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基于RNN的文本生成算法的代码运转

發(fā)布時(shí)間：2024/1/1 编程问答 43 豆豆

生活随笔收集整理的這篇文章主要介紹了基于RNN的文本生成算法的代码运转小編覺(jué)得挺不錯(cuò)的,現(xiàn)在分享給大家,幫大家做個(gè)參考.

目錄(?)[+]

“什么時(shí)候能自動(dòng)生成博客？”

前言

跳過(guò)廢話，直接看正文

RNN相對(duì)于傳統(tǒng)的神經(jīng)網(wǎng)絡(luò)來(lái)說(shuō)對(duì)于把握上下文之間的關(guān)系更為擅長(zhǎng)，因此現(xiàn)在被大量用在自然語(yǔ)言處理的相關(guān)任務(wù)中，例如生成與訓(xùn)練文集相似的文字、序列標(biāo)注、中文分詞等。

此文列出兩種基于RNN的文本生成算法，以供參考。

正文

基于字符的文本生成算法

此代碼為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 sysstart_time = time.time() output_file_handler = open('out.log', 'w') sys.stdout = output_file_handlerpath = 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]] = 1y[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 arraypreds = np.asarray(preds).astype('float64')preds = np.log(preds) / temperatureexp_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 += sentenceprint('----- 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_charsentence = sentence[1:] + next_charsys.stdout.write(next_char)sys.stdout.flush()print()

結(jié)合word2vec的文本生成算法

此代碼還未完成，將來(lái)我再抽空將它完成，這里只是給一個(gè)思路。?
更多代碼參考github

'''Example script to generate text using keras and word2vecAt 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 nltkimport 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 = dirnameself.min_word_count_in_sentence = min_word_count_in_sentence;def process_line(self, line):words = line.split()return wordsdef __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_lineelse:continuedef 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.vocabmake_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 = 0for line in open(os.path.join(input_dir, fname)):line = line.strip(' -=:\"\'_*\n')if len(line) == 0:continuesentences = 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#TODOsentence = " ".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_modeldef load_existing_word2vec_model(model_file_path):model =Noneif os.path.exists(model_file_path):print("load existing model...")model = gensim.models.Word2Vec.load(model_file_path)return modeldef 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 += 1words = sentence.split()len_of_sentence = len(words)if len_of_sentence > fixed_sentence_len:long_than_fixed_len_count += 1continueelif len_of_sentence < fixed_sentence_len:for i in range(0, fixed_sentence_len - len_of_sentence):sentence = sentence + ' ' + sentence_end_tokenoutput_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 LSTMprint('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_modelclass 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 = 30MIN_COUNT = 2;WORD2VEC_SIZE = 20;def make_dir_if_not_exist(dirpath):if not os.path.exists(dirpath):os.mkdir(dirpath)def main():# word2vec trainword2vec_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 traingenerate_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() #

后記

就目前而言，利用基于RNN的文本生成算法雖然能夠生成通順的句子，卻遠(yuǎn)遠(yuǎn)不能用來(lái)創(chuàng)作文章。因?yàn)镽NN本質(zhì)上還是基于詞句在訓(xùn)練集中出現(xiàn)的概率來(lái)生成文本，這種暴力模仿的文本生成算法終究不是根本的解決之道，將來(lái)融合人工智能領(lǐng)域的其他的一些算法或許能夠達(dá)到比較好的效果。

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