LSTM/RNN可用于文本生成。这展示了为Keras模型使用预先训练的GloVe单词嵌入的方法。
- 如何在Keras LSTM中使用预训练的Word2Verc单词嵌入模型这篇文章确实有所帮助
- 当向模型提供单词序列作为其输入时，如何预测/生成下一个单词
尝试的示例方法：
# Sample code to prepare word2vec word embeddings
import gensim
documents = ["Human machine interface for lab abc computer applications",
"A survey of user opinion of computer system response time",
"The EPS user interface management system",
"System and human system engineering testing of EPS",
"Relation of user perceived response time to error measurement",
"The generation of random binary unordered trees",
"The intersection graph of paths in trees",
"Graph minors IV Widths of trees and well quasi ordering",
"Graph minors A survey"]
sentences = [[word for word in document.lower().split()] for document in documents]
word_model = gensim.models.Word2Vec(sentences, size=200, min_count = 1, window = 5)
# Code tried to prepare LSTM model for word generation
from keras.layers.recurrent import LSTM
from keras.layers.embeddings import Embedding
from keras.models import Model, Sequential
from keras.layers import Dense, Activation
embedding_layer = Embedding(input_dim=word_model.syn0.shape[0], output_dim=word_model.syn0.shape[1], weights=[word_model.syn0])
model = Sequential()
model.add(embedding_layer)
model.add(LSTM(word_model.syn0.shape[1]))
model.add(Dense(word_model.syn0.shape[0]))
model.add(Activation('softmax'))
model.compile(optimizer='sgd', loss='mse')
将赞赏用于训练LSTM和预测的示例代码/psuedocode。
我用一个简单的生成器创建了一个要点，它建立在你最初的想法之上：它是一个LSTM网络，连接到预先训练的word2vec嵌入，经过训练可以预测句子中的下一个单词。数据是arXiv网站的摘要列表。
我将在这里强调最重要的部分。
Gensim Word2Verc
你的代码很好，除了训练它的迭代次数。默认的iter=5似乎很低。此外，这绝对不是瓶颈——LSTM训练需要更长的时间。iter=100看起来更好。
word_model = gensim.models.Word2Vec(sentences, vector_size=100, min_count=1,
window=5, iter=100)
pretrained_weights = word_model.wv.syn0
vocab_size, emdedding_size = pretrained_weights.shape
print('Result embedding shape:', pretrained_weights.shape)
print('Checking similar words:')
for word in ['model', 'network', 'train', 'learn']:
most_similar = ', '.join('%s (%.2f)' % (similar, dist)
for similar, dist in word_model.most_similar(word)[:8])
print(' %s -> %s' % (word, most_similar))
def word2idx(word):
return word_model.wv.vocab[word].index
def idx2word(idx):
return word_model.wv.index2word[idx]
结果嵌入矩阵被保存到具有形状CCD_ 4的CCD_。
Keras模型
除了loss函数外，您的代码几乎是正确的。由于模型预测下一个单词，这是一个分类任务，因此损失应该是categorical_crossentropy或sparse_categorical_crossentropy。出于效率的原因，我选择了后者：这样可以避免一个热编码，这对于一个大的词汇表来说是非常昂贵的。
model = Sequential()
model.add(Embedding(input_dim=vocab_size, output_dim=emdedding_size,
weights=[pretrained_weights]))
model.add(LSTM(units=emdedding_size))
model.add(Dense(units=vocab_size))
model.add(Activation('softmax'))
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy')
注意将预先训练的权重传递给weights。
数据准备
为了处理sparse_categorical_crossentropy损失，句子和标签都必须是单词索引。短句的常用长度必须加零。
train_x = np.zeros([len(sentences), max_sentence_len], dtype=np.int32)
train_y = np.zeros([len(sentences)], dtype=np.int32)
for i, sentence in enumerate(sentences):
for t, word in enumerate(sentence[:-1]):
train_x[i, t] = word2idx(word)
train_y[i] = word2idx(sentence[-1])
样本生成
这是非常直接的：模型输出概率向量，其中下一个单词被采样并附加到输入中。请注意，如果下一个单词是采样，而不是拾取作为argmax，则生成的文本会更好、更多样化。这里描述了我使用的基于温度的随机采样。
def sample(preds, temperature=1.0):
if temperature <= 0:
return np.argmax(preds)
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)
def generate_next(text, num_generated=10):
word_idxs = [word2idx(word) for word in text.lower().split()]
for i in range(num_generated):
prediction = model.predict(x=np.array(word_idxs))
idx = sample(prediction[-1], temperature=0.7)
word_idxs.append(idx)
return ' '.join(idx2word(idx) for idx in word_idxs)
生成的文本示例
deep convolutional... -> deep convolutional arithmetic initialization step unbiased effectiveness
simple and effective... -> simple and effective family of variables preventing compute automatically
a nonconvex... -> a nonconvex technique compared layer converges so independent onehidden markov
a... -> a function parameterization necessary both both intuitions with technique valpola utilizes
没有太多意义，但能够产生至少在语法上合理的句子(有时)。
指向完整可运行脚本的链接。