如何自己训练一个中文对话人工智能机器人?
网友回复
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_length, num_encoder_tokens), dtype='float32') 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, (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_target_data is ahead of decoder_input_data by one timestep decoder_input_data[i, t, target_token_index[char]] = 1. if t > 0: # decoder_target_data will be ahead by one timestep # and will not include the start character. decoder_target_data[i, t - 1, target_token_index[char]] = 1. # LSTM_Seq2Seq 模型定义、训练和保存 encoder_inputs = Input(shape=(None, num_encoder_tokens)) encoder = LSTM(latent_dim, return_state=True) encoder_outputs, state_h, state_c = encoder(encoder_inputs) # 输出 `encoder_outputs` encoder_states = [state_h, state_c] # 状态 `encoder_states` 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) # 定义模型 model = Model([encoder_inputs, decoder_inputs], decoder_outputs) # 训练 model.compile(optimizer='rmsprop', loss='categorical_crossentropy') model.summary() model.fit([encoder_input_data, decoder_input_data], decoder_target_data, batch_size=batch_size, epochs=epochs, validation_split=0.2) # 保存模型 model.save('s2s.h5') # Seq2Seq 的 Encoder 操作 encoder_model = Model(encoder_inputs, encoder_states) 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) # 把索引和分词转成序列 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()) # 定义预测函数,先使用预模型预测,然后编码成汉字结果 def decode_sequence(input_seq): # Encode the input as state vectors. states_value = encoder_model.predict(input_seq) #print(states_value) # Generate empty target sequence of length 1. target_seq = np.zeros((1, 1, num_decoder_tokens)) # Populate the first character of target sequence with the start character. target_seq[0, 0, target_token_index['\t']] = 1. # Sampling loop for a batch of sequences # (to simplify, here we assume a batch of size 1). stop_condition = False decoded_sentence = '' while not stop_condition: output_tokens, h, c = decoder_model.predict( [target_seq] + states_value) # Sample a token 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) > max_decoder_seq_length): stop_condition = True # Update the target sequence (of length 1). target_seq = np.zeros((1, 1, num_decoder_tokens)) target_seq[0, 0, sampled_token_index] = 1. # 更新状态 states_value = [h, c] return decoded_sentence # 模型预测 def predict_ans(question): inseq = np.zeros((1, max_encoder_seq_length, num_encoder_tokens), dtype='float16') for t, char in enumerate(question): inseq[0, t, input_token_index[char]] = 1. decoded_sentence = decode_sequence(inseq) return decoded_sentence print('Decoded sentence:', predict_ans("在"))