变长词向量输入（Deprecated）

在keras中，使用model.add()进行LSTM层结构初始化时，如果设定inputshape=(None,输入向量序列中向量的维度)，即可实现向量序列的不定长度，即#time step可变，不用再对每个序列填充0向量,使所有序列长度相同了。

示例

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import LSTM, Dense
import numpy as np

model = Sequential()

model.add(LSTM(32, return_sequences=True, input_shape=(None, 2)))
model.add(LSTM(8, return_sequences=True))
model.add(Dense(1, activation='sigmoid'))

print(model.summary(90))

model.compile(loss='mean_squared_error',
              optimizer='adam')

X1=np.array([[[0.25908799, 0.96578602],
        [0.22886421, 0.16556086],
        [0.82094901, 0.69984487],
        [0.97888577, 0.76304284],
        [0.28470417, 0.11232793],
        [0.23395936, 0.14732181]]])
Y1=np.array([[[1]]])
X2=np.array([[[0.41400308, 0.48925297],
        [0.99921471, 0.6069814 ],
        [0.61261462, 0.7192767 ]]])
Y2=np.array([[[0.5]]])
model.fit(X1, Y1, steps_per_epoch=2, epochs=10, verbose=1)

但是，我个人不推荐这样做，因为如果这样使用变长的输入数据，在使用model.predict_generator()进行预测时，会遇到如下Bug。

ValueError                                Traceback (most recent call last)
<ipython-input-17-11e3da5819e6> in <module>()
----> 1 model.predict_generator(generatorX(X,wv_model),steps=10, verbose=1)

E:\Anaconda3\lib\site-packages\tensorflow\python\keras\engine\training.py in predict_generator(self, generator, steps, max_queue_size, workers, use_multiprocessing, verbose)
   2296         workers=workers,
   2297         use_multiprocessing=use_multiprocessing,
-> 2298         verbose=verbose)
   2299 
   2300   def _get_callback_model(self):

E:\Anaconda3\lib\site-packages\tensorflow\python\keras\engine\training_generator.py in predict_generator(model, generator, steps, max_queue_size, workers, use_multiprocessing, verbose)
    435       return all_outs[0][0]
    436     else:
--> 437       return np.concatenate(all_outs[0])
    438   if steps_done == 1:
    439     return [out[0] for out in all_outs]

ValueError: all the input array dimensions except for the concatenation axis must match exactly

这个Bug可以在github上找到，但是并没有在旧版本中修复。

使用NLTK的Lemmatizer对英文单词进行时态、语态变形和还原

如果要对英文文本进行处理，还要在word embedding之前进行stemming和lemmatization以便减少词典大小。比如将复数名词和动词comes, horses转化为词干come, hors。

示例

import nltk
ps=nltk.stem.PorterStemmer()
print(ps.stem("comes")) #come
print(ps.stem("horses")) #hors

如果只想把复数形式转化为单数形式，可以安装Pattern3库，使用如下命令安装

pip install Pattern3

如果在安装后使用时遇到如下错误：

File "/home/xxx/anaconda3/lib/python3.7/site-packages/pattern3/text/tree.py", line 37
    except:
         ^
IndentationError: expected an indented block

则编辑/home/xxx/anaconda3/lib/python3.7/site-packages/pattern3/text/tree.py这个文件，将

34行的：

from itertools import chain
try:
    
except:
    izip = zip  # Python 3

改为：

try:
    from itertools import chain
except:
    izip = zip  # Python 3

如果国内源速度慢，可以在~/.pip/ 下新建文件pip.conf，输入内容：

[global]
index-url = https://mirrors.aliyun.com/pypi/simple/

示例

from pattern3.en import singularize
print(singularize("comes"))
print(singularize("horses"))

更好的方案是使用NLTK进行lemmatize，它不仅可以改变单复数，还可以改变时态

from nltk.stem.wordnet import WordNetLemmatizer
words = ['gave','went','going','dating','comes','horses']
for word in words:
    print(word+"-->"+WordNetLemmatizer().lemmatize(word,'v'))
#gave-->give
#went-->go
#going-->go
#dating-->date
#comes-->come
#horses-->horse

精简Word2vec模型大小

在使用预训练好的word2vec模型时，常常会遇到极其heavy的模型，如GoogleNews-vectors-negative300.bin，在使用gensim.models.KeyedVectors.load_word2vec_format()加载入内存后使用了将近4个G的内存，在训练时很容易导致MemoryError。然而我们使用的语料库的词汇很可能没有这么多，这个模型中大部分的词汇我们可能都没有用到，因此有必要对模型进行精简，以下代码实现模型精简。

import gensim
import nltk
import csv
import numpy as np
tokenizer=nltk.tokenize.RegexpTokenizer(r'\w+')
def find_minimal_vocab(X):
    vocab = set()
    for Xi in X:
        for j in range(2):
            vocab = vocab.union(set(tokenizer.tokenize(Xi[j])))
    return vocab
def restrict_w2v(w2v, restricted_word_set):
    # minify the word vector model
    #w2v.init_sims()
    new_vectors = []
    new_vocab = {}
    new_index2entity = []
    new_vectors_norm = []

    for i in range(len(w2v.vocab)):
        word = w2v.index2entity[i]
        vec = w2v.vectors[i]
        vocab = w2v.vocab[word]
        #vec_norm = w2v.vectors_norm[i]
        if word in restricted_word_set:
            vocab.index = len(new_index2entity)
            new_index2entity.append(word)
            new_vocab[word] = vocab
            new_vectors.append(vec)
            #new_vectors_norm.append(vec_norm)

    w2v.vocab = new_vocab
    w2v.vectors = np.array(new_vectors)
    w2v.index2entity = new_index2entity
    w2v.index2word = new_index2entity
    #w2v.vectors_norm = new_vectors_norm
wv_model = gensim.models.KeyedVectors.load_word2vec_format(input("你要精简的模型文件路径及文件名: ", binary=True)
M=[]
with open(input("你的训练集路径及文件名："), newline='\n',encoding='utf8') as csvfile:
    reader = csv.reader(csvfile, delimiter='\t', quotechar='\t')
    for row in reader:
        M.append(row)
X=[i[5:7] for i in M]
Y=[i[4] for i in M]
del M

Md=[]
with open(input("你的开发集路径及文件名："), newline='\n',encoding='utf8') as csvfile:
    reader = csv.reader(csvfile, delimiter='\t', quotechar='\t')
    for row in reader:
        Md.append(row)
Xd=[i[5:7] for i in Md]
Yd=[i[4] for i in Md]
del Md

Mt=[]
with open(input("你的测试集路径及文件名："), newline='\n',encoding='utf8') as csvfile:
    reader = csv.reader(csvfile, delimiter='\t', quotechar='\t')
    for row in reader:
        Mt.append(row)
Xt=[i[5:7] for i in Mt]
Yt=[i[4] for i in Mt]
del Mt

v1 = find_minimal_vocab(X)
v2 = find_minimal_vocab(Xd)
v3 = find_minimal_vocab(Xt)
vocab = v1.union(v2,v3)
del v1
del v2
del v3
restrict_w2v(wv_model,vocab)
wv_model.save_word2vec_format(input("精简后的模型文件输出路径及文件名："), binary=True)