如何自己训练一个中文对话人工智能机器人？

可以使用seq2seq：

Seq2seq 是一种机器学习模型，用于处理序列到序列的任务，如机器翻译、自动问答和语音识别、文本摘要。Seq2seq 模型由两个网络组成：一个编码器和一个解码器。

编码器将输入序列转换为内部表示，解码器将内部表示转换为输出序列。

示例代码：

#!/usr/local/python3/bin/python3
# -*- coding: utf-8 -*
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Input, LSTM, Dense
import numpy as np
import pandas as pd

# 定义模型超参数、迭代次数、语料路径
#Batch size 的大小
batch_size = 32
# 迭代次数epochs
epochs = 100
# 编码空间的维度Latent dimensionality
latent_dim = 256
# 要训练的样本数
num_samples = 9
#设置语料的路径
data_path = '/data/wwwroot/default/dataset/ask/askbot.txt'

# 把语料向量化
input_texts = []
target_texts = []
input_characters = set()
target_characters = set()

with open(data_path, 'r', encoding='utf-8') as f:
    lines = f.read().split('\n')
for line in lines[: min(num_samples, len(lines))]:
    # print(line)
    input_text, target_text = line.split('|')
    target_text = target_text[0:100]
    target_text = '\t' + target_text + '\n'
    input_texts.append(input_text)
    target_texts.append(target_text)

    for char in input_text:
        if char not in input_characters:
            input_characters.add(char)
    for char in target_text:
        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_decoder_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)])

encoder_input_data = np.zeros(
    (len(input_texts), max_encoder_seq_leng...

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