keras实战教程三(文本生成)

 

 

仅仅作个记录。

文本生成想学习可以了解一下GPT2-Chinese 地址：https://github.com/Morizeyao/GPT2-Chinese

数据地址：https://github.com/wb14123/couplet-dataset/releases/download/1.0/couplet.tar.gz

模型介绍：模型的原理这里都不做介绍（有空可能会专门写个博客），可以自行查找资料。

数据读取

with open ("./couplet/train/in.txt","r") as f:
    data_in = f.read()
with open ("./couplet/train/out.txt","r") as f:
    data_out = f.read()    
data_in_list = data_in.split("\n")
data_out_list = data_out.split("\n")
data_in_list = [data.split() for data in data_in_list]
data_out_list = [data.split() for data in data_out_list]

构造字典

import itertools
words_all = list(itertools.chain.from_iterable(data_in_list))+list(itertools.chain.from_iterable(data_out_list))
words_all = set(words_all)
vocab = {j:i+1 for i ,j in enumerate(words_all)}
vocab["unk"] = 0

数据处理

from keras.preprocessing.sequence import pad_sequences
data_in2id = [[vocab.get(word,0) for word in sen] for sen in data_in_list]
data_out2id = [[vocab.get(word,0) for word in sen] for sen in data_out_list]
train_data = pad_sequences(data_in2id,100)
train_label = pad_sequences(data_out2id,100)
train_label_input = train_label.reshape(*train_label.shape, 1)

构造模型

from keras.models import Model,Sequential
from keras.layers import GRU, Input, Dense, TimeDistributed, Activation, RepeatVector, Bidirectional
from keras.layers import Embedding
from keras.optimizers import Adam
from keras.losses import sparse_categorical_crossentropy

def seq2seq_model(input_length,output_sequence_length,vocab_size):
    model = Sequential()
    model.add(Embedding(input_dim=vocab_size,output_dim = 128,input_length=input_length))
    model.add(Bidirectional(GRU(128, return_sequences = False)))
    model.add(Dense(128, activation="relu"))
    model.add(RepeatVector(output_sequence_length))
    model.add(Bidirectional(GRU(128, return_sequences = True)))
    model.add(TimeDistributed(Dense(vocab_size, activation = 'softmax')))
    model.compile(loss = sparse_categorical_crossentropy, 
                  optimizer = Adam(1e-3))
    model.summary()
    return model
model = seq2seq_model(train_data.shape[1],train_label.shape[1],len(vocab))

模型训练预测

model.fit(train_data,train_label_input, batch_size =32, epochs =1, validation_split = 0.2) 

import numpy as np
input_sen ="国破山河在"
char2id = [vocab.get(i,0) for i in input_sen]
input_data = pad_sequences([char2id],100)
result = model.predict(input_data)[0][-len(input_sen):]
result_label = [np.argmax(i) for i in result]
dict_res = {i:j for j,i in vocab.items()}
print([dict_res.get(i) for i in  result_label])

 参考 https://www.jianshu.com/p/d7a9dcd14456