如何将keras嵌入替换为CNN的预训练单词嵌入

Question

我目前正在研究如何在文本分类中使用细胞神经网络，并发现了一些使用keras嵌入层的堆栈溢出代码。

我用keras嵌入运行了代码，但现在想测试预训练的嵌入会发生什么，我已经从gensim下载了word2vec api，但不知道如何从那里调整代码？

我的问题是，如何用像word2vec模型或Glove这样的预训练嵌入来替换keras嵌入层？

这是代码

from keras.datasets import imdb
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import LSTM, Convolution1D, Flatten, Dropout
from keras.layers.embeddings import Embedding
from keras.preprocessing import sequence
from keras.callbacks import TensorBoard
# Using keras to load the dataset with the top_words
top_words = 10000
(X_train, y_train), (X_test, y_test) = imdb.load_data(num_words=top_words)
# Pad the sequence to the same length
max_review_length = 1600
X_train = sequence.pad_sequences(X_train, maxlen=max_review_length)
X_test = sequence.pad_sequences(X_test, maxlen=max_review_length)
# Using embedding from Keras
embedding_vecor_length = 300
model = Sequential()
model.add(Embedding(top_words, embedding_vecor_length, input_length=max_review_length))
# Convolutional model (3x conv, flatten, 2x dense)
model.add(Convolution1D(64, 3, padding='same'))
model.add(Convolution1D(32, 3, padding='same'))
model.add(Convolution1D(16, 3, padding='same'))
model.add(Flatten())
model.add(Dropout(0.2))
model.add(Dense(180,activation='sigmoid'))
model.add(Dropout(0.2))
model.add(Dense(1,activation='sigmoid'))
# Log to tensorboard
tensorBoardCallback = TensorBoard(log_dir='./logs', write_graph=True)
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
model.fit(X_train, y_train, epochs=3, callbacks=[tensorBoardCallback], batch_size=64)
# Evaluation on the test set
scores = model.evaluate(X_test, y_test, verbose=0)
print("Accuracy: %.2f%%" % (scores[1]*100))

Answer 1

这将读取包含权重的文本文件，将单词及其权重存储在字典中，然后使用fit标记器的词汇将它们映射到新的矩阵中。

from keras.datasets import imdb
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import LSTM, Convolution1D, Flatten, Dropout
from keras.layers.embeddings import Embedding
from keras.preprocessing import sequence
from keras.callbacks import TensorBoard
from tensorflow import keras
import itertools
import numpy as np

# Using keras to load the dataset with the top_words
top_words = 10000
(X_train, y_train), (X_test, y_test) = imdb.load_data(num_words=top_words)
word_index = keras.datasets.imdb.get_word_index()
embedding_vecor_length = 300  # same as the embeds to be loaded below
embeddings_dictionary = dict()
glove_file = open('./embeds/glove.6B.300d.txt', 'rb')
for line in glove_file:
records = line.split()  # seperates each line by a white space
word = records[0]  # the first element is the word
vector_dimensions = np.asarray(
records[1:], dtype='float32')  # the rest are the weights
# storing in dictionary
embeddings_dictionary[word] = vector_dimensions

glove_file.close()
# len_of_vocab = len(word_index)
embeddings_matrix = np.zeros((top_words, embedding_vecor_length))
# mapping to a new matrix, using only the words in your tokenizer's vocabulary
for word, index in word_index.items():
if index>=top_words:
continue
# the weights of the individual words in your vocabulary
embedding_vector = embeddings_dictionary.get(bytes(word, 'utf-8'))
if embedding_vector is not None:
embeddings_matrix[index] = embedding_vector
# Pad the sequence to the same length
max_review_length = 1600
X_train = sequence.pad_sequences(X_train, maxlen=max_review_length)
X_test = sequence.pad_sequences(X_test, maxlen=max_review_length)

# Using embedding from Keras
model = Sequential()
model.add(Embedding(top_words, embedding_vecor_length,
input_length=max_review_length, name="embeddinglayer", weights=[embeddings_matrix], trainable=True))

# Convolutional model (3x conv, flatten, 2x dense)
model.add(Convolution1D(64, 3, padding='same'))
model.add(Convolution1D(32, 3, padding='same'))
model.add(Convolution1D(16, 3, padding='same'))
model.add(Flatten())
model.add(Dropout(0.2))
model.add(Dense(180, activation='sigmoid'))
model.add(Dropout(0.2))
model.add(Dense(1, activation='sigmoid'))
# Log to tensorboard
tensorBoardCallback = TensorBoard(log_dir='./logs', write_graph=True)
model.compile(loss='binary_crossentropy',
optimizer='adam', metrics=['accuracy'])

model.fit(X_train, y_train, epochs=3, callbacks=[
tensorBoardCallback], batch_size=64)

# Evaluation on the test set
scores = model.evaluate(X_test, y_test, verbose=0)
print("Accuracy: %.2f%%" % (scores[1]*100))