【发布时间】：2020-02-22 23:42:42
【问题描述】：
我在我的特定数据集上训练了自己的词嵌入模型，我想保存模型以供以后对未知数据集进行分类。但是，我尝试使用不同的格式保存模型，例如 (.txt,.mdl,.bin)。但是当我打开文件时，数据似乎有奇怪的字符。如何以与 Glove 和 word2vec 相同的格式保存我的模型，单词后跟它的向量..？
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这是我从https://machinelearningmastery.com/use-word-embedding-layers-deep-learning-keras/#comment-507619 运行的代码：
from numpy import array
from keras.preprocessing.text import one_hot
from keras.preprocessing.sequence import pad_sequences
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import Flatten
from keras.layers.embeddings import Embedding
# define documents
docs = ['Well done!',
'Good work',
'Great effort',
'nice work',
'Excellent!',
'Weak',
'Poor effort!',
'not good',
'poor work',
'Could have done better.']
# define class labels
labels = array([1,1,1,1,1,0,0,0,0,0])
# integer encode the documents
vocab_size = 50
encoded_docs = [one_hot(d, vocab_size) for d in docs]
print(encoded_docs)
# pad documents to a max length of 4 words
max_length = 4
padded_docs = pad_sequences(encoded_docs, maxlen=max_length, padding='post')
print(padded_docs)
# define the model
model = Sequential()
model.add(Embedding(vocab_size, 8, input_length=max_length))
model.add(Flatten())
model.add(Dense(1, activation='sigmoid'))
# compile the model
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
# summarize the model
print(model.summary())
# fit the model
model.fit(padded_docs, labels, epochs=50, verbose=0)
# evaluate the model
loss, accuracy = model.evaluate(padded_docs, labels, verbose=0)
print('Accuracy: %f' % (accuracy*100))
【问题讨论】：