简介
使用Keras实现Siamese Network并进行语句相似度的计算
原理
Siamese Network是指网络中包含两个或以上完全相同的子网络，多应用于语句相似度计算、人脸匹配、签名鉴别等任务上
- 语句相似度计算：输入两句话，判断是否是一个意思
- 人脸匹配：输入两张人脸，判断是否是同一个人
- 签名鉴别：输入两个签名，判断是否是同一个人所写
以语句相似度计算为例，两边的子网络从Embedding层到LSTM层等都是完全相同的，整个模型称作MaLSTM（Manhattan LSTM）
通过LSTM层的最后输出得到两句话的固定长度表示，再使用以下公式计算两者的相似度，相似度在0至1之间
数据
使用Kaggle上的Quora问题对数据，Quora对应外国的知乎，www.kaggle.com/c/quora-que…
训练集和测试集分别有404290和3563475条数据，每条数据包括以下字段，但测试集不包括is_duplicate字段
- id：问题对的id
- qid1：问题1的id
- qid2：问题2的id
- question1：问题1的文本
- question2：问题2的文本
- is_duplicate：两个问题是不是意思一样，0或1
实现
加载库
# -*- coding: utf-8 -*-
from keras.preprocessing.sequence import pad_sequences
from keras.models import Model
from keras.layers import Input, Embedding, LSTM, Lambda
import keras.backend as K
from keras.optimizers import Adam
import pandas as pd
import numpy as np
from gensim.models import KeyedVectors
from nltk.corpus import stopwords
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
%matplotlib inline
import re
from tqdm import tqdm
import pickle
加载训练集和测试集
train_df = pd.read_csv('train.csv')
test_df = pd.read_csv('test.csv')
print(len(train_df), len(test_df))
train_df.head()
加载nltk（Natural Language Toolkit）中的停用词，并定义一个文本预处理函数
# 如果报错nltk没有stopwords则下载
# import nltk
# nltk.download('stopwords')
stops = set(stopwords.words('english'))
def preprocess(text):
# input: 'Hello are you ok?'
# output: ['Hello', 'are', 'you', 'ok', '?']
text = str(text)
text = text.lower()
text = re.sub(r"[^A-Za-z0-9^,!.\/'+-=]", " ", text) # 去掉其他符号
text = re.sub(r"what's", "what is ", text) # 缩写
text = re.sub(r"\'s", " is ", text) # 缩写
text = re.sub(r"\'ve", " have ", text) # 缩写
text = re.sub(r"can't", "cannot ", text) # 缩写
text = re.sub(r"n't", " not ", text) # 缩写
text = re.sub(r"i'm", "i am ", text) # 缩写
text = re.sub(r"\'re", " are ", text) # 缩写
text = re.sub(r"\'d", " would ", text) # 缩写
text = re.sub(r"\'ll", " will ", text) # 缩写
text = re.sub(r",", " ", text) # 去除逗号
text = re.sub(r"\.", " ", text) # 去除句号
text = re.sub(r"!", " ! ", text) # 保留感叹号
text = re.sub(r"\/", " ", text) # 去掉右斜杠
text = re.sub(r"\^", " ^ ", text) # 其他符号
text = re.sub(r"\+", " + ", text) # 其他符号
text = re.sub(r"\-", " - ", text) # 其他符号
text = re.sub(r"\=", " = ", text) # 其他符号
text = re.sub(r"\'", " ", text) # 去掉单引号
text = re.sub(r"(\d+)(k)", r"\g<1>000", text) # 把30k等替换成30000
text = re.sub(r":", " : ", text) # 其他符号
text = re.sub(r" e g ", " eg ", text) # 其他词
text = re.sub(r" b g ", " bg ", text) # 其他词
text = re.sub(r" u s ", " american ", text) # 其他词
text = re.sub(r"\0s", "0", text) # 其他词
text = re.sub(r" 9 11 ", " 911 ", text) # 其他词
text = re.sub(r"e - mail", "email", text) # 其他词
text = re.sub(r"j k", "jk", text) # 其他词
text = re.sub(r"\s{2,}", " ", text) # 将多个空白符替换成一个空格
return text.split()
加载Google预训练好的300维词向量
word2vec = KeyedVectors.load_word2vec_format('GoogleNews-vectors-negative300.bin.gz', binary=True)
整理词典，一共有58564个词，将文本替换成整数序列表示，获得词向量映射矩阵
vocabulary = []
word2id = {}
id2word = {}
for df in [train_df, test_df]:
for i in tqdm(range(len(df))):
row = df.iloc[i]
for column in ['question1', 'question2']:
q2n = []
for word in preprocess(row[column]):
if word in stops or word not in word2vec.vocab:
continue
if word not in vocabulary:
word2id[word] = len(vocabulary) + 1
id2word[len(vocabulary) + 1] = word
vocabulary.append(word)
q2n.append(word2id[word])
else:
q2n.append(word2id[word])
df.at[i, column] = q2n
embedding_dim = 300
embeddings = np.random.randn(len(vocabulary) + 1, embedding_dim)
embeddings[0] = 0 # 零填充对应的词向量
for index, word in enumerate(vocabulary):
embeddings[index] = word2vec.word_vec(word)
del word2vec
print(len(vocabulary))
分割训练集和验证集，将整数序列padding到统一长度
maxlen = max(train_df.question1.map(lambda x: len(x)).max(),
train_df.question2.map(lambda x: len(x)).max(),
test_df.question1.map(lambda x: len(x)).max(),
test_df.question2.map(lambda x: len(x)).max())
valid_size = 40000
train_size = len(train_df) - valid_size
X = train_df[['question1', 'question2']]
Y = train_df['is_duplicate']
X_train, X_valid, Y_train, Y_valid = train_test_split(X, Y, test_size=valid_size)
X_train = {'left': X_train.question1.values, 'right': X_train.question2.values}
X_valid = {'left': X_valid.question1.values, 'right': X_valid.question2.values}
Y_train = np.expand_dims(Y_train.values, axis=-1)
Y_valid = np.expand_dims(Y_valid.values, axis=-1)
# 前向填充或截断
X_train['left'] = np.array(pad_sequences(X_train['left'], maxlen=maxlen))
X_train['right'] = np.array(pad_sequences(X_train['right'], maxlen=maxlen))
X_valid['left'] = np.array(pad_sequences(X_valid['left'], maxlen=maxlen))
X_valid['right'] = np.array(pad_sequences(X_valid['right'], maxlen=maxlen))
print(X_train['left'].shape, X_train['right'].shape)
print(X_valid['left'].shape, X_valid['right'].shape)
print(Y_train.shape, Y_valid.shape)
定义模型并训练
hidden_size = 128
gradient_clipping_norm = 1.25
batch_size = 64
epochs = 20
def exponent_neg_manhattan_distance(args):
left, right = args
return K.exp(-K.sum(K.abs(left - right), axis=1, keepdims=True))
left_input = Input(shape=(None,), dtype='int32')
right_input = Input(shape=(None,), dtype='int32')
embedding_layer = Embedding(len(embeddings), embedding_dim, weights=[embeddings], input_length=maxlen, trainable=False)
embedded_left = embedding_layer(left_input)
embedded_right = embedding_layer(right_input)
shared_lstm = LSTM(hidden_size)
left_output = shared_lstm(embedded_left)
right_output = shared_lstm(embedded_right)
malstm_distance = Lambda(exponent_neg_manhattan_distance, output_shape=(1,))([left_output, right_output])
malstm = Model([left_input, right_input], malstm_distance)
optimizer = Adam(clipnorm=gradient_clipping_norm)
malstm.compile(loss='mean_squared_error', optimizer=optimizer, metrics=['accuracy'])
history = malstm.fit([X_train['left'], X_train['right']], Y_train, batch_size=batch_size, epochs=epochs,
validation_data=([X_valid['left'], X_valid['right']], Y_valid))
绘制训练过程中的正确率曲线和损失函数曲线
# Plot Accuracy
plt.plot(history.history['acc'])
plt.plot(history.history['val_acc'])
plt.title('Model Accuracy')
plt.ylabel('Accuracy')
plt.xlabel('Epoch')
plt.legend(['Train', 'Validation'], loc='upper left')
plt.show()
# Plot Loss
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.title('Model Loss')
plt.ylabel('Loss')
plt.xlabel('Epoch')
plt.legend(['Train', 'Validation'], loc='upper right')
plt.show()
训练集损失不断降低，但验证集损失趋于平缓，说明模型泛化能力还不够
训练集正确率提升到了86%以上，而验证集正确率维持在80%左右，模型有待进一步改进
保存模型，以便后续使用
malstm.save('malstm.h5')
with open('data.pkl', 'wb') as fw:
pickle.dump({'word2id': word2id, 'id2word': id2word}, fw)
在单机上使用训练好的模型做个简单测试，从训练集中随机拿出一些样本，观察模型分类的结果是否和标签一致，主要是熟悉下如何应用模型进行推断
# -*- coding: utf-8 -*-
from keras.preprocessing.sequence import pad_sequences
from keras.models import Model, load_model
import pandas as pd
import numpy as np
from nltk.corpus import stopwords
import re
import pickle
with open('data.pkl', 'rb') as fr:
data = pickle.load(fr)
word2id = data['word2id']
id2word = data['id2word']
train_df = pd.read_csv('train.csv')
stops = set(stopwords.words('english'))
def preprocess(text):
# input: 'Hello are you ok?'
# output: ['Hello', 'are', 'you', 'ok', '?']
text = str(text)
text = text.lower()
text = re.sub(r"[^A-Za-z0-9^,!.\/'+-=]", " ", text) # 去掉其他符号
text = re.sub(r"what's", "what is ", text) # 缩写
text = re.sub(r"\'s", " is ", text) # 缩写
text = re.sub(r"\'ve", " have ", text) # 缩写
text = re.sub(r"can't", "cannot ", text) # 缩写
text = re.sub(r"n't", " not ", text) # 缩写
text = re.sub(r"i'm", "i am ", text) # 缩写
text = re.sub(r"\'re", " are ", text) # 缩写
text = re.sub(r"\'d", " would ", text) # 缩写
text = re.sub(r"\'ll", " will ", text) # 缩写
text = re.sub(r",", " ", text) # 去除逗号
text = re.sub(r"\.", " ", text) # 去除句号
text = re.sub(r"!", " ! ", text) # 保留感叹号
text = re.sub(r"\/", " ", text) # 去掉右斜杠
text = re.sub(r"\^", " ^ ", text) # 其他符号
text = re.sub(r"\+", " + ", text) # 其他符号
text = re.sub(r"\-", " - ", text) # 其他符号
text = re.sub(r"\=", " = ", text) # 其他符号
text = re.sub(r"\'", " ", text) # 去掉单引号
text = re.sub(r"(\d+)(k)", r"\g<1>000", text) # 把30k等替换成30000
text = re.sub(r":", " : ", text) # 其他符号
text = re.sub(r" e g ", " eg ", text) # 其他词
text = re.sub(r" b g ", " bg ", text) # 其他词
text = re.sub(r" u s ", " american ", text) # 其他词
text = re.sub(r"\0s", "0", text) # 其他词
text = re.sub(r" 9 11 ", " 911 ", text) # 其他词
text = re.sub(r"e - mail", "email", text) # 其他词
text = re.sub(r"j k", "jk", text) # 其他词
text = re.sub(r"\s{2,}", " ", text) # 将多个空白符替换成一个空格
return text.split()
malstm = load_model('malstm.h5')
correct = 0
for i in range(5):
print('Testing Case:', i + 1)
random_sample = dict(train_df.iloc[np.random.randint(len(train_df))])
left = random_sample['question1']
right = random_sample['question2']
print('Origin Questions...')
print('==', left)
print('==', right)
left = preprocess(left)
right = preprocess(right)
print('Preprocessing...')
print('==', left)
print('==', right)
left = [word2id[w] for w in left if w in word2id]
right = [word2id[w] for w in right if w in word2id]
print('To ids...')
print('==', left, [id2word[i] for i in left])
print('==', right, [id2word[i] for i in right])
left = np.expand_dims(left, 0)
right = np.expand_dims(right, 0)
maxlen = max(left.shape[-1], right.shape[-1])
left = pad_sequences(left, maxlen=maxlen)
right = pad_sequences(right, maxlen=maxlen)
print('Padding...')
print('==', left.shape)
print('==', right.shape)
pred = malstm.predict([left, right])
pred = 1 if pred[0][0] > 0.5 else 0
print('True:', random_sample['is_duplicate'])
print('Pred:', pred)
if pred == random_sample['is_duplicate']:
correct += 1
print(correct / 5)
参考
- How to predict Quora Question Pairs using Siamese Manhattan LSTM：github.com/eliorc/Medi…
- Siamese Recurrent Architectures for Learning Sentence Similarity：www.mit.edu/~jonasm/inf…
