基于RNN的文本生成算法的代码运转
目录(?)[+]
“什么时候能自动生成博客？”
前言
跳过废话，直接看正文
RNN相对于传统的神经网络来说对于把握上下文之间的关系更为擅长，因此现在被大量用在自然语言处理的相关任务中，例如生成与训练文集相似的文字、序列标注、中文分词等。
此文列出两种基于RNN的文本生成算法，以供参考。
正文
基于字符的文本生成算法
此代码为keras的官方例子
'''Example script to generate text from Nietzsche's writings.
At least 20 epochs are required before the generated text
starts sounding coherent.
It is recommended to run this script on GPU, as recurrent
networks are quite computationally intensive.
If you try this script on new data, make sure your corpus
has at least ~100k characters. ~1M is better.
'''
from __future__ import print_function
from keras.models import Sequential
from keras.layers import Dense, Activation, Dropout
from keras.layers import LSTM
from keras.optimizers import RMSprop
from keras.utils.data_utils import get_file
import numpy as np
import random
import sys
start_time = time.time()
output_file_handler = open('out.log', 'w')
sys.stdout = output_file_handler
path = get_file('nietzsche.txt', origin="https://s3.amazonaws.com/text-datasets/nietzsche.txt")
text = open(path).read().lower()
print('corpus length:', len(text))
chars = sorted(list(set(text)))
print('total chars:', len(chars))
char_indices = dict((c, i) for i, c in enumerate(chars))
indices_char = dict((i, c) for i, c in enumerate(chars))
# cut the text in semi-redundant sequences of maxlen characters
maxlen = 40
step = 3
sentences = []
next_chars = []
for i in range(0, len(text) - maxlen, step):
sentences.append(text[i: i + maxlen])
next_chars.append(text[i + maxlen])
print('nb sequences:', len(sentences))
print('Vectorization...')
X = np.zeros((len(sentences), maxlen, len(chars)), dtype=np.bool)
y = np.zeros((len(sentences), len(chars)), dtype=np.bool)
for i, sentence in enumerate(sentences):
for t, char in enumerate(sentence):
X[i, t, char_indices[char]] = 1
y[i, char_indices[next_chars[i]]] = 1
# build the model: a single LSTM
print('Build model...')
model = Sequential()
model.add(LSTM(128, input_shape=(maxlen, len(chars))))
model.add(Dense(len(chars)))
model.add(Activation('softmax'))
optimizer = RMSprop(lr=0.01)
model.compile(loss='categorical_crossentropy', optimizer=optimizer)
def sample(preds, temperature=1.0):
# helper function to sample an index from a probability array
preds = np.asarray(preds).astype('float64')
preds = np.log(preds) / temperature
exp_preds = np.exp(preds)
preds = exp_preds / np.sum(exp_preds)
probas = np.random.multinomial(1, preds, 1)
return np.argmax(probas)
# train the model, output generated text after each iteration
for iteration in range(1, 60):
end_time = time.time()
print 'training used time : ' + str(end_time - start_time)
print()
print('-' * 50)
print('Iteration', iteration)
model.fit(X, y, batch_size=128, nb_epoch=1)
start_index = random.randint(0, len(text) - maxlen - 1)
for diversity in [0.2, 0.5, 1.0, 1.2]:
print()
print('----- diversity:', diversity)
generated = ''
sentence = text[start_index: start_index + maxlen]
generated += sentence
print('----- Generating with seed: "' + sentence + '"')
sys.stdout.write(generated)
for i in range(400):
x = np.zeros((1, maxlen, len(chars)))
for t, char in enumerate(sentence):
x[0, t, char_indices[char]] = 1.
preds = model.predict(x, verbose=0)[0]
next_index = sample(preds, diversity)
next_char = indices_char[next_index]
generated += next_char
sentence = sentence[1:] + next_char
sys.stdout.write(next_char)
sys.stdout.flush()
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结合word2vec的文本生成算法
此代码还未完成，将来我再抽空将它完成，这里只是给一个思路。
更多代码参考github
'''Example script to generate text using keras and word2vec
At least 20 epochs are required before the generated text
starts sounding coherent.
It is recommended to run this script on GPU, as recurrent
networks are quite computationally intensive.
'''
from __future__ import print_function
from keras.models import Sequential
from keras.layers import Dense, Activation, Dropout
from keras.layers import LSTM
from keras.optimizers import RMSprop
from keras.utils.data_utils import get_file
from nltk import tokenize
import numpy as np
import random
import sys
import os
import nltk
import gensim, logging
import os
logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)
# a memory-friendly iterator
class MySentences(object):
def __init__(self, dirname, min_word_count_in_sentence = 1):
self.dirname = dirname
self.min_word_count_in_sentence = min_word_count_in_sentence;
def process_line(self, line):
words = line.split()
return words
def __iter__(self):
for fname in os.listdir(self.dirname):
for line in open(os.path.join(self.dirname, fname)):
processed_line = self.process_line(line)
if (len(processed_line) >= self.min_word_count_in_sentence):
yield processed_line
else:
continue
def generate_word2vec_train_files(input_dir, output_dir, sentence_start_token, sentence_end_token, unkown_token, word_min_count, word2vec_size):
print('generate_word2vec_train_files...')
tmp_word2vec_model = gensim.models.Word2Vec(min_count = word_min_count, size = word2vec_size)
original_sentences = MySentences(input_dir)
tmp_word2vec_model.build_vocab(original_sentences)
original_word2vec_vocab = tmp_word2vec_model.vocab
make_dir_if_not_exist(output_dir)
for fname in os.listdir(input_dir):
output_file = open(os.path.join(output_dir, fname), 'w')
line_count = 0
for line in open(os.path.join(input_dir, fname)):
line = line.strip(' -=:\"\'_*\n')
if len(line) == 0:
continue
sentences = tokenize.sent_tokenize(line)
for idx, sentence in enumerate(sentences):
words = sentence.split()
for word_idx, word in enumerate(words):
if word not in original_word2vec_vocab:
words[word_idx] = unkown_token#TODO
sentence = " ".join(word for word in words)
sentences[idx] = sentence_start_token + ' ' + sentence + ' ' + sentence_end_token + '\n'
line_count += len(sentences)
output_file.writelines(sentences)
output_file.close()
print("line_count", line_count)
def train_word2vec_model(dataset_dir, save_model_file, word_min_count, word2vec_size):
print('train_word2vec_model...')
word2vec_model = gensim.models.Word2Vec(min_count = word_min_count, size = word2vec_size)
train_sentences = MySentences(dataset_dir)
word2vec_model.build_vocab(train_sentences)
sentences = MySentences(dataset_dir)
word2vec_model.train(sentences)
word2vec_model.save(save_model_file)
return word2vec_model
def load_existing_word2vec_model(model_file_path):
model =None
if os.path.exists(model_file_path):
print("load existing model...")
model = gensim.models.Word2Vec.load(model_file_path)
return model
def generate_rnn_train_files(input_dir, output_dir, fixed_sentence_len, unkown_token, sentence_start_token, sentence_end_token):
print('generate_rnn_train_files...')
make_dir_if_not_exist(output_dir)
long_than_fixed_len_count = 0;
total_sentence_count = 0;
for fname in os.listdir(input_dir):
output_file = open(os.path.join(output_dir, fname), 'w')
for sentence in open(os.path.join(input_dir, fname)):
sentence = sentence.strip('\n')
total_sentence_count += 1
words = sentence.split()
len_of_sentence = len(words)
if len_of_sentence > fixed_sentence_len:
long_than_fixed_len_count += 1
continue
elif len_of_sentence < fixed_sentence_len:
for i in range(0, fixed_sentence_len - len_of_sentence):
sentence = sentence + ' ' + sentence_end_token
output_file.write(sentence + '\n')
output_file.close()
print ("sentence longer than fixed_len : %d / %d" %(long_than_fixed_len_count, total_sentence_count))
def train_rnn_model(dataset_dir, fixed_sentence_len, word2vec_size, word2vec_model):
# build the model: a single LSTM
print('Build RNN model...')
rnn_model = Sequential()
rnn_model.add(LSTM(128, input_shape=(fixed_sentence_len, word2vec_size)))
rnn_model.add(Dense(word2vec_size))
rnn_model.add(Activation('softmax'))
optimizer = RMSprop(lr=0.01)
rnn_model.compile(loss='categorical_crossentropy', optimizer=optimizer)
print('Generating RNN train data...')
X = [] #np.zeros((0, fixed_sentence_len, word2vec_size), dtype=np.float32)
y = [] #np.zeros((0, word2vec_size), dtype=np.float32)
sentences = MySentences(dataset_dir)
for sentence in sentences:
tmp_x = np.asarray([word2vec_model[w] for w in sentence[:-1]])
tmp_y = np.asarray([word2vec_model[w] for w in sentence[1:]])
tmp_x = np.zeros((fixed_sentence_len, word2vec_size), dtype=np.float32)
for idx, word in enumerate(sentence):
tmp_x[idx] = word2vec_model[word]
X.append()
# X, y = generate_rnn_train_data()
print(X)
print(y)
print('Generate RNN train data end!')
# rnn_model.fit()
print('Build RNN model over!')
return rnn_model
class Config:
WORD2VEC_MODE_FILE = "./word2vec_model.model"
ORIGINAL_TRAIN_DATASET_DIR = "./small_train_text"
WORD2VEC_TRAIN_DATASET_DIR = "./small_word2vec_train_text"
RNN_TRAIN_DATASET_DIR = "./small_rnn_train_text"
SENTENCE_START_TOKEN = "SENTENCE_START_TOKEN"
SENTENCE_END_TOKEN = "SENTENCE_END_TOKEN"
UNKNOWN_TOKEN = "UNKNOWN_TOKEN"
FIXED_SENTENCE_LEN = 30
MIN_COUNT = 2;
WORD2VEC_SIZE = 20;
def make_dir_if_not_exist(dirpath):
if not os.path.exists(dirpath):
os.mkdir(dirpath)
def main():
# word2vec train
word2vec_model = load_existing_word2vec_model(Config.WORD2VEC_MODE_FILE)
if word2vec_model == None:
generate_word2vec_train_files(
Config.ORIGINAL_TRAIN_DATASET_DIR, Config.WORD2VEC_TRAIN_DATASET_DIR,
Config.SENTENCE_START_TOKEN, Config.SENTENCE_END_TOKEN, Config.UNKNOWN_TOKEN, Config.MIN_COUNT, Config.WORD2VEC_SIZE)
word2vec_model = train_word2vec_model(Config.WORD2VEC_TRAIN_DATASET_DIR, Config.WORD2VEC_MODE_FILE, Config.MIN_COUNT, Config.WORD2VEC_SIZE)
# rnn train
generate_rnn_train_files(
Config.WORD2VEC_TRAIN_DATASET_DIR, Config.RNN_TRAIN_DATASET_DIR,
Config.FIXED_SENTENCE_LEN, Config.UNKNOWN_TOKEN,
Config.SENTENCE_START_TOKEN, Config.SENTENCE_END_TOKEN)
rnn_model = train_rnn_model(Config.RNN_TRAIN_DATASET_DIR, Config.FIXED_SENTENCE_LEN, Config.WORD2VEC_SIZE, word2vec_model)
main()
# if __name__ == "__main__":
# main()
# 后记
就目前而言，利用基于RNN的文本生成算法虽然能够生成通顺的句子，却远远不能用来创作文章。因为RNN本质上还是基于词句在训练集中出现的概率来生成文本，这种暴力模仿的文本生成算法终究不是根本的解决之道，将来融合人工智能领域的其他的一些算法或许能够达到比较好的效果。
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