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(_):
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)
if line:
lines.append(line)
else:
break
if lines[0] == 'quit':
print('Goodbye...')
break # exit program
print('Thinking...')
input_seq = '\n'.join(lines)
results = model.autocomplete(sess, input_seq)
print('Autocomplete:')
for i, r in enumerate(results):
print('{0}. {1}'.format(i+1, r[len(input_seq):][:-1])) # skip context and trailing newline char
else:
print('---')
print('Hit enter to sample more characters (or type \'quit\' to quit):')
line = input()
if line == 'quit':
print('Goodbye...')
break # exit program
print('Sampling...')
if input_seq is None:
input_seq = np.random.choice(vocab) # pick random character
sample = model.sample(sess, input_seq=input_seq, num_to_sample=500, temperature=0.5)
input_seq += sample
print(sample)
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)
if i % 100 == 0:
# Write loss and accuracy to log
batch_loss, batch_acc = model.calc_loss_acc(
sess, batch_x, batch_y, batch_seqlen)
with open(os.path.join('.', run_name, 'loss-history.txt'),
mode='a') as f:
f.write('{0},{1}\n'.format(i, batch_loss))
with open(os.path.join('.', run_name, 'acc-history.txt'),
mode='a') as f:
f.write('{0},{1}\n'.format(i, batch_acc))
# Print status
if i % 1000 == 0:
print(datetime.datetime.now(), '| iter', i, 'batch_loss:',
batch_loss, 'batch_acc:', batch_acc)
print(
repr(
model.sample(sess,
input_seq=np.random.choice(chars),
num_to_sample=200)))
# Save checkpoint
if i % 10000 == 0:
save_path = model.save_checkpoint(
sess,
os.path.join('.', run_name,
'checkpoint-{0}.ckpt'.format(i)))
print('Checkpoint saved:', save_path)
print('--')
