写一个seq2seq的中文聊天机器人的python代码

以下是一个简单的基于seq2seq模型的中文聊天机器人的Python代码：

import numpy as np
import tensorflow as tf
from tensorflow.keras.layers import Input, LSTM, Dense
from tensorflow.keras.models import Model

# 定义超参数
batch_size = 64
epochs = 100
latent_dim = 256
num_samples = 10000

# 读取数据
input_texts = []
target_texts = []
input_characters = set()
target_characters = set()

with open('data/input.txt', 'r', encoding='utf-8') as f:
    lines = f.read().split('\n')
for line in lines[: min(num_samples, len(lines) - 1)]:
    input_text, target_text = line.split('\t')
    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)

# 构建字符索引映射表
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)])

# 构建编码器输入
encoder_input_data = np.zeros((len(input_texts), max_encoder_seq_length, num_encoder_tokens), dtype='float32')
for i, input_text in enumerate(input_texts):
    for t, char in enumerate(input_text):
        encoder_input_data[i, t, input_token_index[char]] = 1.0

# 构建解码器输入和输出
decoder_input_data = np.zeros((len(input_texts), max_decoder_seq_length, num_decoder_tokens), dtype='float32')
decoder_target_data = np.zeros((len(input_texts), max_decoder_seq_length, num_decoder_tokens), dtype='float32')
for i, target_text in enumerate(target_texts):
    for t, char in enumerate(target_text):
        decoder_input_data[i, t, target_token_index[char]] = 1.0
        if t > 0:
            decoder_target_data[i, t - 1, target_token_index[char]] = 1.0

# 构建模型
encoder_inputs = Input(shape=(None, num_encoder_tokens))
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, num_decoder_tokens))
decoder_lstm = LSTM(latent_dim, return_sequences=True, return_state=True)
decoder_outputs, _, _ = decoder_lstm(decoder_inputs, initial_state=encoder_states)
decoder_dense = Dense(num_decoder_tokens, activation='softmax')
decoder_outputs = decoder_dense(decoder_outputs)

model = Model([encoder_inputs, decoder_inputs], decoder_outputs)
model.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy'])

# 训练模型
model.fit([encoder_input_data, decoder_input_data], decoder_target_data,
          batch_size=batch_size,
          epochs=epochs,
          validation_split=0.2)

# 构建编码器模型
encoder_model = Model(encoder_inputs, encoder_states)

# 构建解码器模型
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)

# 定义反向字符索引映射表
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())

# 定义解码函数
def decode_sequence(input_seq):
    # 将输入编码
    states_value = encoder_model.predict(input_seq)

    # 生成空的目标序列
    target_seq = np.zeros((1, 1, num_decoder_tokens))
    target_seq[0, 0, target_token_index['\t']] = 1.0

    # 生成解码结果
    stop_condition = False
    decoded_sentence = ''
    while not stop_condition:
        output_tokens, h, c = decoder_model.predict([target_seq] + states_value)

        # 从词汇表中选择概率最高的字符
        sampled_token_index = np.argmax(output_tokens[0, -1, :])
        sampled_char = reverse_target_char_index[sampled_token_index]
        decoded_sentence += sampled_char

        # 如果达到终止条件，则退出循环
        if (sampled_char == '\n' or len(decoded_sentence) > max_decoder_seq_length):
            stop_condition = True

        # 更新目标序列和状态
        target_seq = np.zeros((1, 1, num_decoder_tokens))
        target_seq[0, 0, sampled_token_index] = 1.0
        states_value = [h, c]

    return decoded_sentence

# 测试模型
for i in range(10):
    input_seq = encoder_input_data[i:i+1]
    decoded_sentence = decode_sequence(input_seq)
    print('-' * 20)
    print('Input sentence:', input_texts[i])
    print('Decoded sentence:', decoded_sentence.strip())

其中，`data/input.txt`是一个包含输入和输出对的文本文件，每行一个对，输入和输出之间用制表符分隔。在代码中，我们首先读取输入和输出对，并构建字符索引映射表和编码器、解码器输入。然后，我们构建一个seq2seq模型，并训练它以学习输入和输出之间的映射。最后，我们使用训练好的模型对新的输入进行预测，并输出相应的响应。