def main(_):
FLAGS.start_string = FLAGS.start_string #.decode('utf-8')
converter = TextConverter(filename=FLAGS.converter_path)
if os.path.isdir(FLAGS.checkpoint_path):
FLAGS.checkpoint_path =\
tf.train.latest_checkpoint(FLAGS.checkpoint_path)
model = CharRNN(converter.vocab_size,
sampling=True,
lstm_size=FLAGS.lstm_size,
num_layers=FLAGS.num_layers,
use_embedding=FLAGS.use_embedding,
embedding_size=FLAGS.embedding_size)
model.load(FLAGS.checkpoint_path)
start_string = FLAGS.start_string
sys.stdout.write("> ")
sys.stdout.flush()
start_string = sys.stdin.readline()
while start_string:
start = converter.text_to_arr(start_string)
arr = model.sample(FLAGS.max_length, start, converter.vocab_size)
print(converter.arr_to_text(arr))
sys.stdout.write("> ")
sys.stdout.flush()
sentence = sys.stdin.readline()
def main(_):
converter = TextConverter(filename=FLAGS.converter_path)
if os.path.isdir(FLAGS.checkpoint_path):
FLAGS.checkpoint_path = tf.train.latest_checkpoint(
FLAGS.checkpoint_path)
model = CharRNN(converter.vocab_size,
None,
sampling=True,
lstm_size=FLAGS.lstm_size,
num_layers=FLAGS.num_layers,
use_embedding=FLAGS.use_embedding,
embedding_size=FLAGS.embedding_size)
model.load(FLAGS.checkpoint_path)
# start = converter.text_to_arr(FLAGS.seed_for_generating)
seeds = [
'var a = fun', 'function a(', 'this.', 'document.', 'window.',
'var a = document.g', 'var a;', 'jQuery'
]
for seed in seeds:
start = converter.text_to_arr(seed)
for i in range(0, FLAGS.num_to_generate):
print('Generating: ' + seed + ' -> ' + str(i))
file_name = str(uuid.uuid1())
file_path = '../../BrowserFuzzingData/generated/' + FLAGS.file_type + '/' + file_name + '.' + FLAGS.file_type
arr = model.sample(FLAGS.max_length_of_generated, start,
converter.vocab_size, converter.word_to_int)
f = open(file_path, "wb")
f.write(converter.arr_to_text(arr).encode('utf-8'))
f.close()
def composePotery():
converter = TextConverter(filename=FLAGS.converter_path)
if os.path.isdir(FLAGS.checkpoint_path):
FLAGS.checkpoint_path =\
tf.train.latest_checkpoint(FLAGS.checkpoint_path)
model = CharRNN(converter.vocab_size, sampling=True,
lstm_size=FLAGS.lstm_size, num_layers=FLAGS.num_layers,
use_embedding=FLAGS.use_embedding,
embedding_size=FLAGS.embedding_size)
model.load(FLAGS.checkpoint_path)
start = []
arr = model.sample(FLAGS.max_length, start, converter.vocab_size)
rawText = converter.arr_to_text(arr)
return(selectPoetry(rawText))
def main(_):
converter = TextConverter(filename=FLAGS.converter_path)
if os.path.isdir(FLAGS.checkpoint_path):
FLAGS.checkpoint_path =\
tf.train.latest_checkpoint(FLAGS.checkpoint_path)
model = CharRNN(converter.vocab_size, sampling=True,
lstm_size=FLAGS.lstm_size, num_layers=FLAGS.num_layers,
use_embedding=FLAGS.use_embedding,
embedding_size=FLAGS.embedding_size)
model.load(FLAGS.checkpoint_path)
start = converter.text_to_arr(FLAGS.start_string)
arr = model.sample(FLAGS.max_length, start, converter.vocab_size)
print(converter.arr_to_text(arr))
def main(_):
tc = TextConverter("", -1, byte_file=FLAGS.vocab_path)
output_size = tc.vocab_size
if os.path.isdir(FLAGS.checkpoint_path):
FLAGS.checkpoint_path = tf.train.latest_checkpoint(FLAGS.checkpoint_path)
model = CharRNN(output_size=output_size,
lstm_size=FLAGS.lstm_size,
num_layers=FLAGS.num_layers,
sampling=True)
model.load(FLAGS.checkpoint_path)
start = tc.text_to_arr(FLAGS.start_string)
generate_arr = model.sample(FLAGS.length, start, output_size)
generate_text = tc.arr_to_text(generate_arr)
with open(FLAGS.save_path, 'w', encoding='utf-8') as f:
f.write(generate_text)
print(generate_text)
def main(_):
tokenizer = Tokenizer(vocab_path=FLAGS.tokenizer_path)
if os.path.isdir(FLAGS.checkpoint_path):
FLAGS.checkpoint_path = \
tf.train.latest_checkpoint(FLAGS.checkpoint_path)
model = CharRNN(tokenizer.vocab_size, sampling=True,
n_neurons=FLAGS.n_neurons, n_layers=FLAGS.n_layers,
embedding=FLAGS.embedding,
embedding_size=FLAGS.embedding_size)
model.load(FLAGS.checkpoint_path)
start = tokenizer.texts_to_sequences(FLAGS.start_string)
arr = model.sample(FLAGS.max_length, start, tokenizer.vocab_size)
print(tokenizer.sequences_to_texts(arr))
def main(_):
FLAGS.start_string = FLAGS.start_string.decode('utf-8')
converter = TextConverter(filename=FLAGS.converter_path)
if os.path.isdir(FLAGS.checkpoint_path):
FLAGS.checkpoint_path =\
tf.train.latest_checkpoint(FLAGS.checkpoint_path)
model = CharRNN(converter.vocab_size, sampling=True,
lstm_size=FLAGS.lstm_size, num_layers=FLAGS.num_layers,
use_embedding=FLAGS.use_embedding,
embedding_size=FLAGS.embedding_size)
model.load(FLAGS.checkpoint_path)
start = converter.text_to_arr(FLAGS.start_string)
arr = model.sample(FLAGS.max_length, start, converter.vocab_size)
print(converter.arr_to_text(arr))
def main(_):
model_path = os.path.join('model', FLAGS.name)
print(model_path)
if os.path.exists(model_path) is False:
os.makedirs(model_path)
path_exist = False
else:
path_exist = True
with codecs.open(FLAGS.input_file, encoding='utf-8') as f:
text = f.read()
converter = TextConverter(text, FLAGS.max_vocab)
converter.save_to_file(os.path.join(model_path, 'converter.pkl'))
arr = converter.text_to_arr(text)
g = batch_generator(arr, FLAGS.num_seqs, FLAGS.num_steps)
print(converter.vocab_size)
model = CharRNN(converter.vocab_size,
num_seqs=FLAGS.num_seqs,
num_steps=FLAGS.num_steps,
lstm_size=FLAGS.lstm_size,
num_layers=FLAGS.num_layers,
learning_rate=FLAGS.learning_rate,
train_keep_prob=FLAGS.train_keep_prob,
use_embedding=FLAGS.use_embedding,
embedding_size=FLAGS.embedding_size
)
model_file_path = tf.train.latest_checkpoint(model_path)
if path_exist:
model.load(model_file_path)
indexes = []
for dirpath, dirnames, filenames in os.walk(model_path):
for name in filenames:
filepath = os.path.join(dirpath, name)
if filepath.endswith(".index"):
indexes.append(int(name[6:-6]))
indexes.sort()
last_index = indexes[-1]
model.step = last_index
model.train(g,
FLAGS.max_steps,
model_path,
FLAGS.save_every_n,
FLAGS.log_every_n,
)
def main(_):
FLAGS.start_string = FLAGS.start_string.decode('utf-8')
converter = TextConverter(filename=FLAGS.converter_path) #创建文本转化器
if os.path.isdir(FLAGS.checkpoint_path):
FLAGS.checkpoint_path = tf.train.latest_checkpoint(
FLAGS.checkpoint_path) #下载最新模型
model = CharRNN(converter.vocab_size,
sampling=True,
lstm_size=FLAGS.lstm_size,
num_layers=FLAGS.num_layers,
use_embedding=FLAGS.use_embedding,
embedding_size=FLAGS.embedding_size)
model.load(FLAGS.checkpoint_path) #加载模型
start = converter.text_to_arr(FLAGS.start_string) #将input text转为id
arr = model.sample(FLAGS.max_length, start,
converter.vocab_size) #输出为生成的序列
print(converter.arr_to_text(arr))
def generate():
tf.compat.v1.disable_eager_execution()
converter = TextConverter(filename=FLAGS.converter_path)
if os.path.isdir(FLAGS.checkpoint_path):
FLAGS.checkpoint_path =\
tf.train.latest_checkpoint(FLAGS.checkpoint_path)
model = CharRNN(converter.vocab_size,
sampling=True,
lstm_size=FLAGS.lstm_size,
num_layers=FLAGS.num_layers,
use_embedding=FLAGS.use_embedding,
embedding_size=FLAGS.embedding_size)
model.load(FLAGS.checkpoint_path)
start = converter.text_to_arr(FLAGS.start_string)
arr = model.sample(FLAGS.max_length, start, converter.vocab_size)
return converter.arr_to_text(arr)
def main(_):
FLAGS.start_string = FLAGS.start_string
convert = TextConvert(fname=FLAGS.convert_path)
if os.path.isdir(FLAGS.checkpoint_path):
FLAGS.checkpoint_path = tf.train.latest_checkpoint(
FLAGS.checkpoint_path)
model = CharRNN(convert.vocab_size,
sampling=True,
lstm_size=FLAGS.lstm_size,
num_layers=FLAGS.num_layers,
use_embedding=FLAGS.use_embedding,
embedding_size=FLAGS.embedding_size)
model.load(FLAGS.checkpoint_path)
start = convert.text2arr(FLAGS.start_string)
arr = model.sample(FLAGS.max_length, start, convert.vocab_size)
res = convert.arr2text(arr)
print('get result: \n', res)
def main(_):
FLAGS.start_string = FLAGS.start_string
converter = TextConverter(filename=FLAGS.converter_path)
if os.path.isdir(FLAGS.checkpoint_path):
FLAGS.checkpoint_path = \
tf.train.latest_checkpoint(FLAGS.checkpoint_path)
model = CharRNN(converter.vocab_size,
sampling=True,
state_size=FLAGS.state_size,
n_layers=FLAGS.n_layers,
use_embedding=FLAGS.use_embedding,
embedding_size=FLAGS.embedding_size)
model.load(FLAGS.checkpoint_path)
start = converter.text_to_data(FLAGS.start_string)
data = model.sample(FLAGS.max_length, start, converter.vocab_size)
# for c in converter.data_to_text(data):
# for d in c:
# # print(d,end="")
# time.sleep(0.5)
print(converter.data_to_text(data))
def main(_):
converter = TextConverter(filename=FLAGS.converter_path)
if os.path.isdir(FLAGS.checkpoint_path):
FLAGS.checkpoint_path = \
tf.train.latest_checkpoint(FLAGS.checkpoint_path)
model = CharRNN(converter.vocab_size,
sampling=True,
lstm_size=FLAGS.lstm_size,
num_layers=FLAGS.num_layers,
use_embedding=FLAGS.use_embedding,
embedding_size=FLAGS.embedding_size)
model.load(FLAGS.checkpoint_path)
start = converter.text_to_arr(FLAGS.start_string)
# JS/Html/CSS
for i in range(0, 1):
print('Generating: ' + str(i))
file_path = '../../BrowserFuzzingData/generated/' + FLAGS.file_type + '/' + str(
i) + '.' + FLAGS.file_type
# f = open(file_path, "x")
arr = model.sample(FLAGS.max_length, start, converter.vocab_size)
content = converter.arr_to_text(arr)
content = content.replace("\\t", "\t")
content = content.replace("\\r", "\r")
content = content.replace("\\n", "\n")
if FLAGS.file_type.__eq__('js'):
print(content)
# f.write(content)
# f.close()
elif FLAGS.file_type.__eq__('html'):
content = post_process(content)
f.write(content)
f.close()
# TODO: 预留给CSS，暂不作任何处理
else:
pass
class Dianpin(Singleton):
def __init__(self):
self.text = ''
self.tfmodel = None
self.converter = None
def model_built(self):#,vocab_size,sampling,lstm_size,num_layers,use_embedding,embedding_size):
FLAGS.start_string = FLAGS.start_string.decode('utf-8')
self.converter = TextConverter(filename=FLAGS.converter_path)
if os.path.isdir(FLAGS.checkpoint_path):
FLAGS.checkpoint_path =\
tf.train.latest_checkpoint(FLAGS.checkpoint_path)
self.tfmodel = CharRNN(self.converter.vocab_size, sampling=True,
lstm_size=FLAGS.lstm_size, num_layers=FLAGS.num_layers,
use_embedding=FLAGS.use_embedding,
embedding_size=FLAGS.embedding_size)
self.tfmodel.load(FLAGS.checkpoint_path)
def final_predict(self):
start = self.converter.text_to_arr(FLAGS.start_string)
arr = self.tfmodel.sample(FLAGS.max_length, start, self.converter.vocab_size)
return self.converter.arr_to_text(arr)
def main(_):
FLAGS.start_string = FLAGS.start_string
converter = TextConverter(filename=FLAGS.converter_path)
if os.path.isdir(FLAGS.checkpoint_path):
FLAGS.checkpoint_path =\
tf.train.latest_checkpoint(FLAGS.checkpoint_path)
model = CharRNN(converter.vocab_size, sampling=True,
lstm_size=FLAGS.lstm_size, num_layers=FLAGS.num_layers,
use_embedding=FLAGS.use_embedding,
embedding_size=FLAGS.embedding_size)
model.load(FLAGS.checkpoint_path)
start = converter.text_to_arr(FLAGS.start_string)
arr = model.predict(FLAGS.max_length, start, converter.vocab_size, 10)
for c, p in arr:
prediction = converter.arr_to_text(c)
prediction = remove_return(prediction)
# 如果有中文字生成，请将 {1:^14} 改为 {1:{4}^14} 以修复对齐问题。
# {1:^14}中的 14 随着生成的字符数量而定，一般可以设为字符数+4
print("{0} -> {1:^14} {2} {3}".format(FLAGS.start_string, prediction, "probability:", p, chr(12288)))
def poem_genetate(poem_start=u'君'):
#FLAGS.start_string = FLAGS.start_string
#FLAGS.start_string = FLAGS.start_string.decode('utf-8')
converter = TextConverter(filename=FLAGS.converter_path)
if os.path.isdir(FLAGS.checkpoint_path):
FLAGS.checkpoint_path =tf.train.latest_checkpoint(FLAGS.checkpoint_path)
print FLAGS.checkpoint_path
"""
model = CharRNN(converter.vocab_size, sampling=True,
lstm_size=FLAGS.lstm_size, num_layers=FLAGS.num_layers,
use_embedding=FLAGS.use_embedding,
embedding_size=FLAGS.embedding_size)
"""
model = CharRNN(converter.vocab_size, sampling=True,
lstm_size=lstm_size, num_layers=num_layers,
use_embedding=use_embedding,embedding_size=FLAGS.embedding_size)
model.load(FLAGS.checkpoint_path)
#start = converter.text_to_arr(start_string)
start1 = converter.text_to_arr(poem_start)
arr = model.sample(max_length, start1, converter.vocab_size)
#pl = model.poemline(max_length, start, converter.vocab_size)
#sp=model.sample_hide_poetry( start, converter.vocab_size)
poem=converter.arr_to_text(arr)
#print (converter.arr_to_text(sp))
print('---------')
print(poem)
print('---------')
#print(converter.arr_to_text(pl))
print('---------')
#0:, 1:。 2:\n,每行12个字符。不可以有0,1,2大于1个
lines=poem.split('\n')
r_poem=[]
for i in range(len(lines)):
if len(lines[i])==12:
count=0
print lines[i][5]
if lines[i][5]==',':
print "true"
if lines[i][5]==u'，':
print "u true"
if lines[i][5]==u'，' and lines[i][11]==u'。':
for j in range(len(lines[i])):
if lines[i][j]==u'，' or lines[i][j]==u'。':
count+=1
if count==2:
r_poem.append(lines[i])
if len(r_poem)==2:
break
"""
lines=poem.split('\n')
r_poem=[]
for i in range(len(lines)):
if len(lines[i])==12:
count=0
if lines[i][5]==0 and lines[i][11]==1:
for j in range(len(lines[i])):
if lines[i][j]==0 or lines[i][j]==1:
count+=1
if count==2:
r_poem.append(lines[i])
if len(r_poem)==2:
break
"""
with codecs.open("app/poem.txt","w",'utf-8') as f:
words="".join(r_poem)
print (lines)
print (r_poem)
print (words)
#words=words.decode('utf-8')
f.write(words)
