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 main(_): model_path = os.path.join('models', Config.file_name) converter = TextConverter(vocab_dir='data/vocabs', max_vocab=Config.vocab_size, seq_length=Config.seq_length) print('vocab lens:', converter.vocab_size) # 加载上一次保存的模型 model = Model(Config) checkpoint_path = tf.train.latest_checkpoint(model_path) if checkpoint_path: model.load(checkpoint_path) while True: english_speek = input("上联:") english_speek = ' '.join(english_speek) english_speek = english_speek.split() en_arr, arr_len = converter.text_en_to_arr(english_speek) test_g = [np.array([ en_arr, ]), np.array([ arr_len, ])] output_ids = model.test(test_g, model_path, converter) strs = converter.arr_to_text(output_ids) print('下联:', strs)
def main(_): model_path = os.path.join('models', Config.file_name) et = TextConverter(text=None,save_dir='models/en_vocab.pkl', max_vocab=Config.en_vocab_size, seq_length = Config.seq_length) zt = TextConverter(text=None,save_dir='models/zh_vocab.pkl', max_vocab=Config.zh_vocab_size, seq_length = Config.seq_length+1) # +1是因为,decoder层序列拆成input=[:-1]和label=[1:] print('english vocab lens:',et.vocab_size) print('chinese vocab lens:',zt.vocab_size) # 加载上一次保存的模型 model = Model(Config) checkpoint_path = tf.train.latest_checkpoint(model_path) if checkpoint_path: model.load(checkpoint_path) while True: # english_speek = 'what can i help you ?' # print('english:', english_speek) english_speek = input("english:") english_speek = english_speek.split() en_arr, arr_len = et.text_to_arr(english_speek) test_g = [np.array([en_arr,]), np.array([arr_len,])] output_ids = model.test(test_g, model_path, zt) strs = zt.arr_to_text(output_ids) print('chinese:',strs)
def main(_): converter = TextConverter(filename=FLAGS.converter_path) model = charRNN(converter.vocab_size, train=False) model.load(tf.train.latest_checkpoint(FLAGS.checkpoint_path)) start = converter.text_to_arr(FLAGS.start_string) arr = model.generate(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) 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 test_vocab_size(self): testConverter = TextConverter(text=[ "We", "are", "accounted", "poor", "citizens,", "the", "patricians", "goodare", "accounted", "poor", "citizens,", "the", "patricians", "good" ], max_vocab=10) print(testConverter.vocab_size) print(testConverter.int_to_word(4)) print(testConverter.text_to_arr(['the'])) print(testConverter.arr_to_text([3, 4]))
def sample(): with tf.Session() as sess: model_path = os.path.join(FLAGS.train_dir, FLAGS.model_name) converter = TextConverter(None, FLAGS.max_vocab_size, os.path.join(model_path, 'converter.pkl')) model = create_model(sess, converter.vocab_size, True, model_path) sys.stdout.write("> ") sys.stdout.flush() start_str = sys.stdin.readline().decode('utf-8') while start_str: start = converter.text_to_arr(start_str) samples = [c for c in start] initial_state = sess.run(model.initial_state) x = np.zeros((1, 1)) for c in start: x[0, 0] = c feed = {model.inputs: x, model.initial_state: initial_state} preds, final_state = sess.run( [model.proba_prediction, model.final_state], feed_dict=feed) initial_state = final_state c = pick_top_n(preds, converter.vocab_size) while c == converter.vocab_size - 1: c = pick_top_n(preds, converter.vocab_size) samples.append(c) for i in range(FLAGS.sample_length): x[0, 0] = c feed = {model.inputs: x, model.initial_state: initial_state} preds, final_state = sess.run( [model.proba_prediction, model.final_state], feed_dict=feed) initial_state = final_state c = pick_top_n(preds, converter.vocab_size) while c == converter.vocab_size - 1: c = pick_top_n(preds, converter.vocab_size) samples.append(c) print(converter.arr_to_text(np.array(samples))) sys.stdout.write("> ") sys.stdout.flush() start_str = sys.stdin.readline().decode('utf-8')
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)
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)