记录下如何用RNN 的LSTM 来生成评语吧，python应用

需求分析

我们需要生成一个完整通顺的句子，用来点评书法的课程表现。
如图：

‘爱齐齐，是一个资质非常不错的男孩，多练字让他的手部控笔得到良好的训练，初期顿笔夸张异形也在练字中得到了改正。在小金熊培养出了对书写的兴趣，能够完全静心书写，很少被其他事情影响。让书写成为小齐齐最闪光的优点，一手漂亮的字能更让孩子更自信。相信再经过暑假的集训学习，到了一年级新班小齐齐的书写一定会惊艳全班、、’

实现逻辑

处理数据：

可以看到数据集中有很多垃圾的字符，运用正则去除。

content = re.sub('\[[^\]]*\]|\n', '', content)

• 再运用jieba库将中文分词

seg_list = jieba.cut(content, cut_all= False)str = " ".join(seg_list)

import pandas as pds
import re
from sklearn.feature_extraction.text import CountVectorizer
import jiebadata = pds.read_excel('./comment.xlsx')
arr = []
for content in data.content:char = '\n'list=[i.start() for i in re.finditer(char, content)]if len(list) > 3:content = content[list[2]:]content = re.sub('\[[^\]]*\]|\n', '', content)seg_list = jieba.cut(content, cut_all= False)str = " ".join(seg_list)arr.append(str)

• 查看数据
  

将数据数字化

• 生成语料库：相当于字典

import tensorflow.keras as keras
from tensorflow.keras.preprocessing.text import Tokenizertokenizer = Tokenizer(num_words= 500, filters='!"#$%&()*+,-./:;<=>?@[\\]^_`{|}~\t\n')
tokenizer.fit_on_texts(arr)

• 将数据转成数值表示

token_list = tokenizer.texts_to_sequences([line])[0]

• 将每个句子转化 成回归模式：所以要把句子转化成
  
  32
  12 32
  1 12 32
  用于训练
• 将数据长度归一化。运用pad_sequences

input_sentences = np.array(pad_sequences(input_sentences, maxlen=max_sentences_len, padding='pre'))

代码：

import numpy as np  
from keras.preprocessing.sequence import pad_sequences
input_sentences = []
for line in arr:token_list = tokenizer.texts_to_sequences([line])[0]for i in range(1,len(token_list)):n_pre_sequences = token_list[:i+1]input_sentences.append(n_pre_sequences)
max_sentences_len = max([len(x) for x in input_sentences])
input_sentences = np.array(pad_sequences(input_sentences, maxlen=max_sentences_len, padding='pre'))
input_sentences

构建训练集

xs = input_sentences[:,:-1]
labels = input_sentences[:,-1]

• 将标签转成稀疏矩阵

from tensorflow.keras.utils import to_categorical
ys = to_categorical(labels, num_classes=total_words)

构建模型

model = keras.Sequential()
model.add(keras.layers.Embedding(total_words, 64, input_length=max_sentences_len - 1))
model.add(keras.layers.Bidirectional(keras.layers.LSTM(20)))
model.add(keras.layers.Dense(total_words, activation='softmax'))
model.compile(loss = keras.losses.categorical_crossentropy, optimizer='adam', metrics=['accuracy'])

x训练模型

history = model.fit(xs, ys, epochs=200, verbose=1)

预测句子方法

def pre_text(seed_text, next_words):for _ in range(1, next_words):token_list = tokenizer.texts_to_sequences([seed_text])[0]token_list = pad_sequences([token_list], maxlen=max_sentences_len - 1, padding='pre')predicted = model.predict(token_list, verbose=0)pre_index = np.argmax(predicted[0])for word, index in tokenizer.word_index.items():if index == pre_index:seed_text += " " +wordbreakreturn seed_text.replace(" ", '')

测试

print(pre_text('宝贝', 100))
pre_text('一学期', 100)

宝贝，是一个资质非常不错的男孩，多练字让他的手部控笔得到良好的训练，初期顿笔夸张异形也在练字中得到了改正。在小金熊培养出了对书写的兴趣，能够完全静心书写，很少被其他事情影响。让书写成为小齐齐最闪光的优点，一手漂亮的字能更让孩子更自信。相信再经过暑假的集训学习，到了一年级新班小齐齐的书写一定会惊艳全班、、。

‘一学期乐乐的进步不是一点点，感谢妈妈的信任与坚持，还记得第一次课一个笔画都歪歪扭扭，现在可以整张冒满泡泡，懿杰也更开心、更自信了建议，趁热打铁，赶紧多练。在我们在小金熊课程每次都开开心心，每次是她的机灵的，让他的。书写基础。书写的很小气、扭扭捏捏到现在字型结构正确、很大气，真的进步不小，熟能生巧，书写速度还有待提升多锻炼手部全班的力量’

总结：

基本功能实现，由于数据太少，所以预测的很多都是重复的，下一步就是收集更多的数据进行训练。

完整代码：

import pandas as pds
import re
from sklearn.feature_extraction.text import CountVectorizer
import jiebadata = pds.read_excel('./comment.xlsx')
arr = []
for content in data.content:char = '\n'list=[i.start() for i in re.finditer(char, content)]if len(list) > 3:content = content[list[2]:]content = re.sub('\[[^\]]*\]|\n', '', content)seg_list = jieba.cut(content, cut_all= False)str = " ".join(seg_list)arr.append(str)import tensorflow.keras as keras
from tensorflow.keras.preprocessing.text import Tokenizertokenizer = Tokenizer(num_words= 500, filters='!"#$%&()*+,-./:;<=>?@[\\]^_`{|}~\t\n')
tokenizer.fit_on_texts(arr)total_words = len(tokenizer.word_index) + 1import numpy as np  
from keras.preprocessing.sequence import pad_sequences
input_sentences = []
for line in arr:token_list = tokenizer.texts_to_sequences([line])[0]for i in range(1,len(token_list)):n_pre_sequences = token_list[:i+1]input_sentences.append(n_pre_sequences)
max_sentences_len = max([len(x) for x in input_sentences])
input_sentences = np.array(pad_sequences(input_sentences, maxlen=max_sentences_len, padding='pre'))
input_sentencesxs = input_sentences[:,:-1]
labels = input_sentences[:,-1]from tensorflow.keras.utils import to_categorical
ys = to_categorical(labels, num_classes=total_words)
model = keras.Sequential()
model.add(keras.layers.Embedding(total_words, 64, input_length=max_sentences_len - 1))
model.add(keras.layers.Bidirectional(keras.layers.LSTM(20)))
model.add(keras.layers.Dense(total_words, activation='softmax'))
model.compile(loss = keras.losses.categorical_crossentropy, optimizer='adam', metrics=['accuracy'])history = model.fit(xs, ys, epochs=200, verbose=1)
# def pre_text(seed_text, next_words):for _ in range(1, next_words):token_list = tokenizer.texts_to_sequences([seed_text])[0]token_list = pad_sequences([token_list], maxlen=max_sentences_len - 1, padding='pre')predicted = model.predict(token_list, verbose=0)pre_index = np.argmax(predicted[0])for word, index in tokenizer.word_index.items():if index == pre_index:seed_text += " " +wordbreakreturn seed_text.replace(" ", '')
print(pre_text('宝贝', 100))
pre_text('一学期', 100)