def __init__(self,args):
self.args = args
logging.basicConfig(stream=sys.stdout,
format='%(asctime)s %(levelname)s:%(message)s',
level=logging.INFO, datefmt='%I:%M:%S')
#with open(os.path.join(self.args.model_dir, 'result.json'), 'r') as f:
# 获取 绝对路径
with open(r'G:\Postgraduate\myClassesOfGraduateStudentOne\张华平大数据人工智能课程\大作业\字符级LSTM文本自动生成模型\TangPoemGenerator\poet\output_poem\result.json', 'r') as f:
result = json.load(f)
params = result['params']
best_model = result['best_model']
best_valid_ppl = result['best_valid_ppl']
if 'encoding' in result:
self.args.encoding = result['encoding']
else:
self.args.encoding = 'utf-8'
base_path = args.data_dir
"""
源代码中都是使用os.path.join 形成的路径在windows下面，类似于
“FileNotFoundError: [Errno 2] No such file or directory: './data/poem\\rhyme_words.txt'”
这样的错误，
所以本次试验使用 绝对路径进行替换
"""
#w2v_file = os.path.join(base_path, "vectors_poem.bin")
w2v_file=r'G:\Postgraduate\myClassesOfGraduateStudentOne\张华平大数据人工智能课程\大作业\字符级LSTM文本自动生成模型\TangPoemGenerator\poet\data\poem\vectors_poem.bin'
#将词嵌入加载进来
self.w2v = Word2Vec(w2v_file)
#读取押韵词
RhymeWords.read_rhyme_words(r'G:\Postgraduate\myClassesOfGraduateStudentOne\张华平大数据人工智能课程\大作业\字符级LSTM文本自动生成模型\TangPoemGenerator\poet\data\poem\rhyme_words.txt')
if args.seed >= 0:
np.random.seed(args.seed)
logging.info('best_model: %s\n', best_model)
best_model=r'G:\Postgraduate\myClassesOfGraduateStudentOne\张华平大数据人工智能课程\大作业\字符级LSTM文本自动生成模型\TangPoemGenerator\poet\output_poem\best_model\model-16312'
self.sess = tf.Session()
w2v_vocab_size = len(self.w2v.model.vocab)
with tf.name_scope('evaluation'):
self.model = CharRNNLM(is_training=False,w2v_model = self.w2v.model,vocab_size=w2v_vocab_size, infer=True, **params)
saver = tf.train.Saver(name='model_saver')
saver.restore(self.sess, best_model)
def __init__(self, args):
self.args = args
logging.basicConfig(stream=sys.stdout,
format='%(asctime)s %(levelname)s:%(message)s',
level=logging.INFO,
datefmt='%I:%M:%S')
with open(os.path.join(self.args.model_dir, 'result.json'), 'r') as f:
result = json.load(f)
params = result['params']
best_model = result['best_model']
best_valid_ppl = result['best_valid_ppl']
if 'encoding' in result:
self.args.encoding = result['encoding']
else:
self.args.encoding = 'utf-8'
base_path = args.data_dir
w2v_file = os.path.join(base_path, "vectors_poem.bin")
self.w2v = Word2Vec(w2v_file)
RhymeWords.read_rhyme_words(os.path.join(base_path, 'rhyme_words.txt'))
if args.seed >= 0:
np.random.seed(args.seed)
logging.info('best_model: %s\n', best_model)
# self.sess = tf.Session()
config = tf.ConfigProto()
config.gpu_options.per_process_gpu_memory_fraction = 0.2
self.sess = tf.Session(config=config)
# self.sess = tf.Session(config=tf.ConfigProto(device_count={'gpu':0}))
w2v_vocab_size = len(self.w2v.model.vocab)
with tf.name_scope('evaluation'):
self.model = CharRNNLM(is_training=False,
w2v_model=self.w2v.model,
vocab_size=w2v_vocab_size,
infer=True,
**params)
saver = tf.train.Saver(name='model_saver')
saver.restore(self.sess, best_model)
class WritePoem():
def __init__(self, args):
self.args = args
logging.basicConfig(stream=sys.stdout,
format='%(asctime)s %(levelname)s:%(message)s',
level=logging.INFO,
datefmt='%I:%M:%S')
with open(os.path.join(self.args.model_dir, 'result.json'), 'r') as f:
result = json.load(f)
params = result['params']
best_model = result['best_model']
best_valid_ppl = result['best_valid_ppl']
if 'encoding' in result:
self.args.encoding = result['encoding']
else:
self.args.encoding = 'utf-8'
base_path = args.data_dir
w2v_file = os.path.join(base_path, "vectors_poem.bin")
self.w2v = Word2Vec(w2v_file)
RhymeWords.read_rhyme_words(os.path.join(base_path, 'rhyme_words.txt'))
if args.seed >= 0:
np.random.seed(args.seed)
logging.info('best_model: %s\n', best_model)
self.sess = tf.Session()
w2v_vocab_size = len(self.w2v.model.vocab)
with tf.name_scope('evaluation'):
self.model = CharRNNLM(is_training=False,
w2v_model=self.w2v.model,
vocab_size=w2v_vocab_size,
infer=True,
**params)
saver = tf.train.Saver(name='model_saver')
saver.restore(self.sess, best_model)
def free_verse(self):
'''
自由诗
Returns:
'''
sample = self.model.sample_seq(self.sess,
40,
'[',
sample_type=SampleType.weighted_sample)
if not sample:
return 'err occar!'
print('free_verse:', sample)
idx_end = sample.find(']')
parts = sample.split('。')
if len(parts) > 1:
two_sentence_len = len(parts[0]) + len(parts[1])
if idx_end < 0 or two_sentence_len < idx_end:
return sample[1:two_sentence_len + 2]
return sample[1:idx_end]
@staticmethod
def assemble(sample):
if sample:
parts = sample.split('。')
if len(parts) > 1:
return '{}。{}。'.format(parts[0][1:], parts[1][:len(parts[0])])
return ''
def rhyme_verse(self):
'''
押韵诗
Returns:
'''
gen_len = 20
sample = self.model.sample_seq(self.sess,
gen_len,
start_text='[',
sample_type=SampleType.weighted_sample)
if not sample:
return 'err occar!'
print('rhyme_verse:', sample)
parts = sample.split('。')
if len(parts) > 0:
start = parts[0] + '。'
rhyme_ref_word = start[-2]
rhyme_seq = len(start) - 3
sample = self.model.sample_seq(
self.sess,
gen_len,
start,
sample_type=SampleType.weighted_sample,
rhyme_ref=rhyme_ref_word,
rhyme_idx=rhyme_seq)
print(sample)
return WritePoem.assemble(sample)
return sample[1:]
def hide_words(self, given_text):
'''
藏字诗
Args:
given_text:
Returns:
'''
if (not given_text):
return self.rhyme_verse()
givens = ['', '']
split_len = math.ceil(len(given_text) / 2)
givens[0] = given_text[:split_len]
givens[1] = given_text[split_len:]
gen_len = 20
sample = self.model.sample_seq(self.sess,
gen_len,
start_text='[',
sample_type=SampleType.select_given,
given=givens[0])
if not sample:
return 'err occar!'
print('rhyme_verse:', sample)
parts = sample.split('。')
if len(parts) > 0:
start = parts[0] + '。'
rhyme_ref_word = start[-2]
rhyme_seq = len(start) - 3
# gen_len = len(start) - 1
sample = self.model.sample_seq(self.sess,
gen_len,
start,
sample_type=SampleType.select_given,
given=givens[1],
rhyme_ref=rhyme_ref_word,
rhyme_idx=rhyme_seq)
print(sample)
return WritePoem.assemble(sample)
return sample[1:]
def cangtou(self, given_text):
'''
藏头诗
Returns:
'''
if (not given_text):
return self.rhyme_verse()
start = ''
rhyme_ref_word = ''
rhyme_seq = 0
# for i,word in enumerate(given_text):
for i in range(4):
word = ''
if i < len(given_text):
word = given_text[i]
if i == 0:
start = '[' + word
else:
start += word
before_idx = len(start)
if (i != 3):
sample = self.model.sample_seq(
self.sess,
self.args.length,
start,
sample_type=SampleType.weighted_sample)
else:
if not word:
rhyme_seq += 1
sample = self.model.sample_seq(self.sess,
self.args.length,
start,
sample_type=SampleType.max_prob,
rhyme_ref=rhyme_ref_word,
rhyme_idx=rhyme_seq)
print('Sampled text is:\n\n%s' % sample)
sample = sample[before_idx:]
idx1 = sample.find('，')
idx2 = sample.find('。')
min_idx = min(idx1, idx2)
if min_idx == -1:
if idx1 > -1:
min_idx = idx1
else:
min_idx = idx2
if min_idx > 0:
# last_sample.append(sample[:min_idx + 1])
start = '{}{}'.format(start, sample[:min_idx + 1])
if i == 1:
rhyme_seq = min_idx - 1
rhyme_ref_word = sample[rhyme_seq]
print('last_sample text is:\n\n%s' % start)
return WritePoem.assemble(start)
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))
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)
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))
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)