logging.basicConfig(stream=sys.stdout,
40		format='%(asctime)s %(levelname)s:%(message)s',
41		level=logging.INFO, datefmt='%I:%M:%S')
42
43		print('=' * 60)
44		print('All final and intermediate outputs will be stored in %s/' % args.output_dir)
45		print('=' * 60 + '\n')
46
47		logging.info('args are:\n%s', args)
48
49		if len(args.init_dir) != 0:
50		with open(os.path.join(args.init_dir, 'result.json'), 'r') as f:
51		result = json.load(f)
52		params = result['params']
53		args.init_model = result['latest_model']
54		best_model = result['best_model']
55		best_valid_ppl = result['best_valid_ppl']
56		if 'encoding' in result:
57		args.encoding = result['encoding']
58		else:
59		args.encoding = 'utf-8'
60
61		else:
62		params = {'batch_size': args.batch_size,
63		'num_unrollings': args.num_unrollings,
64		'hidden_size': args.hidden_size,
65		'max_grad_norm': args.max_grad_norm,
66		'embedding_size': args.embedding_size,
67		'num_layers': args.num_layers,
68		'learning_rate': args.learning_rate,
69		'cell_type': args.cell_type,
70		'dropout': args.dropout,
71		'input_dropout': args.input_dropout}
72		best_model = ''
73		logging.info('Parameters are:\n%s\n', json.dumps(params, sort_keys=True, indent=4))
74
75		# Create batch generators.
76		batch_size = params['batch_size']
77		num_unrollings = params['num_unrollings']
78
79		base_path = args.data_path
80		w2v_file = os.path.join(base_path, "vectors_poem.bin")
81		w2v = Word2Vec(w2v_file)
82
83		train_data_loader = DataLoader(base_path, batch_size, num_unrollings, w2v.model, 'train')
84		test1_data_loader = DataLoader(base_path, batch_size, num_unrollings, w2v.model, 'test')
85		valid_data_loader = DataLoader(base_path, batch_size, num_unrollings, w2v.model, 'valid')
86
87		# Create graphs
88		logging.info('Creating graph')
89		graph = tf.Graph()
90		with graph.as_default():
91		w2v_vocab_size = len(w2v.model.vocab)
92		with tf.name_scope('training'):
93		train_model = CharRNNLM(is_training=True, w2v_model = w2v.model, vocab_size=w2v_vocab_size, infer=False, **params)
94		tf.get_variable_scope().reuse_variables()
95
96		with tf.name_scope('validation'):
97		valid_model = CharRNNLM(is_training=False, w2v_model = w2v.model, vocab_size=w2v_vocab_size, infer=False, **params)
98
99		with tf.name_scope('evaluation'):
100		test_model = CharRNNLM(is_training=False, w2v_model = w2v.model,vocab_size=w2v_vocab_size, infer=False, **params)
101		saver = tf.train.Saver(name='model_saver')
102		best_model_saver = tf.train.Saver(name='best_model_saver')
103
104		logging.info('Start training\n')
105
106		result = {}
107		result['params'] = params
108
109		try:
110		with tf.Session(graph=graph) as session:
111		# Version 8 changed the api of summary writer to use
112		# graph instead of graph_def.
113		if TF_VERSION >= 8:
114		graph_info = session.graph
115		else:
116		graph_info = session.graph_def
117
118		train_summary_dir = os.path.join(args.tb_log_dir, "summaries", "train")
119		train_writer = tf.summary.FileWriter(train_summary_dir, graph_info)
120		valid_summary_dir = os.path.join(args.tb_log_dir, "summaries", "valid")
121		valid_writer = tf.summary.FileWriter(valid_summary_dir, graph_info)
122
123		# load a saved model or start from random initialization.
124		if len(args.init_model) != 0:
125		saver.restore(session, args.init_model)
126		else:
127		tf.global_variables_initializer().run()
128
129		learning_rate = args.learning_rate
130		for epoch in range(args.num_epochs):
131		logging.info('=' * 19 + ' Epoch %d ' + '=' * 19 + '\n', epoch)
132		logging.info('Training on training set')
133		# training step
134		ppl, train_summary_str, global_step = train_model.run_epoch(session, train_data_loader, is_training=True,
135		learning_rate=learning_rate, verbose=args.verbose, freq=args.progress_freq)
136		# record the summary
137		train_writer.add_summary(train_summary_str, global_step)
138		train_writer.flush()
139		# save model
140		saved_path = saver.save(session, args.save_model,
141		global_step=train_model.global_step)
142
143		logging.info('Latest model saved in %s\n', saved_path)
144		logging.info('Evaluate on validation set')
145
146		valid_ppl, valid_summary_str, _ = valid_model.run_epoch(session, valid_data_loader, is_training=False,
147		learning_rate=learning_rate, verbose=args.verbose, freq=args.progress_freq)
148
149		# save and update best model
150		if (len(best_model) == 0) or (valid_ppl < best_valid_ppl):
151		best_model = best_model_saver.save(session, args.save_best_model,
152		global_step=train_model.global_step)
153		best_valid_ppl = valid_ppl
154		else:
155		learning_rate /= 2.0
156		logging.info('Decay the learning rate: ' + str(learning_rate))
157
158		valid_writer.add_summary(valid_summary_str, global_step)
159		valid_writer.flush()
160
161		logging.info('Best model is saved in %s', best_model)
162		logging.info('Best validation ppl is %f\n', best_valid_ppl)
163
164		result['latest_model'] = saved_path
165		result['best_model'] = best_model
166		# Convert to float because numpy.float is not json serializable.
167		result['best_valid_ppl'] = float(best_valid_ppl)
168
169		result_path = os.path.join(args.output_dir, 'result.json')
170		if os.path.exists(result_path):
171		os.remove(result_path)
172		with open(result_path, 'w') as f:
173		json.dump(result, f, indent=2, sort_keys=True)
174
175		logging.info('Latest model is saved in %s', saved_path)
176		logging.info('Best model is saved in %s', best_model)
177		logging.info('Best validation ppl is %f\n', best_valid_ppl)
178
179		logging.info('Evaluate the best model on test set')
180		saver.restore(session, best_model)
181		test_ppl, _, _ = test_model.run_epoch(session, test1_data_loader, is_training=False,
182		learning_rate=learning_rate, verbose=args.verbose, freq=args.progress_freq)
183		result['test_ppl'] = float(test_ppl)
184		except Exception as e:
185		print('err :{}'.format(e))
186		finally:
187		result_path = os.path.join(args.output_dir, 'result.json')
188		if os.path.exists(result_path):
189		os.remove(result_path)
190		with open(result_path, 'w', encoding='utf-8', errors='ignore') as f:
191		json.dump(result, f, indent=2, sort_keys=True)
192
193
194		if __name__ == '__main__':
195		args = '--output_dir ./results/output_poem --data_path ./datasets/yangsaisai-poetrydatasets-0_0_1/ --hidden_size 128 --embedding_size 128 --cell_type lstm'
196		main(args)

-1

.ipynb_checkpoints/write_poem-checkpoint.py less more

24	24
25	25	params = result['params']
26	26	best_model = result['best_model']
27		best_valid_ppl = result['best_valid_ppl']
	27	# best_valid_ppl = result['best_valid_ppl']
28	28	if 'encoding' in result:
29	29	self.args.encoding = result['encoding']
30	30	else:

215	215	writer = WritePoem(args)
216	216	return writer
217	217
	218
218	219	if __name__ == '__main__':
219	220	writer = start_model()

-18

OVERVIEW.md less more

0	0
1		## Intro
	1	## 介绍
2	2
3		Provide a short overview of your algorithm that explains the value and primary use cases.
4
5		## Usage
6
7		```
8		def function_name(conf={})
9		```
10
11		## Example
12
13		Provide and explain examples of input and output for your algorithm.
14
15		`Code`
16
17		```
18		please input your example code
19		```
	3	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。
	4	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。”
	5	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～⏎

-85

~~Untitled.ipynb~~ less more

0		{
1		"cells": [
2		{
3		"cell_type": "code",
4		"execution_count": 1,
5		"metadata": {},
6		"outputs": [],
7		"source": [
8		"# You can use other public modules via our Client object with module's identifier\n",
9		"# and parameters.\n",
10		"# For more detailes, please see our online document - https://momodel.github.io/docs/#\n",
11		"\n",
12		"import os\n",
13		"import sys\n",
14		"\n",
15		"# Define root path\n",
16		"sys.path.append('../')\n",
17		"\n",
18		"# Import necessary packages\n",
19		"from modules import json_parser\n",
20		"from modules import Client\n",
21		"\n",
22		"# Initialise Client object\n",
23		"client = Client(api_key='33799e1c5d6fa05fdd7dec3aa7aad868445d1c737edcf9c37fa89cb3b39cb2d9',\n",
24		" project_id='5bfd118f1afd942b66b36b30', user_ID='yangsaisai',\n",
25		" project_type='app', source_file_path='Untitled.ipynb')\n",
26		"\n",
27		"# Make run/train/predict command alias for further use\n",
28		"run = client.run\n",
29		"train = client.train\n",
30		"predict = client.predict\n",
31		"\n",
32		"# Make controller alias for further use\n",
33		"controller = client.controller\n",
34		"\n"
35		]
36		},
37		{
38		"cell_type": "code",
39		"execution_count": 2,
40		"metadata": {},
41		"outputs": [
42		{
43		"name": "stdout",
44		"output_type": "stream",
45		"text": [
46		"OVERVIEW.md poems_edge_split.txt rhyme_words.txt\n",
47		"poem_ids.txt \u001b[0m\u001b[01;34mresults\u001b[0m/ vectors_poem.bin\n"
48		]
49		}
50		],
51		"source": [
52		"ls /home/jovyan/work/datasets/yangsaisai-poetrydatasets-0_0_1"
53		]
54		},
55		{
56		"cell_type": "code",
57		"execution_count": null,
58		"metadata": {},
59		"outputs": [],
60		"source": []
61		}
62		],
63		"metadata": {
64		"kernelspec": {
65		"display_name": "Python 3",
66		"language": "python",
67		"name": "python3"
68		},
69		"language_info": {
70		"codemirror_mode": {
71		"name": "ipython",
72		"version": 3
73		},
74		"file_extension": ".py",
75		"mimetype": "text/x-python",
76		"name": "python",
77		"nbconvert_exporter": "python",
78		"pygments_lexer": "ipython3",
79		"version": "3.5.2"
80		}
81		},
82		"nbformat": 4,
83		"nbformat_minor": 2
84		}

+40

-0

handler.py less more

	0	# coding: utf-8
	1	import os
	2	import sys
	3
	4	# Import necessary packages
	5	from modules import json_parser
	6	from modules import Client
	7	from write_poem import start_model
	8
	9	# Initialise Client object
	10	client = Client(api_key='33799e1c5d6fa05fdd7dec3aa7aad868445d1c737edcf9c37fa89cb3b39cb2d9',
	11	project_id='5bfd118f1afd942b66b36b30', user_ID='yangsaisai',
	12	project_type='app', source_file_path='main.ipynb',
	13	silent=True)
	14
	15	# Make run/train/predict command alias for further use
	16	run = client.run
	17	train = client.train
	18	predict = client.predict
	19
	20	# Make controller alias for further use
	21	controller = client.controller
	22	writer = start_model()
	23
	24
	25	def handle(conf):
	26	# paste your code here
	27	if conf['style'] == '藏头诗':
	28	poetry = writer.cangtou(conf['Chinese_word'])
	29	elif conf['style'] == '藏字诗':
	30	poetry = writer.hide_words(conf['Chinese_word'])
	31	else:
	32	poetry = writer.rhyme_verse()
	33	return {"Poetry": poetry}
	34	# return {"Poetry": 'asadd'}
	35
	36	# if __name__ == '__main__':
	37	# conf = {"style": ['藏头诗'], "Chinese_word": '杨赛赛'}
	38	# aa = handle(conf)
	39	# print(aa)

+12

-0

job_logs/job-gpu-5bfd16971afd943016b451b7.log less more

	0	2018-11-27T10:04:22.659993645Z SYSTEM: Preparing env...
	1	2018-11-27T10:04:22.976307578Z SYSTEM: Running...
	2	2018-11-27T10:04:25.913548121Z Traceback (most recent call last):
	3	2018-11-27T10:04:25.913592229Z File "train.py", line 12, in <module>
	4	2018-11-27T10:04:25.913626672Z from data_loader import DataLoader
	5	2018-11-27T10:04:25.913659904Z File "/home/jovyan/work/data_loader.py", line 5, in <module>
	6	2018-11-27T10:04:25.913871435Z from word2vec_helper import Word2Vec
	7	2018-11-27T10:04:25.913883837Z File "/home/jovyan/work/word2vec_helper.py", line 2, in <module>
	8	2018-11-27T10:04:25.921915312Z import word2vec
	9	2018-11-27T10:04:25.921941434Z ImportError: No module named 'word2vec'
	10	2018-11-27T10:04:26.261989876Z SYSTEM: Finishing...
	11	2018-11-27T10:04:26.469404368Z SYSTEM: Error Exists!

+38

-0

job_logs/job-gpu-5bfd16f91afd942b66b36b34.log less more

	0	2018-11-27T10:07:01.321228212Z SYSTEM: Preparing env...
	1	2018-11-27T10:07:01.578526766Z SYSTEM: Running...
	2	2018-11-27T10:07:04.466118802Z Writing to /home/jovyan/work/results/output_poem/tensorboard_log/1543313224
	3	2018-11-27T10:07:04.466172666Z
	4	2018-11-27T10:07:04.588355555Z ============================================================
	5	2018-11-27T10:07:04.588401989Z All final and intermediate outputs will be stored in /home/jovyan/work/results/output_poem/
	6	2018-11-27T10:07:04.588410821Z ============================================================
	7	2018-11-27T10:07:04.588415325Z
	8	2018-11-27T10:07:04.588419611Z 06:07:04 INFO:args are:
	9	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)
	10	2018-11-27T10:07:04.588534657Z 06:07:04 INFO:Parameters are:
	11	2018-11-27T10:07:04.58854925Z {
	12	2018-11-27T10:07:04.588554542Z "batch_size": 16,
	13	2018-11-27T10:07:04.588559Z "cell_type": "lstm",
	14	2018-11-27T10:07:04.588563781Z "dropout": 0.0,
	15	2018-11-27T10:07:04.588568162Z "embedding_size": 128,
	16	2018-11-27T10:07:04.588572675Z "hidden_size": 128,
	17	2018-11-27T10:07:04.588577034Z "input_dropout": 0.0,
	18	2018-11-27T10:07:04.588581659Z "learning_rate": 0.005,
	19	2018-11-27T10:07:04.588585927Z "max_grad_norm": 5.0,
	20	2018-11-27T10:07:04.588590309Z "num_layers": 2,
	21	2018-11-27T10:07:04.58859518Z "num_unrollings": 64
	22	2018-11-27T10:07:04.588599829Z }
	23	2018-11-27T10:07:04.588604427Z
	24	2018-11-27T10:07:04.588939576Z Traceback (most recent call last):
	25	2018-11-27T10:07:04.588951533Z File "train.py", line 196, in <module>
	26	2018-11-27T10:07:04.589352881Z main(args)
	27	2018-11-27T10:07:04.589365786Z File "train.py", line 80, in main
	28	2018-11-27T10:07:04.589569032Z w2v = Word2Vec(w2v_file)
	29	2018-11-27T10:07:04.589579072Z File "/home/jovyan/work/word2vec_helper.py", line 7, in __init__
	30	2018-11-27T10:07:04.589921048Z self.model = word2vec.load(file_path)
	31	2018-11-27T10:07:04.589969692Z File "/home/jovyan/work/.localenv/lib/python3.5/site-packages/word2vec/io.py", line 16, in load
	32	2018-11-27T10:07:04.590576088Z return word2vec.WordVectors.from_binary(fname, args, *kwargs)
	33	2018-11-27T10:07:04.590602769Z File "/home/jovyan/work/.localenv/lib/python3.5/site-packages/word2vec/wordvectors.py", line 200, in from_binary
	34	2018-11-27T10:07:04.592977346Z with open(fname, "rb") as fin:
	35	2018-11-27T10:07:04.5929906Z FileNotFoundError: [Errno 2] No such file or directory: '/home/jovyan/work/src/data/poem/vectors_poem.bin'
	36	2018-11-27T10:07:04.896550412Z SYSTEM: Finishing...
	37	2018-11-27T10:07:05.127609348Z SYSTEM: Error Exists!

+327

-0

job_logs/job-gpu-5bfd17d51afd943016b451b9.log less more

	0	2018-11-27T10:09:39.680843461Z SYSTEM: Preparing env...
	1	2018-11-27T10:09:39.97799912Z SYSTEM: Running...
	2	2018-11-27T10:09:42.917073854Z Writing to /home/jovyan/work/results/output_poem/tensorboard_log/1543313382
	3	2018-11-27T10:09:42.917115651Z
	4	2018-11-27T10:09:42.921576408Z ============================================================
	5	2018-11-27T10:09:42.921590985Z All final and intermediate outputs will be stored in /home/jovyan/work/results/output_poem/
	6	2018-11-27T10:09:42.92160796Z ============================================================
	7	2018-11-27T10:09:42.921612797Z
	8	2018-11-27T10:09:42.921615725Z 06:09:42 INFO:args are:
	9	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)
	10	2018-11-27T10:09:42.921625405Z 06:09:42 INFO:Parameters are:
	11	2018-11-27T10:09:42.92162832Z {
	12	2018-11-27T10:09:42.921631027Z "batch_size": 16,
	13	2018-11-27T10:09:42.921634151Z "cell_type": "lstm",
	14	2018-11-27T10:09:42.92163743Z "dropout": 0.0,
	15	2018-11-27T10:09:42.921640321Z "embedding_size": 128,
	16	2018-11-27T10:09:42.921643287Z "hidden_size": 128,
	17	2018-11-27T10:09:42.92164597Z "input_dropout": 0.0,
	18	2018-11-27T10:09:42.921648802Z "learning_rate": 0.005,
	19	2018-11-27T10:09:42.921651533Z "max_grad_norm": 5.0,
	20	2018-11-27T10:09:42.921654202Z "num_layers": 2,
	21	2018-11-27T10:09:42.921656955Z "num_unrollings": 64
	22	2018-11-27T10:09:42.921659795Z }
	23	2018-11-27T10:09:42.921662383Z
	24	2018-11-27T10:09:43.077707805Z tensor_file:/home/jovyan/work/data/poem/poem_ids.txt
	25	2018-11-27T10:09:43.077982531Z Loading dataset from /home/jovyan/work/data/poem/poem_ids.txt
	26	2018-11-27T10:09:43.388716364Z file maxSeqLen = 64
	27	2018-11-27T10:09:43.390863888Z Loaded /home/jovyan/work/data/poem/: training samples:65235 ,validationSamples:3837,testingSamples:7676
	28	2018-11-27T10:09:43.899007416Z tensor_file:/home/jovyan/work/data/poem/poem_ids.txt
	29	2018-11-27T10:09:43.89904835Z Loading dataset from /home/jovyan/work/data/poem/poem_ids.txt
	30	2018-11-27T10:09:44.240973562Z file maxSeqLen = 64
	31	2018-11-27T10:09:44.241007275Z Loaded /home/jovyan/work/data/poem/: training samples:65235 ,validationSamples:3837,testingSamples:7676
	32	2018-11-27T10:09:44.299236281Z tensor_file:/home/jovyan/work/data/poem/poem_ids.txt
	33	2018-11-27T10:09:44.299265026Z Loading dataset from /home/jovyan/work/data/poem/poem_ids.txt
	34	2018-11-27T10:09:44.770941043Z file maxSeqLen = 64
	35	2018-11-27T10:09:44.770973167Z Loaded /home/jovyan/work/data/poem/: training samples:65235 ,validationSamples:3837,testingSamples:7676
	36	2018-11-27T10:09:44.791900509Z 06:09:44 INFO:Creating graph
	37	2018-11-27T10:09:55.183177953Z 06:09:55 INFO:Start training
	38	2018-11-27T10:09:55.183218136Z
	39	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
	40	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
	41	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:
	42	2018-11-27T10:09:55.375461183Z name: Tesla P100-PCIE-16GB major: 6 minor: 0 memoryClockRate(GHz): 1.3285
	43	2018-11-27T10:09:55.37546483Z pciBusID: 0000:00:07.0
	44	2018-11-27T10:09:55.375467846Z totalMemory: 15.90GiB freeMemory: 15.61GiB
	45	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
	46	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:
	47	2018-11-27T10:09:56.360894668Z 2018-11-27 18:09:56.360088: I tensorflow/core/common_runtime/gpu/gpu_device.cc:988] 0
	48	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
	49	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)
	50	2018-11-27T10:10:01.217951495Z 06:10:01 INFO:=================== Epoch 0 ===================
	51	2018-11-27T10:10:01.217992526Z
	52	2018-11-27T10:10:01.217997421Z 06:10:01 INFO:Training on training set
	53	2018-11-27T10:10:11.032344661Z 06:10:11 INFO:2.5%, step:99, perplexity: 579.997, speed: 10555 words
	54	2018-11-27T10:10:15.540573754Z 06:10:15 INFO:4.9%, step:199, perplexity: 365.581, speed: 14412 words
	55	2018-11-27T10:10:19.868017238Z 06:10:19 INFO:7.4%, step:299, perplexity: 280.805, speed: 16569 words
	56	2018-11-27T10:10:24.319889409Z 06:10:24 INFO:9.8%, step:399, perplexity: 234.879, speed: 17815 words
	57	2018-11-27T10:10:28.914214196Z 06:10:28 INFO:12.3%, step:499, perplexity: 205.698, speed: 18560 words
	58	2018-11-27T10:10:33.221910427Z 06:10:33 INFO:14.7%, step:599, perplexity: 184.924, speed: 19264 words
	59	2018-11-27T10:10:37.499999909Z 06:10:37 INFO:17.2%, step:699, perplexity: 169.067, speed: 19817 words
	60	2018-11-27T10:10:41.782939554Z 06:10:41 INFO:19.6%, step:799, perplexity: 156.408, speed: 20250 words
	61	2018-11-27T10:10:46.086558656Z 06:10:46 INFO:22.1%, step:899, perplexity: 146.158, speed: 20591 words
	62	2018-11-27T10:10:50.391412226Z 06:10:50 INFO:24.5%, step:999, perplexity: 137.768, speed: 20871 words
	63	2018-11-27T10:10:54.836464541Z 06:10:54 INFO:27.0%, step:1099, perplexity: 130.760, speed: 21052 words
	64	2018-11-27T10:10:59.273939872Z 06:10:59 INFO:29.4%, step:1199, perplexity: 124.744, speed: 21206 words
	65	2018-11-27T10:11:03.674085815Z 06:11:03 INFO:31.9%, step:1299, perplexity: 119.542, speed: 21352 words
	66	2018-11-27T10:11:08.11125808Z 06:11:08 INFO:34.3%, step:1399, perplexity: 114.989, speed: 21466 words
	67	2018-11-27T10:11:12.379560338Z 06:11:12 INFO:36.8%, step:1499, perplexity: 110.970, speed: 21618 words
	68	2018-11-27T10:11:16.762358631Z 06:11:16 INFO:39.2%, step:1599, perplexity: 107.408, speed: 21720 words
	69	2018-11-27T10:11:21.049873647Z 06:11:21 INFO:41.7%, step:1699, perplexity: 104.224, speed: 21836 words
	70	2018-11-27T10:11:25.449693668Z 06:11:25 INFO:44.1%, step:1799, perplexity: 101.359, speed: 21911 words
	71	2018-11-27T10:11:29.832817937Z 06:11:29 INFO:46.6%, step:1899, perplexity: 98.749, speed: 21983 words
	72	2018-11-27T10:11:34.45785477Z 06:11:34 INFO:49.0%, step:1999, perplexity: 96.351, speed: 21991 words
	73	2018-11-27T10:11:38.701768996Z 06:11:38 INFO:51.5%, step:2099, perplexity: 94.168, speed: 22084 words
	74	2018-11-27T10:11:42.84874719Z 06:11:42 INFO:53.9%, step:2199, perplexity: 92.176, speed: 22190 words
	75	2018-11-27T10:11:47.237563225Z 06:11:47 INFO:56.4%, step:2299, perplexity: 90.341, speed: 22238 words
	76	2018-11-27T10:11:51.334678139Z 06:11:51 INFO:58.9%, step:2399, perplexity: 88.632, speed: 22340 words
	77	2018-11-27T10:11:55.887346818Z 06:11:55 INFO:61.3%, step:2499, perplexity: 87.042, speed: 22347 words
	78	2018-11-27T10:12:00.221511695Z 06:12:00 INFO:63.8%, step:2599, perplexity: 85.542, speed: 22393 words
	79	2018-11-27T10:12:04.577282729Z 06:12:04 INFO:66.2%, step:2699, perplexity: 84.125, speed: 22433 words
	80	2018-11-27T10:12:09.192723567Z 06:12:09 INFO:68.7%, step:2799, perplexity: 82.786, speed: 22424 words
	81	2018-11-27T10:12:13.340539137Z 06:12:13 INFO:71.1%, step:2899, perplexity: 81.517, speed: 22495 words
	82	2018-11-27T10:12:17.551625495Z 06:12:17 INFO:73.6%, step:2999, perplexity: 80.310, speed: 22551 words
	83	2018-11-27T10:12:21.821009645Z 06:12:21 INFO:76.0%, step:3099, perplexity: 79.170, speed: 22595 words
	84	2018-11-27T10:12:26.07197127Z 06:12:26 INFO:78.5%, step:3199, perplexity: 78.088, speed: 22638 words
	85	2018-11-27T10:12:30.401157218Z 06:12:30 INFO:80.9%, step:3299, perplexity: 77.054, speed: 22668 words
	86	2018-11-27T10:12:34.649919317Z 06:12:34 INFO:83.4%, step:3399, perplexity: 76.065, speed: 22708 words
	87	2018-11-27T10:12:39.034718148Z 06:12:39 INFO:85.8%, step:3499, perplexity: 75.120, speed: 22726 words
	88	2018-11-27T10:12:43.378686867Z 06:12:43 INFO:88.3%, step:3599, perplexity: 74.220, speed: 22749 words
	89	2018-11-27T10:12:47.9802102Z 06:12:47 INFO:90.7%, step:3699, perplexity: 73.360, speed: 22735 words
	90	2018-11-27T10:12:52.460013383Z 06:12:52 INFO:93.2%, step:3799, perplexity: 72.535, speed: 22738 words
	91	2018-11-27T10:12:56.782595052Z 06:12:56 INFO:95.6%, step:3899, perplexity: 71.743, speed: 22761 words
	92	2018-11-27T10:13:01.210315439Z 06:13:01 INFO:98.1%, step:3999, perplexity: 70.985, speed: 22771 words
	93	2018-11-27T10:13:04.515673325Z 06:13:04 INFO:Perplexity: 41.065, speed: 22795 words per sec
	94	2018-11-27T10:13:06.383265041Z 06:13:06 INFO:Latest model saved in /home/jovyan/work/results/output_poem/save_model/model-4078
	95	2018-11-27T10:13:06.38335135Z
	96	2018-11-27T10:13:06.383361623Z 06:13:06 INFO:Evaluate on validation set
	97	2018-11-27T10:13:10.150923351Z 06:13:10 INFO:41.7%, step:99, perplexity: 27.212, speed: 31566 words
	98	2018-11-27T10:13:12.141522115Z 06:13:12 INFO:83.3%, step:199, perplexity: 27.008, speed: 39128 words
	99	2018-11-27T10:13:12.852132512Z 06:13:12 INFO:Perplexity: 27.770, speed: 41338 words per sec
	100	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
	101	2018-11-27T10:13:14.134886734Z 06:13:14 INFO:Best validation ppl is 27.769833
	102	2018-11-27T10:13:14.134891404Z
	103	2018-11-27T10:13:14.136491627Z 06:13:14 INFO:=================== Epoch 1 ===================
	104	2018-11-27T10:13:14.136507328Z
	105	2018-11-27T10:13:14.136512125Z 06:13:14 INFO:Training on training set
	106	2018-11-27T10:13:18.37093865Z 06:13:18 INFO:2.5%, step:99, perplexity: 31.367, speed: 24195 words
	107	2018-11-27T10:13:22.67200999Z 06:13:22 INFO:4.9%, step:199, perplexity: 30.462, speed: 23999 words
	108	2018-11-27T10:13:27.100723019Z 06:13:27 INFO:7.4%, step:299, perplexity: 30.253, speed: 23701 words
	109	2018-11-27T10:13:31.506242791Z 06:13:31 INFO:9.8%, step:399, perplexity: 30.191, speed: 23585 words
	110	2018-11-27T10:13:35.754251214Z 06:13:35 INFO:12.3%, step:499, perplexity: 30.156, speed: 23686 words
	111	2018-11-27T10:13:40.123424997Z 06:13:40 INFO:14.7%, step:599, perplexity: 30.072, speed: 23644 words
	112	2018-11-27T10:13:44.805115792Z 06:13:44 INFO:17.2%, step:699, perplexity: 29.913, speed: 23374 words
	113	2018-11-27T10:13:49.109015968Z 06:13:49 INFO:19.6%, step:799, perplexity: 29.684, speed: 23425 words
	114	2018-11-27T10:13:53.444719904Z 06:13:53 INFO:22.1%, step:899, perplexity: 29.479, speed: 23447 words
	115	2018-11-27T10:13:57.70057894Z 06:13:57 INFO:24.5%, step:999, perplexity: 29.336, speed: 23507 words
	116	2018-11-27T10:14:02.035195139Z 06:14:02 INFO:27.0%, step:1099, perplexity: 29.224, speed: 23517 words
	117	2018-11-27T10:14:06.513941916Z 06:14:06 INFO:29.4%, step:1199, perplexity: 29.109, speed: 23461 words
	118	2018-11-27T10:14:10.843937135Z 06:14:10 INFO:31.9%, step:1299, perplexity: 29.007, speed: 23476 words
	119	2018-11-27T10:14:15.100460237Z 06:14:15 INFO:34.3%, step:1399, perplexity: 28.916, speed: 23517 words
	120	2018-11-27T10:14:19.328567505Z 06:14:19 INFO:36.8%, step:1499, perplexity: 28.837, speed: 23562 words
	121	2018-11-27T10:14:23.602516105Z 06:14:23 INFO:39.2%, step:1599, perplexity: 28.775, speed: 23587 words
	122	2018-11-27T10:14:27.891736465Z 06:14:27 INFO:41.7%, step:1699, perplexity: 28.724, speed: 23603 words
	123	2018-11-27T10:14:32.264798343Z 06:14:32 INFO:44.1%, step:1799, perplexity: 28.683, speed: 23593 words
	124	2018-11-27T10:14:36.751389471Z 06:14:36 INFO:46.6%, step:1899, perplexity: 28.640, speed: 23551 words
	125	2018-11-27T10:14:40.963914224Z 06:14:40 INFO:49.0%, step:1999, perplexity: 28.594, speed: 23588 words
	126	2018-11-27T10:14:45.299287048Z 06:14:45 INFO:51.5%, step:2099, perplexity: 28.562, speed: 23589 words
	127	2018-11-27T10:14:49.698047951Z 06:14:49 INFO:53.9%, step:2199, perplexity: 28.542, speed: 23575 words
	128	2018-11-27T10:14:54.334874276Z 06:14:54 INFO:56.4%, step:2299, perplexity: 28.526, speed: 23507 words
	129	2018-11-27T10:14:58.855924359Z 06:14:58 INFO:58.9%, step:2399, perplexity: 28.509, speed: 23469 words
	130	2018-11-27T10:15:03.431963114Z 06:15:03 INFO:61.3%, step:2499, perplexity: 28.493, speed: 23423 words
	131	2018-11-27T10:15:07.994242671Z 06:15:07 INFO:63.8%, step:2599, perplexity: 28.469, speed: 23384 words
	132	2018-11-27T10:15:12.421086797Z 06:15:12 INFO:66.2%, step:2699, perplexity: 28.439, speed: 23375 words
	133	2018-11-27T10:15:16.630874054Z 06:15:16 INFO:68.7%, step:2799, perplexity: 28.404, speed: 23407 words
	134	2018-11-27T10:15:20.951036643Z 06:15:20 INFO:71.1%, step:2899, perplexity: 28.367, speed: 23417 words
	135	2018-11-27T10:15:25.27899812Z 06:15:25 INFO:73.6%, step:2999, perplexity: 28.325, speed: 23425 words
	136	2018-11-27T10:15:29.775674058Z 06:15:29 INFO:76.0%, step:3099, perplexity: 28.286, speed: 23404 words
	137	2018-11-27T10:15:34.126985091Z 06:15:34 INFO:78.5%, step:3199, perplexity: 28.248, speed: 23408 words
	138	2018-11-27T10:15:38.464845561Z 06:15:38 INFO:80.9%, step:3299, perplexity: 28.207, speed: 23415 words
	139	2018-11-27T10:15:42.990394552Z 06:15:42 INFO:83.4%, step:3399, perplexity: 28.165, speed: 23390 words
	140	2018-11-27T10:15:47.425927234Z 06:15:47 INFO:85.8%, step:3499, perplexity: 28.123, speed: 23381 words
	141	2018-11-27T10:15:51.87598534Z 06:15:51 INFO:88.3%, step:3599, perplexity: 28.083, speed: 23371 words
	142	2018-11-27T10:15:56.338438287Z 06:15:56 INFO:90.7%, step:3699, perplexity: 28.046, speed: 23359 words
	143	2018-11-27T10:16:00.900162713Z 06:16:00 INFO:93.2%, step:3799, perplexity: 28.009, speed: 23334 words
	144	2018-11-27T10:16:05.255897951Z 06:16:05 INFO:95.6%, step:3899, perplexity: 27.972, speed: 23339 words
	145	2018-11-27T10:16:09.663934234Z 06:16:09 INFO:98.1%, step:3999, perplexity: 27.937, speed: 23336 words
	146	2018-11-27T10:16:13.21823541Z 06:16:13 INFO:Perplexity: 26.581, speed: 23319 words per sec
	147	2018-11-27T10:16:14.410917929Z 06:16:14 INFO:Latest model saved in /home/jovyan/work/results/output_poem/save_model/model-8156
	148	2018-11-27T10:16:14.410963935Z
	149	2018-11-27T10:16:14.410971269Z 06:16:14 INFO:Evaluate on validation set
	150	2018-11-27T10:16:17.491999462Z 06:16:17 INFO:41.7%, step:99, perplexity: 23.190, speed: 37610 words
	151	2018-11-27T10:16:19.430069711Z 06:16:19 INFO:83.3%, step:199, perplexity: 23.023, speed: 43960 words
	152	2018-11-27T10:16:20.172559347Z 06:16:20 INFO:Perplexity: 23.695, speed: 45476 words per sec
	153	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
	154	2018-11-27T10:16:21.328933688Z 06:16:21 INFO:Best validation ppl is 23.695059
	155	2018-11-27T10:16:21.328941622Z
	156	2018-11-27T10:16:21.331146965Z 06:16:21 INFO:=================== Epoch 2 ===================
	157	2018-11-27T10:16:21.331195602Z
	158	2018-11-27T10:16:21.33120213Z 06:16:21 INFO:Training on training set
	159	2018-11-27T10:16:25.822634972Z 06:16:25 INFO:2.5%, step:99, perplexity: 26.108, speed: 22809 words
	160	2018-11-27T10:16:30.154094196Z 06:16:30 INFO:4.9%, step:199, perplexity: 25.357, speed: 23218 words
	161	2018-11-27T10:16:34.519838458Z 06:16:34 INFO:7.4%, step:299, perplexity: 25.189, speed: 23296 words
	162	2018-11-27T10:16:38.863471994Z 06:16:38 INFO:9.8%, step:399, perplexity: 25.155, speed: 23365 words
	163	2018-11-27T10:16:43.276922221Z 06:16:43 INFO:12.3%, step:499, perplexity: 25.147, speed: 23338 words
	164	2018-11-27T10:16:47.667144852Z 06:16:47 INFO:14.7%, step:599, perplexity: 25.098, speed: 23331 words
	165	2018-11-27T10:16:51.989385556Z 06:16:51 INFO:17.2%, step:699, perplexity: 24.986, speed: 23383 words
	166	2018-11-27T10:16:56.648716698Z 06:16:56 INFO:19.6%, step:799, perplexity: 24.814, speed: 23197 words
	167	2018-11-27T10:17:00.896572508Z 06:17:00 INFO:22.1%, step:899, perplexity: 24.662, speed: 23294 words
	168	2018-11-27T10:17:05.339943441Z 06:17:05 INFO:24.5%, step:999, perplexity: 24.559, speed: 23270 words
	169	2018-11-27T10:17:09.829883397Z 06:17:09 INFO:27.0%, step:1099, perplexity: 24.483, speed: 23227 words
	170	2018-11-27T10:17:14.132195825Z 06:17:14 INFO:29.4%, step:1199, perplexity: 24.404, speed: 23274 words
	171	2018-11-27T10:17:18.893229243Z 06:17:18 INFO:31.9%, step:1299, perplexity: 24.337, speed: 23127 words
	172	2018-11-27T10:17:23.198893748Z 06:17:23 INFO:34.3%, step:1399, perplexity: 24.279, speed: 23173 words
	173	2018-11-27T10:17:27.691213117Z 06:17:27 INFO:36.8%, step:1499, perplexity: 24.230, speed: 23147 words
	174	2018-11-27T10:17:32.023904568Z 06:17:32 INFO:39.2%, step:1599, perplexity: 24.197, speed: 23177 words
	175	2018-11-27T10:17:36.622163391Z 06:17:36 INFO:41.7%, step:1699, perplexity: 24.174, speed: 23122 words
	176	2018-11-27T10:17:40.874052641Z 06:17:40 INFO:44.1%, step:1799, perplexity: 24.158, speed: 23173 words
	177	2018-11-27T10:17:45.210869425Z 06:17:45 INFO:46.6%, step:1899, perplexity: 24.142, speed: 23196 words
	178	2018-11-27T10:17:49.66595633Z 06:17:49 INFO:49.0%, step:1999, perplexity: 24.121, speed: 23185 words
	179	2018-11-27T10:17:54.114666252Z 06:17:54 INFO:51.5%, step:2099, perplexity: 24.112, speed: 23177 words
	180	2018-11-27T10:17:58.506224862Z 06:17:58 INFO:53.9%, step:2199, perplexity: 24.113, speed: 23183 words
	181	2018-11-27T10:18:02.778089366Z 06:18:02 INFO:56.4%, step:2299, perplexity: 24.118, speed: 23217 words
	182	2018-11-27T10:18:07.230983119Z 06:18:07 INFO:58.9%, step:2399, perplexity: 24.121, speed: 23209 words
	183	2018-11-27T10:18:12.027530948Z 06:18:12 INFO:61.3%, step:2499, perplexity: 24.125, speed: 23127 words
	184	2018-11-27T10:18:16.407152069Z 06:18:16 INFO:63.8%, step:2599, perplexity: 24.122, speed: 23137 words
	185	2018-11-27T10:18:20.835552933Z 06:18:20 INFO:66.2%, step:2699, perplexity: 24.114, speed: 23136 words
	186	2018-11-27T10:18:25.445355239Z 06:18:25 INFO:68.7%, step:2799, perplexity: 24.103, speed: 23102 words
	187	2018-11-27T10:18:29.688021836Z 06:18:29 INFO:71.1%, step:2899, perplexity: 24.088, speed: 23136 words
	188	2018-11-27T10:18:33.953928626Z 06:18:33 INFO:73.6%, step:2999, perplexity: 24.070, speed: 23164 words
	189	2018-11-27T10:18:38.383252436Z 06:18:38 INFO:76.0%, step:3099, perplexity: 24.054, speed: 23163 words
	190	2018-11-27T10:18:42.893777871Z 06:18:42 INFO:78.5%, step:3199, perplexity: 24.038, speed: 23149 words
	191	2018-11-27T10:18:47.31285914Z 06:18:47 INFO:80.9%, step:3299, perplexity: 24.020, speed: 23149 words
	192	2018-11-27T10:18:51.576226913Z 06:18:51 INFO:83.4%, step:3399, perplexity: 24.001, speed: 23173 words
	193	2018-11-27T10:18:55.943989507Z 06:18:55 INFO:85.8%, step:3499, perplexity: 23.981, speed: 23181 words
	194	2018-11-27T10:19:00.411905354Z 06:19:00 INFO:88.3%, step:3599, perplexity: 23.964, speed: 23174 words
	195	2018-11-27T10:19:04.863901015Z 06:19:04 INFO:90.7%, step:3699, perplexity: 23.948, speed: 23169 words
	196	2018-11-27T10:19:09.275138043Z 06:19:09 INFO:93.2%, step:3799, perplexity: 23.932, speed: 23170 words
	197	2018-11-27T10:19:13.679228985Z 06:19:13 INFO:95.6%, step:3899, perplexity: 23.917, speed: 23172 words
	198	2018-11-27T10:19:18.070953998Z 06:19:18 INFO:98.1%, step:3999, perplexity: 23.902, speed: 23176 words
	199	2018-11-27T10:19:21.512945327Z 06:19:21 INFO:Perplexity: 23.384, speed: 23176 words per sec
	200	2018-11-27T10:19:22.751208491Z 06:19:22 INFO:Latest model saved in /home/jovyan/work/results/output_poem/save_model/model-12234
	201	2018-11-27T10:19:22.751259163Z
	202	2018-11-27T10:19:22.751265813Z 06:19:22 INFO:Evaluate on validation set
	203	2018-11-27T10:19:26.027680038Z 06:19:26 INFO:41.7%, step:99, perplexity: 22.205, speed: 35042 words
	204	2018-11-27T10:19:27.900094306Z 06:19:27 INFO:83.3%, step:199, perplexity: 22.039, speed: 42714 words
	205	2018-11-27T10:19:28.716925585Z 06:19:28 INFO:Perplexity: 22.719, speed: 43791 words per sec
	206	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
	207	2018-11-27T10:19:30.073333517Z 06:19:30 INFO:Best validation ppl is 22.719276
	208	2018-11-27T10:19:30.073345188Z
	209	2018-11-27T10:19:30.07483804Z 06:19:30 INFO:=================== Epoch 3 ===================
	210	2018-11-27T10:19:30.074854818Z
	211	2018-11-27T10:19:30.075604575Z 06:19:30 INFO:Training on training set
	212	2018-11-27T10:19:34.542630855Z 06:19:34 INFO:2.5%, step:99, perplexity: 23.989, speed: 22953 words
	213	2018-11-27T10:19:38.991967833Z 06:19:38 INFO:4.9%, step:199, perplexity: 23.323, speed: 22973 words
	214	2018-11-27T10:19:43.373945257Z 06:19:43 INFO:7.4%, step:299, perplexity: 23.167, speed: 23105 words
	215	2018-11-27T10:19:47.800022625Z 06:19:47 INFO:9.8%, step:399, perplexity: 23.135, speed: 23111 words
	216	2018-11-27T10:19:52.116562199Z 06:19:52 INFO:12.3%, step:499, perplexity: 23.130, speed: 23231 words
	217	2018-11-27T10:19:56.633976252Z 06:19:56 INFO:14.7%, step:599, perplexity: 23.090, speed: 23136 words
	218	2018-11-27T10:20:00.949289863Z 06:20:00 INFO:17.2%, step:699, perplexity: 22.991, speed: 23219 words
	219	2018-11-27T10:20:05.290965539Z 06:20:05 INFO:19.6%, step:799, perplexity: 22.838, speed: 23264 words
	220	2018-11-27T10:20:09.612679003Z 06:20:09 INFO:22.1%, step:899, perplexity: 22.705, speed: 23311 words
	221	2018-11-27T10:20:13.943285394Z 06:20:13 INFO:24.5%, step:999, perplexity: 22.619, speed: 23344 words
	222	2018-11-27T10:20:18.344987697Z 06:20:18 INFO:27.0%, step:1099, perplexity: 22.557, speed: 23338 words
	223	2018-11-27T10:20:22.570522059Z 06:20:22 INFO:29.4%, step:1199, perplexity: 22.492, speed: 23409 words
	224	2018-11-27T10:20:26.901527245Z 06:20:26 INFO:31.9%, step:1299, perplexity: 22.437, speed: 23427 words
	225	2018-11-27T10:20:31.059737324Z 06:20:31 INFO:34.3%, step:1399, perplexity: 22.391, speed: 23509 words
	226	2018-11-27T10:20:35.343045386Z 06:20:35 INFO:36.8%, step:1499, perplexity: 22.354, speed: 23539 words
	227	2018-11-27T10:20:40.22345844Z 06:20:40 INFO:39.2%, step:1599, perplexity: 22.329, speed: 23357 words
	228	2018-11-27T10:20:44.557509946Z 06:20:44 INFO:41.7%, step:1699, perplexity: 22.314, speed: 23373 words
	229	2018-11-27T10:20:48.708852989Z 06:20:48 INFO:44.1%, step:1799, perplexity: 22.306, speed: 23441 words
	230	2018-11-27T10:20:53.022344152Z 06:20:53 INFO:46.6%, step:1899, perplexity: 22.297, speed: 23456 words
	231	2018-11-27T10:20:57.37546736Z 06:20:57 INFO:49.0%, step:1999, perplexity: 22.285, speed: 23460 words
	232	2018-11-27T10:21:01.833109767Z 06:21:01 INFO:51.5%, step:2099, perplexity: 22.282, speed: 23436 words
	233	2018-11-27T10:21:06.151597571Z 06:21:06 INFO:53.9%, step:2199, perplexity: 22.289, speed: 23449 words
	234	2018-11-27T10:21:10.828731892Z 06:21:10 INFO:56.4%, step:2299, perplexity: 22.299, speed: 23376 words
	235	2018-11-27T10:21:15.126494472Z 06:21:15 INFO:58.9%, step:2399, perplexity: 22.308, speed: 23395 words
	236	2018-11-27T10:21:19.461160547Z 06:21:19 INFO:61.3%, step:2499, perplexity: 22.317, speed: 23404 words
	237	2018-11-27T10:21:23.793901164Z 06:21:23 INFO:63.8%, step:2599, perplexity: 22.320, speed: 23413 words
	238	2018-11-27T10:21:27.997158767Z 06:21:27 INFO:66.2%, step:2699, perplexity: 22.319, speed: 23447 words
	239	2018-11-27T10:21:32.29960135Z 06:21:32 INFO:68.7%, step:2799, perplexity: 22.314, speed: 23459 words
	240	2018-11-27T10:21:36.655561044Z 06:21:36 INFO:71.1%, step:2899, perplexity: 22.306, speed: 23461 words
	241	2018-11-27T10:21:41.143006311Z 06:21:41 INFO:73.6%, step:2999, perplexity: 22.295, speed: 23439 words
	242	2018-11-27T10:21:45.557130333Z 06:21:45 INFO:76.0%, step:3099, perplexity: 22.286, speed: 23431 words
	243	2018-11-27T10:21:49.887959316Z 06:21:49 INFO:78.5%, step:3199, perplexity: 22.277, speed: 23437 words
	244	2018-11-27T10:21:54.829782389Z 06:21:54 INFO:80.9%, step:3299, perplexity: 22.266, speed: 23345 words
	245	2018-11-27T10:21:59.329112412Z 06:21:59 INFO:83.4%, step:3399, perplexity: 22.254, speed: 23327 words
	246	2018-11-27T10:22:04.015500912Z 06:22:04 INFO:85.8%, step:3499, perplexity: 22.241, speed: 23282 words
	247	2018-11-27T10:22:08.357942505Z 06:22:08 INFO:88.3%, step:3599, perplexity: 22.231, speed: 23290 words
	248	2018-11-27T10:22:13.066890435Z 06:22:13 INFO:90.7%, step:3699, perplexity: 22.222, speed: 23246 words
	249	2018-11-27T10:22:17.645017158Z 06:22:17 INFO:93.2%, step:3799, perplexity: 22.212, speed: 23222 words
	250	2018-11-27T10:22:22.107816071Z 06:22:22 INFO:95.6%, step:3899, perplexity: 22.204, speed: 23215 words
	251	2018-11-27T10:22:26.560310365Z 06:22:26 INFO:98.1%, step:3999, perplexity: 22.196, speed: 23209 words
	252	2018-11-27T10:22:29.903176124Z 06:22:29 INFO:Perplexity: 21.945, speed: 23222 words per sec
	253	2018-11-27T10:22:31.075931565Z 06:22:31 INFO:Latest model saved in /home/jovyan/work/results/output_poem/save_model/model-16312
	254	2018-11-27T10:22:31.075967919Z
	255	2018-11-27T10:22:31.075972504Z 06:22:31 INFO:Evaluate on validation set
	256	2018-11-27T10:22:34.245962411Z 06:22:34 INFO:41.7%, step:99, perplexity: 22.034, speed: 36666 words
	257	2018-11-27T10:22:36.0676778Z 06:22:36 INFO:83.3%, step:199, perplexity: 21.854, speed: 44384 words
	258	2018-11-27T10:22:36.77789724Z 06:22:36 INFO:Perplexity: 22.599, speed: 46162 words per sec
	259	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
	260	2018-11-27T10:22:37.860691125Z 06:22:37 INFO:Best validation ppl is 22.599243
	261	2018-11-27T10:22:37.860699352Z
	262	2018-11-27T10:22:37.862282085Z 06:22:37 INFO:=================== Epoch 4 ===================
	263	2018-11-27T10:22:37.862332248Z
	264	2018-11-27T10:22:37.862339865Z 06:22:37 INFO:Training on training set
	265	2018-11-27T10:22:42.37426273Z 06:22:42 INFO:2.5%, step:99, perplexity: 23.107, speed: 22705 words
	266	2018-11-27T10:22:46.680374852Z 06:22:46 INFO:4.9%, step:199, perplexity: 22.428, speed: 23232 words
	267	2018-11-27T10:22:51.03296342Z 06:22:51 INFO:7.4%, step:299, perplexity: 22.259, speed: 23334 words
	268	2018-11-27T10:22:55.426832231Z 06:22:55 INFO:9.8%, step:399, perplexity: 22.217, speed: 23327 words
	269	2018-11-27T10:22:59.777773075Z 06:22:59 INFO:12.3%, step:499, perplexity: 22.202, speed: 23365 words
	270	2018-11-27T10:23:04.145072125Z 06:23:04 INFO:14.7%, step:599, perplexity: 22.157, speed: 23378 words
	271	2018-11-27T10:23:09.1136806Z 06:23:09 INFO:17.2%, step:699, perplexity: 22.057, speed: 22977 words
	272	2018-11-27T10:23:13.648830834Z 06:23:13 INFO:19.6%, step:799, perplexity: 21.906, speed: 22893 words
	273	2018-11-27T10:23:18.025400022Z 06:23:18 INFO:22.1%, step:899, perplexity: 21.775, speed: 22948 words
	274	2018-11-27T10:23:22.348901926Z 06:23:22 INFO:24.5%, step:999, perplexity: 21.688, speed: 23019 words
	275	2018-11-27T10:23:26.676677066Z 06:23:26 INFO:27.0%, step:1099, perplexity: 21.626, speed: 23076 words
	276	2018-11-27T10:23:30.951963306Z 06:23:30 INFO:29.4%, step:1199, perplexity: 21.561, speed: 23146 words
	277	2018-11-27T10:23:35.232935959Z 06:23:35 INFO:31.9%, step:1299, perplexity: 21.507, speed: 23204 words
	278	2018-11-27T10:23:39.603813529Z 06:23:39 INFO:34.3%, step:1399, perplexity: 21.461, speed: 23221 words
	279	2018-11-27T10:23:44.017493496Z 06:23:44 INFO:36.8%, step:1499, perplexity: 21.424, speed: 23219 words
	280	2018-11-27T10:23:48.326467059Z 06:23:48 INFO:39.2%, step:1599, perplexity: 21.399, speed: 23252 words
	281	2018-11-27T10:23:52.585672907Z 06:23:52 INFO:41.7%, step:1699, perplexity: 21.384, speed: 23298 words
	282	2018-11-27T10:23:56.989893971Z 06:23:56 INFO:44.1%, step:1799, perplexity: 21.376, speed: 23295 words
	283	2018-11-27T10:24:01.393023201Z 06:24:01 INFO:46.6%, step:1899, perplexity: 21.367, speed: 23293 words
	284	2018-11-27T10:24:05.58477811Z 06:24:05 INFO:49.0%, step:1999, perplexity: 21.356, speed: 23347 words
	285	2018-11-27T10:24:09.947020057Z 06:24:09 INFO:51.5%, step:2099, perplexity: 21.354, speed: 23353 words
	286	2018-11-27T10:24:14.412994293Z 06:24:14 INFO:53.9%, step:2199, perplexity: 21.361, speed: 23333 words
	287	2018-11-27T10:24:18.954771216Z 06:24:18 INFO:56.4%, step:2299, perplexity: 21.371, speed: 23298 words
	288	2018-11-27T10:24:23.433249285Z 06:24:23 INFO:58.9%, step:2399, perplexity: 21.380, speed: 23280 words
	289	2018-11-27T10:24:27.670924005Z 06:24:27 INFO:61.3%, step:2499, perplexity: 21.389, speed: 23314 words
	290	2018-11-27T10:24:32.040370971Z 06:24:32 INFO:63.8%, step:2599, perplexity: 21.393, speed: 23318 words
	291	2018-11-27T10:24:36.300870634Z 06:24:36 INFO:66.2%, step:2699, perplexity: 21.393, speed: 23344 words
	292	2018-11-27T10:24:40.600139039Z 06:24:40 INFO:68.7%, step:2799, perplexity: 21.389, speed: 23361 words
	293	2018-11-27T10:24:45.049495157Z 06:24:45 INFO:71.1%, step:2899, perplexity: 21.383, speed: 23349 words
	294	2018-11-27T10:24:49.439408318Z 06:24:49 INFO:73.6%, step:2999, perplexity: 21.374, speed: 23348 words
	295	2018-11-27T10:24:53.802195957Z 06:24:53 INFO:76.0%, step:3099, perplexity: 21.366, speed: 23353 words
	296	2018-11-27T10:24:58.262550854Z 06:24:58 INFO:78.5%, step:3199, perplexity: 21.359, speed: 23339 words
	297	2018-11-27T10:25:02.636673549Z 06:25:02 INFO:80.9%, step:3299, perplexity: 21.350, speed: 23342 words
	298	2018-11-27T10:25:06.982697089Z 06:25:06 INFO:83.4%, step:3399, perplexity: 21.340, speed: 23348 words
	299	2018-11-27T10:25:11.487683521Z 06:25:11 INFO:85.8%, step:3499, perplexity: 21.329, speed: 23330 words
	300	2018-11-27T10:25:15.850716369Z 06:25:15 INFO:88.3%, step:3599, perplexity: 21.321, speed: 23334 words
	301	2018-11-27T10:25:20.217657971Z 06:25:20 INFO:90.7%, step:3699, perplexity: 21.314, speed: 23337 words
	302	2018-11-27T10:25:24.900127389Z 06:25:24 INFO:93.2%, step:3799, perplexity: 21.307, speed: 23296 words
	303	2018-11-27T10:25:29.231327973Z 06:25:29 INFO:95.6%, step:3899, perplexity: 21.300, speed: 23304 words
	304	2018-11-27T10:25:33.590834675Z 06:25:33 INFO:98.1%, step:3999, perplexity: 21.294, speed: 23309 words
	305	2018-11-27T10:25:36.874541841Z 06:25:36 INFO:Perplexity: 21.121, speed: 23327 words per sec
	306	2018-11-27T10:25:37.997983144Z 06:25:37 INFO:Latest model saved in /home/jovyan/work/results/output_poem/save_model/model-20390
	307	2018-11-27T10:25:37.998028676Z
	308	2018-11-27T10:25:37.99803331Z 06:25:37 INFO:Evaluate on validation set
	309	2018-11-27T10:25:41.034447802Z 06:25:41 INFO:41.7%, step:99, perplexity: 21.705, speed: 38149 words
	310	2018-11-27T10:25:42.95159539Z 06:25:42 INFO:83.3%, step:199, perplexity: 21.544, speed: 44505 words
	311	2018-11-27T10:25:43.688599759Z 06:25:43 INFO:Perplexity: 22.314, speed: 46031 words per sec
	312	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
	313	2018-11-27T10:25:44.848221175Z 06:25:44 INFO:Best validation ppl is 22.313692
	314	2018-11-27T10:25:44.848228199Z
	315	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
	316	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
	317	2018-11-27T10:25:44.849375611Z 06:25:44 INFO:Best validation ppl is 22.313692
	318	2018-11-27T10:25:44.849378949Z
	319	2018-11-27T10:25:44.849382077Z 06:25:44 INFO:Evaluate the best model on test set
	320	2018-11-27T10:25:48.459385356Z 06:25:48 INFO:20.8%, step:99, perplexity: 25.941, speed: 34469 words
	321	2018-11-27T10:25:50.191089303Z 06:25:50 INFO:41.7%, step:199, perplexity: 27.051, speed: 43544 words
	322	2018-11-27T10:25:52.06291597Z 06:25:52 INFO:62.5%, step:299, perplexity: 27.151, speed: 46722 words
	323	2018-11-27T10:25:53.799448645Z 06:25:53 INFO:83.3%, step:399, perplexity: 27.173, speed: 49281 words
	324	2018-11-27T10:25:55.447932056Z 06:25:55 INFO:Perplexity: 27.004, speed: 49355 words per sec
	325	2018-11-27T10:25:56.815541581Z SYSTEM: Finishing...
	326	2018-11-27T10:25:57.032365081Z SYSTEM: Done!

-0

job_logs/job-gpu-5bfe24501afd942eee5c5020.log less more

484	484	2018-11-28T05:43:09.502860421Z 01:43:09 INFO:83.3%, step:399, perplexity: 25.702, speed: 46441 words
485	485	2018-11-28T05:43:11.366013365Z 01:43:11 INFO:Perplexity: 25.562, speed: 46007 words per sec
486	486	2018-11-28T05:43:12.680105735Z SYSTEM: Finishing...
	487	2018-11-28T05:43:12.909020997Z SYSTEM: Done!

-0

job_logs/job-gpu-5c00ae391afd942b83265f05.log less more

10	10	2018-11-30T03:28:33.43192371Z mkdir(name, mode)
11	11	2018-11-30T03:28:33.431928773Z FileExistsError: [Errno 17] File exists: '/home/jovyan/work/results/tb_results/tensorboard_log'
12	12	2018-11-30T03:28:33.993854401Z SYSTEM: Finishing...
	13	2018-11-30T03:28:34.319930803Z SYSTEM: Error Exists!

-0

job_logs/job-gpu-5c00bbe61afd942cd74d7c6e.log less more

10	10	2018-11-30T04:26:45.569333699Z mkdir(name, mode)
11	11	2018-11-30T04:26:45.569338847Z FileExistsError: [Errno 17] File exists: '/home/jovyan/work/results/tb_results/tensorboard_log'
12	12	2018-11-30T04:26:46.164187505Z SYSTEM: Finishing...
	13	2018-11-30T04:26:46.500529602Z SYSTEM: Error Exists!

-0

job_logs/job-gpu-5c00be431afd942b66b36bad.log less more

10	10	2018-11-30T04:36:51.276884231Z mkdir(name, mode)
11	11	2018-11-30T04:36:51.276888696Z FileExistsError: [Errno 17] File exists: '/home/jovyan/work/results/tb_results/tensorboard_log'
12	12	2018-11-30T04:36:51.975847222Z SYSTEM: Finishing...
	13	2018-11-30T04:36:52.34015789Z SYSTEM: Error Exists!

-0

job_logs/job-gpu-5c00c00d1afd942b54c88504.log less more

859	859	2018-11-30T05:59:38.355844634Z 01:59:38 INFO:83.3%, step:399, perplexity: 25.498, speed: 31709 words
860	860	2018-11-30T05:59:40.519902775Z 01:59:40 INFO:Perplexity: 25.508, speed: 32599 words per sec
861	861	2018-11-30T05:59:42.862057327Z SYSTEM: Finishing...
	862	2018-11-30T05:59:43.22784752Z SYSTEM: Done!

-47

main.ipynb less more

1	1	"cells": [
2	2	{
3	3	"cell_type": "code",
4		"execution_count": 1,
	4	"execution_count": null,
5	5	"metadata": {},
6		"outputs": [
7		{
8		"ename": "ImportError",
9		"evalue": "No module named 'modules'",
10		"output_type": "error",
11		"traceback": [
12		"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
13		"\u001b[0;31mImportError\u001b[0m Traceback (most recent call last)",
14		"\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",
15		"\u001b[0;31mImportError\u001b[0m: No module named 'modules'"
16		]
17		}
18		],
	6	"outputs": [],
19	7	"source": [
20	8	"# coding = utf8\n",
21	9	"# You can use other public modules via our Client object with module's identifier\n",

52	40	"execution_count": null,
53	41	"metadata": {},
54	42	"outputs": [],
55		"source": []
56		},
57		{
58		"cell_type": "code",
59		"execution_count": 2,
60		"metadata": {},
61		"outputs": [
62		{
63		"name": "stdout",
64		"output_type": "stream",
65		"text": [
66		"/home/jovyan/work\n",
67		"07:26:17 INFO:best_model: /home/jovyan/work/results/output_poem/best_model/model-20390\n",
68		"\n",
69		"result：\n",
70		"大妃非是列，志事满黄枢。始喜封阳礼，开摩舞罢歌。\n"
71		]
72		}
73		],
74	43	"source": [
75	44	"from write_poem import start_model\n",
76	45	"\n",

101	70	},
102	71	{
103	72	"cell_type": "code",
104		"execution_count": 2,
	73	"execution_count": null,
105	74	"metadata": {},
106		"outputs": [
107		{
108		"ename": "ImportError",
109		"evalue": "No module named 'modules'",
110		"output_type": "error",
111		"traceback": [
112		"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
113		"\u001b[0;31mImportError\u001b[0m Traceback (most recent call last)",
114		"\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",
115		"\u001b[0;31mImportError\u001b[0m: No module named 'modules'"
116		]
117		}
118		],
	75	"outputs": [],
119	76	"source": [
120	77	"# coding: utf-8\n",
121	78	"import os\n",

-40

~~main.py~~ less more

0		# coding: utf-8
1		import os
2		import sys
3
4		# Import necessary packages
5		from modules import json_parser
6		from modules import Client
7		from write_poem import start_model
8
9		# Initialise Client object
10		client = Client(api_key='33799e1c5d6fa05fdd7dec3aa7aad868445d1c737edcf9c37fa89cb3b39cb2d9',
11		project_id='5bfd118f1afd942b66b36b30', user_ID='yangsaisai',
12		project_type='app', source_file_path='main.ipynb',
13		silent=True)
14
15		# Make run/train/predict command alias for further use
16		run = client.run
17		train = client.train
18		predict = client.predict
19
20		# Make controller alias for further use
21		controller = client.controller
22		writer = start_model()
23
24
25		def handle(conf):
26		# paste your code here
27		if conf['style'] == '藏头诗':
28		poetry = writer.cangtou(conf['Chinese_word'])
29		elif conf['style'] == '藏字诗':
30		poetry = writer.hide_words(conf['Chinese_word'])
31		else:
32		poetry = writer.rhyme_verse()
33		return {"Poetry": poetry}
34		# return {"Poetry": 'asadd'}
35
36		# if __name__ == '__main__':
37		# conf = {"style": ['藏头诗'], "Chinese_word": '杨赛赛'}
38		# aa = handle(conf)
39		# print(aa)

+18

-0

results/output_poem/.ipynb_checkpoints/result-checkpoint.json less more

	0	{
	1	"best_model": "./results/output_poem/best_model/model-61170",
	2	"best_valid_ppl": 21.06464385986328,
	3	"latest_model": "./results/output_poem/save_model/model-61170",
	4	"params": {
	5	"batch_size": 16,
	6	"cell_type": "lstm",
	7	"dropout": 0.0,
	8	"embedding_size": 128,
	9	"hidden_size": 128,
	10	"input_dropout": 0.0,
	11	"learning_rate": 0.005,
	12	"max_grad_norm": 5.0,
	13	"num_layers": 2,
	14	"num_unrollings": 64
	15	},
	16	"test_ppl": 25.508337020874023
	17	}⏎

~~results/tb_results/tensorboard_log/1543547440/summaries/train/.nfs00000000011509b40000001c~~ less more

Binary diff not shown

~~results/tb_results/tensorboard_log/1543547440/summaries/valid/.nfs00000000011509b60000001d~~ less more

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-3

train.py less more

0		import codecs
1	0	import json
2	1	import logging
3	2	import os
4		import shutil
5	3	import sys
6	4	import time
7		import numpy as np
8	5	import tensorflow as tf
9	6	from char_rnn_model import CharRNNLM
10	7	from config_poem import config_poem_train

-1

write_poem.py less more

24	24
25	25	params = result['params']
26	26	best_model = result['best_model']
27		best_valid_ppl = result['best_valid_ppl']
	27	# best_valid_ppl = result['best_valid_ppl']
28	28	if 'encoding' in result:
29	29	self.args.encoding = result['encoding']
30	30	else:

215	215	writer = WritePoem(args)
216	216	return writer
217	217
	218
218	219	if __name__ == '__main__':
219	220	writer = start_model()