中文聊天机器人（一）
一、数据预处理
1.定义预处理参数，文件data_util.py
from keras.models import Model
from keras.layers import Input,LSTM,Dense
import numpy as np
import pandas as pd
num_samples = 100000
# 定义路径
question_path = 'question.txt'
answer_path = 'answer.txt'
max_encoder_seq_length = None
max_decoder_seq_length = None
num_encoder_tokens = None
num_decoder_tokens = None2.获取训练数据X, Y
def get_xy_data():
input_texts = []
target_texts = []
with open(question_path, 'r', encoding='utf-8') as f:
input_texts = f.read().split('\n')
input_texts = input_texts[:min(num_samples,len(input_texts)-1)]
with open(answer_path, 'r', encoding='utf-8') as f:
target_texts = ['\t' + line + '\n' for line in f.read().split('\n')]
target_texts = target_texts[:min(num_samples,len(input_texts)-1)]
return input_texts, target_texts3.需要将Input数据向量化，这里根据Input数据X, Y获取字符词典
def get_vocab_dict(X, Y):
global max_encoder_seq_length, max_decoder_seq_length, num_encoder_tokens, num_decoder_tokens
input_texts = X
target_texts = Y
input_characters = set()
target_characters = set()
for line in input_texts[:min(num_samples,len(input_texts)-1)]:
for char in line:
if char not in input_characters:
input_characters.add(char)
for line in target_texts[:min(num_samples,len(target_texts)-1)]:
for char in line:
if char not in target_characters:
target_characters.add(char)
input_characters = sorted(list(input_characters))
target_characters = sorted(list(target_characters))
num_encoder_tokens = len(input_characters)
num_decoder_tokens = len(target_characters)
max_encoder_seq_length = max([len(txt) for txt in input_texts])
max_decoder_seq_length = max([len(txt) for txt in target_texts])
print('Number of samples:', len(input_texts))
print('Number of unique input tokens:', num_encoder_tokens)
print('Number of unique output tokens:', num_decoder_tokens)
print('Max sequence length for inputs:', max_encoder_seq_length)
print('Max sequence length for outputs:', max_encoder_seq_length)
input_token_index = dict(
[(char,i) for i, char in enumerate(input_characters)])
target_token_index = dict(
[(char,i) for i, char in enumerate(target_characters)])
return input_token_index, target_token_index4.需要建立一个逆转词典，用于预测阶段将向量转化为可识别的字符
def get_rev_dict(input_token_index, target_token_index):
reverse_input_char_index = dict(
(i, char) for char, i in input_token_index.items())
reverse_target_char_index = dict(
(i, char) for char, i in target_token_index.items())
return reverse_input_char_index, reverse_target_char_index二、训练
1.定义参数
from keras.models import Model
from keras.layers import Input,LSTM,Dense
import numpy as np
import pandas as pd
import data_util
from data_util import get_vocab_dict
from data_util import get_xy_data
# 定义超参数
batch_size = 32
epochs = 100
latent_dim = 256
input_texts = []
target_texts = []
input_token_index = []
target_token_index = []
encoder_input_data = None
decoder_input_data = None
decoder_target_data = None2.调用预处理 data_util.py 得到训练数据和词典
def data_deal():
global encoder_input_data,decoder_input_data,decoder_target_data
global input_texts, target_texts, input_token_index,target_token_index
input_texts, target_texts = get_xy_data()
input_token_index, target_token_index = get_vocab_dict(input_texts, target_texts)
# 每个input_text句子都是一个二维矩阵，
# 那么input_texts是多个二维矩阵组合的三维矩阵
encoder_input_data = np.zeros(
(len(input_texts), data_util.max_encoder_seq_length, len(input_token_index)),dtype='float32')
decoder_input_data = np.zeros(
(len(input_texts), data_util.max_decoder_seq_length, len(target_token_index)),dtype='float32')
decoder_target_data = np.zeros(
(len(input_texts), data_util.max_decoder_seq_length, len(target_token_index)),dtype='float32')
for i, (input_text, target_text) in enumerate(zip(input_texts, target_texts)):
for t, char in enumerate(input_text):
encoder_input_data[i, t, input_token_index[char]] = 1
for t, char in enumerate(target_text):
decoder_input_data[i, t, target_token_index[char]] = 1
if t > 0:
decoder_target_data[i, t-1, target_token_index[char]] =13.建立seq2seq模型
def build_model():
global input_token_index,target_token_index
encoder_inputs = Input(shape=(None, len(input_token_index)))
encoder = LSTM(latent_dim, return_state=True)
encoder_outputs, state_h, state_c = encoder(encoder_inputs)
encoder_states = [state_h, state_c]
decoder_inputs = Input(shape=(None, len(target_token_index)))
decoder_lstm = LSTM(latent_dim, return_sequences=True,return_state=True)
decoder_outputs, _, _ = decoder_lstm(decoder_inputs,
initial_state=encoder_states)
decoder_dense = Dense(len(target_token_index), activation='softmax')
decoder_outputs = decoder_dense(decoder_outputs)
model = Model([encoder_inputs, decoder_inputs], decoder_outputs)
# 新序列预测时需要的encoder
encoder_model = Model(encoder_inputs, encoder_states)
# 新序列预测时需要的decoder
decoder_state_input_h = Input(shape=(latent_dim,))
decoder_state_input_c = Input(shape=(latent_dim,))
decoder_states_inputs = [decoder_state_input_h, decoder_state_input_c]
decoder_outputs, state_h, state_c = decoder_lstm(decoder_inputs, initial_state=decoder_states_inputs)
decoder_states = [state_h, state_c]
decoder_outputs = decoder_dense(decoder_outputs)
decoder_model = Model([decoder_inputs] + decoder_states_inputs, [decoder_outputs] + decoder_states)
return model, encoder_model, decoder_model4.训练模型并保存
# 训练并保存
if __name__ == "__main__":
data_deal()
model,encoder_model,decoder_model = build_model()
model.compile(optimizer='rmsprop',loss='categorical_crossentropy')
model.fit([encoder_input_data,decoder_input_data],decoder_target_data,
batch_size=batch_size,
epochs=epochs,
validation_split=0.2)
model.save('model.h5')
encoder_model.save('encoder_model.h5')
decoder_model.save('decoder_model.h5')三、预测
1.定义参数
from keras.models import Model,load_model
from keras.layers import Input,LSTM,Dense
import numpy as np
import pandas as pd
from data_util import get_vocab_dict
from data_util import get_xy_data
from data_util import get_rev_dict
import data_util
latent_dim = 256
# 语料向量化
input_texts = []
target_texts = []
input_token_index = []
target_token_index = []2.开始预测
# 开始inference
def decoder_sequence(input_seq):
# Encode the input as state vectors
states_value = encoder_model.predict(input_seq)
target_seq = np.zeros((1,1,data_util.num_decoder_tokens))
# '\t' is starting character
target_seq[0,0,target_token_index['\t']] = 1
# Sampling loop for a batch of sequences
stop_condition = False
decoded_sentence= ''
while not stop_condition:
output_tokens, h, c = decoder_model.predict(
[target_seq] + states_value)
sampled_token_index = np.argmax(output_tokens[0,-1,:])
sampled_char = reverse_target_char_index[sampled_token_index]
decoded_sentence += sampled_char
if(sampled_char == '\n' or len(decoded_sentence) > data_util.max_decoder_seq_length):
stop_condition = True
# Update the target sequenco to predict next token
target_seq = np.zeros((1,1,data_util.num_decoder_tokens))
target_seq[0,0,sampled_token_index] = 1
# Update state
states_value = [h, c]
return decoded_sentence
def predict_ans(question):
input_seq = np.zeros((1, data_util.max_encoder_seq_length, data_util.num_encoder_tokens),dtype='float16')
for t, char in list(enumerate(question)):
input_seq[0,t,input_token_index[char]] = 1
decoded_sentence = decoder_sequence(input_seq)
return decoded_sentence
if __name__ == "__main__":
input_texts, target_texts = get_xy_data()
input_token_index, target_token_index = get_vocab_dict(input_texts, target_texts)
reverse_input_char_index, reverse_target_char_index = get_rev_dict(input_token_index, target_token_index)
encoder_model = load_model('encoder_model.h5')
decoder_model = load_model('decoder_model.h5')
print('Decoded sentence:', predict_ans('这是个傻子'))github：项目链接