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seq2seq模型即通过序列预测序列，但是相对于传统单一深度学习系统，如CNN或者RNN，这些模型的输入输出都是固定的长度，比如图像识别中图像的大小。但是对于机器翻译或者语音对话而言，由于输入的序列文本大小可变，预测输出也是可变的，因而这种单一的格式很难适应。因此提出了seq2seq模型，这是一种编解码架构模型（encoder-decoder）

大体原理个人解释：

• 对于输入序列，假设序列长度统一为10；如：0123456789
• 第一步encoder，当我们对其使用encoder时，设其循环网络单元个数为50个，输出即50个状态，这些都将作为解码器decoder的隐藏状态输入，即解码器的先验知识。
• 第二步解码，解码部分的输入由两部分组成：encoder的隐藏状态输出state和解码器的输入。注意这里的解码器输入由于使用了目标序列——即最终结果序列。所以训练时的解码输入和预测时的有所不同。
  • 训练时，为了更快的训练，也为了防止误差，使用了目标序列作为先验条件。
    
  • 预测时，由于我们并不知道目标序列，所以这里的输入使用前一刻的输出作为输入，初始化为一个固定值。
    

代码来源：

详细步骤：

"""@author: zoutai@file: seq2seq.py @time: 2018/09/25 @description: 基本seq2seq系统"""from distutils.version import LooseVersionimport tensorflow as tffrom tensorflow.python.layers.core import Denseimport os# Check TensorFlow Versionassert LooseVersion(tf.__version__) >= LooseVersion('1.1'), 'Please use TensorFlow version 1.1 or newer'print('TensorFlow Version: {}'.format(tf.__version__))import numpy as npimport tensorflow as tf# 读取数据with open('data/letters_source.txt', 'r', encoding='utf-8') as f:    source_data = f.read()with open('data/letters_target.txt', 'r', encoding='utf-8') as f:    target_data = f.read()# 预览数据print(source_data.split('\n')[:10],target_data.split('\n')[:10])# 数据预处理def extract_character_vocab(data):    '''    构造映射表：    PAD用于填充    UNK表示未知单词，比如你超过 VOCAB_SIZE范围的则认为未知单词，    GO表示decoder开始的符号，    EOS表示回答结束的符号，    '''    special_words = ['
   
    ', '
    
     ', '
     
      ',  '
      
       ']    set_words = list(set([character for line in data.split('\n') for character in line]))    # 这里要把四个特殊字符添加进词典    int_to_vocab = {
    idx: word for idx, word in enumerate(special_words + set_words)}    vocab_to_int = {
    word: idx for idx, word in int_to_vocab.items()}    return int_to_vocab, vocab_to_int# 构造映射表source_int_to_letter, source_letter_to_int = extract_character_vocab(source_data)target_int_to_letter, target_letter_to_int = extract_character_vocab(target_data)# 对字母进行转换：'''dict.get(key, default=None)参数：    key - - 字典中要查找的键。    default - - 如果指定键的值不存在时，返回该默认值值。'''source_int = [[source_letter_to_int.get(letter, source_letter_to_int['
       
        '])               for letter in line] for line in source_data.split('\n')]target_int = [[target_letter_to_int.get(letter, target_letter_to_int['
        
         ']) for letter in line] + [target_letter_to_int['
         
          ']] for line in target_data.split('\n')]print(source_int[:10],target_int[:10])## 构建模型# 1.输入层def get_inputs(): ''' 模型输入tensor ''' inputs = tf.placeholder(tf.int32, [None, None], name='inputs') targets = tf.placeholder(tf.int32, [None, None], name='targets') learning_rate = tf.placeholder(tf.float32, name='learning_rate') # 定义target序列最大长度（之后target_sequence_length和source_sequence_length会作为feed_dict的参数） target_sequence_length = tf.placeholder(tf.int32, (None,), name='target_sequence_length') max_target_sequence_length = tf.reduce_max(target_sequence_length, name='max_target_len') source_sequence_length = tf.placeholder(tf.int32, (None,), name='source_sequence_length') return inputs, targets, learning_rate, target_sequence_length, max_target_sequence_length, source_sequence_length# 2.Encoder编码部分# 在Encoder端，我们需要进行两步，第一步要对我们的输入进行Embedding，再把Embedding以后的向量传给RNN进行处理。# 在Embedding中，我们使用[tf.contrib.layers.embed_sequence](https://www.tensorflow.org/api_docs/python/tf/contrib/layers/embed_sequence)，# 它会对每个batch执行embedding操作。def get_encoder_layer(input_data, rnn_size, num_layers, source_sequence_length, source_vocab_size, encoding_embedding_size): ''' 构造Encoder层 参数说明： - input_data: 输入tensor - rnn_size: rnn隐层结点数量 - num_layers: 堆叠的rnn cell数量 - source_sequence_length: 源数据的序列长度 - source_vocab_size: 源数据的词典大小 - encoding_embedding_size: embedding的大小 ''' # Encoder embedding # returns: [batch_size, doc_length, embed_dim] - [128,7,15]/[?,?,15] encoder_embed_input = tf.contrib.layers.embed_sequence(input_data, source_vocab_size, encoding_embedding_size) # RNN cell def get_lstm_cell(rnn_size): lstm_cell = tf.contrib.rnn.LSTMCell(rnn_size, initializer=tf.random_uniform_initializer(-0.1, 0.1, seed=2)) return lstm_cell cell = tf.contrib.rnn.MultiRNNCell([get_lstm_cell(rnn_size) for _ in range(num_layers)]) # encoder_output = [batch_size, max_time, cell.output_size] - [128,256,50] 相当于特征扩大 # encoder_state = [batch_size, cell.state_size] - [128,50] # 参考：https://blog.csdn.net/u010223750/article/details/71079036 # [?,?,50]-[?,50] encoder_output, encoder_state = tf.nn.dynamic_rnn(cell, encoder_embed_input, sequence_length=source_sequence_length, dtype=tf.float32) return encoder_output, encoder_state## 3.Decoder 解码器输入# 对target数据进行预处理def process_decoder_input(data, vocab_to_int, batch_size): ''' 补充
          
           ，并移除最后一个字符 ''' # cut掉最后一个字符 # 参考：https://blog.csdn.net/banana1006034246/article/details/75092388 # 假设data=[128,256] -> [128,255] ending = tf.strided_slice(data, [0, 0], [batch_size, -1], [1, 1]) # [128,256] = [128,1] + [128,255] decoder_input = tf.concat([tf.fill([batch_size, 1], vocab_to_int['
           
            ']), ending], 1) return decoder_input## 4.解码层# 对数据进行embedding# 同样地，我们还需要对target数据进行embedding，使得它们能够传入Decoder中的RNN。# Dense的说明在https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/layers/core.pydef decoding_layer(target_letter_to_int, decoding_embedding_size, num_layers, rnn_size, target_sequence_length, max_target_sequence_length, encoder_state, decoder_input): ''' 构造Decoder层 参数： - target_letter_to_int: target数据的映射表 - decoding_embedding_size: embed向量大小 - num_layers: 堆叠的RNN单元数量 - rnn_size: RNN单元的隐层结点数量 - target_sequence_length: target数据序列长度 - max_target_sequence_length: target数据序列最大长度 - encoder_state: encoder端编码的状态向量 - decoder_input: decoder端输入 ''' # 1. Embedding target_vocab_size = len(target_letter_to_int) # 假设target_vocab_size=30,字母种类26+4=30 # 生成随机嵌入矩阵[30,15] decoder_embeddings = tf.Variable(tf.random_uniform([target_vocab_size, decoding_embedding_size])) # 得到映射嵌入[128,?,15]/[128,length,15] = lookup([30,15],[128,legth]) decoder_embed_input = tf.nn.embedding_lookup(decoder_embeddings, decoder_input) # 2. 构造Decoder中的RNN单元 def get_decoder_cell(rnn_size): # rnn_size = 50 decoder_cell = tf.contrib.rnn.LSTMCell(rnn_size, initializer=tf.random_uniform_initializer(-0.1, 0.1, seed=2)) return decoder_cell # 多层RNN cell = tf.contrib.rnn.MultiRNNCell([get_decoder_cell(rnn_size) for _ in range(num_layers)]) # 3. Output全连接层 # 细胞数为target_vocab_size=30 output_layer = Dense(target_vocab_size, kernel_initializer=tf.truncated_normal_initializer(mean=0.0, stddev=0.1)) # 4. Training decoder with tf.variable_scope("decode"): # 得到help对象 training_helper = tf.contrib.seq2seq.TrainingHelper(inputs=decoder_embed_input, sequence_length=target_sequence_length, time_major=False) # 构造decoder training_decoder = tf.contrib.seq2seq.BasicDecoder(cell, training_helper, encoder_state, output_layer) # returns: (final_outputs, final_state, final_sequence_lengths) # final_outputs是一个namedtuple，里面包含两项(rnn_outputs, sample_id) # rnn_output: [batch_size, decoder_targets_length, vocab_size]，保存decode每个时刻每个单词的概率矩阵，可以用来计算loss # sample_id: [batch_size], tf.int32，保存最终的编码结果。可以表示最后的答案（最可能的路径） # decoder_targets_length 即真是单词的长度，比如cmmoon为6；30代表每个细胞返回的概率 # [128,?,30]-[128,?] training_decoder_output, _ , _ = tf.contrib.seq2seq.dynamic_decode(training_decoder, impute_finished=True, maximum_iterations=max_target_sequence_length) # 5. Predicting decoder # 与training共享参数 with tf.variable_scope("decode", reuse=True): # 创建一个常量tensor并复制为batch_size的大小 start_tokens = tf.tile(tf.constant([target_letter_to_int['
            
             ']], dtype=tf.int32), [batch_size], name='start_tokens') predicting_helper = tf.contrib.seq2seq.GreedyEmbeddingHelper(decoder_embeddings, start_tokens, target_letter_to_int['
             
              ']) predicting_decoder = tf.contrib.seq2seq.BasicDecoder(cell, predicting_helper, encoder_state, output_layer) predicting_decoder_output, _, _ = tf.contrib.seq2seq.dynamic_decode(predicting_decoder, impute_finished=True, maximum_iterations=max_target_sequence_length) return training_decoder_output, predicting_decoder_output# Seq2Seq# 上面已经构建完成Encoder和Decoder，下面将这两部分连接起来，构建seq2seq模型def seq2seq_model(input_data, targets, lr, target_sequence_length, max_target_sequence_length, source_sequence_length, source_vocab_size, target_vocab_size, encoder_embedding_size, decoder_embedding_size, rnn_size, num_layers): # 2.获取encoder的状态输出 # [128,50]/[?,50] 128批，每个单词每个cell输出一个节点，共50个cell _, encoder_state = get_encoder_layer(input_data, rnn_size, num_layers, source_sequence_length, source_vocab_size, encoding_embedding_size) # 3.预处理后的decoder输入 # [128,?]/[128,7] decoder_input = process_decoder_input(targets, target_letter_to_int, batch_size) # 4.将状态向量与输入传递给decoder training_decoder_output, predicting_decoder_output = decoding_layer(target_letter_to_int, decoding_embedding_size, num_layers, rnn_size, target_sequence_length, max_target_sequence_length, encoder_state, decoder_input) return training_decoder_output, predicting_decoder_output# 超参数# Number of Epochsepochs = 60# Batch Sizebatch_size = 128# RNN Sizernn_size = 50# Number of Layersnum_layers = 2# Embedding Sizeencoding_embedding_size = 15decoding_embedding_size = 15# Learning Ratelearning_rate = 0.001save_path = '/home/zoutai/code/Exercise/tensorflow-exercises/basic_seq2seq/checkpoints'# 构造graphtrain_graph = tf.Graph()## 定义模型运行结构with train_graph.as_default(): # 1.获得模型输入 input_data, targets, lr, target_sequence_length, max_target_sequence_length, source_sequence_length = get_inputs() # 234.建立模型 # predicting_decoder_output并没有使用？ # [128,?,30] [128,?] training_decoder_output, predicting_decoder_output = seq2seq_model(input_data, targets, lr, target_sequence_length, max_target_sequence_length, source_sequence_length, len(source_letter_to_int), len(target_letter_to_int), encoding_embedding_size, decoding_embedding_size, rnn_size, num_layers) # tf.identity就是为了在图上显示/获取这个值而创建的虚拟节点 training_logits = tf.identity(training_decoder_output.rnn_output, 'logits') predicting_logits = tf.identity(predicting_decoder_output.sample_id, name='predictions') print(training_logits,predicting_logits) masks = tf.sequence_mask(target_sequence_length, max_target_sequence_length, dtype=tf.float32, name='masks') # 5.定义优化器 with tf.name_scope("optimization"): # Loss function # logits：尺寸[batch_size, sequence_length, num_decoder_symbols] # targets：尺寸[batch_size, sequence_length]，不用做one_hot。 # weights：[batch_size, sequence_length]，即mask，滤去padding的loss计算，使loss计算更准确。 # 注意这里的logits是概率矩阵，所以不需要维数相同，sequence_loss会自动计算误差值 # 计算加权交叉熵损失 # 交叉熵原理参考：https://blog.csdn.net/chaipp0607/article/details/73392175 cost = tf.contrib.seq2seq.sequence_loss( training_logits, targets, masks) # Optimizer optimizer = tf.train.AdamOptimizer(lr) # Gradient Clipping 梯度裁剪：约束梯度值，防止梯度爆炸/跨度过大 # 1.计算梯度 # Returns: A list of (gradient, variable) # 参考：https://blog.csdn.net/jetFlow/article/details/80161354 gradients = optimizer.compute_gradients(cost) capped_gradients = [(tf.clip_by_value(grad, -5., 5.), var) for grad, var in gradients if grad is not None] # 2.调用apply_gradients更新网络 # 将梯度应用在变量上，返回此操作。即：使用该梯度进行权重更新 train_op = optimizer.apply_gradients(capped_gradients)## Batchesdef pad_sentence_batch(sentence_batch, pad_int): ''' 对batch中的序列进行补全，保证batch中的每行都有相同的sequence_length 参数： - sentence batch - pad_int: 
              
               对应索引号 ''' max_sentence = max([len(sentence) for sentence in sentence_batch]) return [sentence + [pad_int] * (max_sentence - len(sentence)) for sentence in sentence_batch]def get_batches(targets, sources, batch_size, source_pad_int, target_pad_int): ''' 定义生成器，用来获取batch，每次获取一批数据 ''' for batch_i in range(0, len(sources) // batch_size): start_i = batch_i * batch_size sources_batch = sources[start_i:start_i + batch_size] targets_batch = targets[start_i:start_i + batch_size] # 补全序列 pad_sources_batch = np.array(pad_sentence_batch(sources_batch, source_pad_int)) pad_targets_batch = np.array(pad_sentence_batch(targets_batch, target_pad_int)) # 记录每条记录的长度 targets_lengths = [] for target in targets_batch: targets_lengths.append(len(target)) source_lengths = [] for source in sources_batch: source_lengths.append(len(source)) # 这里使用yield代替return从而节约内存使用，因为yield使用生成模式，即迭代模式，相当于一个迭代器iterator # 每次返回下一个元素，而不是等所有元素生成完一起返回，大大降低内存使用 # 参考：https://www.ibm.com/developerworks/cn/opensource/os-cn-python-yield/index.html yield pad_targets_batch, pad_sources_batch, targets_lengths, source_lengths## 训练def train(): ## Train # 将数据集分割为train和validation；第一批作为验证，后面的作为训练 train_source = source_int[batch_size:] train_target = target_int[batch_size:] # 留出一个batch进行验证 valid_source = source_int[:batch_size] valid_target = target_int[:batch_size] # next函数参考：http://www.runoob.com/python/python-func-next.html (valid_targets_batch, valid_sources_batch, valid_targets_lengths, valid_sources_lengths) = next( get_batches(valid_target, valid_source, batch_size, source_letter_to_int['
               
                '], target_letter_to_int['
                
                 '])) display_step = 50 # 每隔50轮输出loss checkpoint = "trained_model.ckpt" # 添加使用GPU os.environ["CUDA_VISIBLE_DEVICES"] = '-1' # use GPU with ID=0 ;use CPU with ID = -1 config = tf.ConfigProto() config.gpu_options.per_process_gpu_memory_fraction = 0.98 # maximun alloc gpu98% of MEM config.gpu_options.allow_growth = True # allocate dynamically with tf.Session(graph=train_graph,config=config) as sess: # 初始化全局变量 sess.run(tf.global_variables_initializer()) # 训练次数60 for epoch_i in range(1, epochs + 1): # 分批训练10000/128-1=78.125-1=77 for batch_i, (targets_batch, sources_batch, targets_lengths, sources_lengths) in enumerate( get_batches(train_target, train_source, batch_size, source_letter_to_int['
                 
                  '], target_letter_to_int['
                  
                   '])): t_logits = sess.run( [training_logits,targets], { input_data: sources_batch, targets: targets_batch, lr: learning_rate, target_sequence_length: targets_lengths, source_sequence_length: sources_lengths}) print(t_logits,targets) # run( # fetches, 元组：一些用于计算结果的张量，用于去除计算结果tensor （比如我们要求姐y=w*x中的w，这里的fetches相当于y） # feed_dict=None, 字典：输入变量占位符 （这里的feed_dict相当于x） # options=None, # run_metadata=None # ) # 参考：http://wiki.jikexueyuan.com/project/tensorflow-zh/get_started/basic_usage.html # cost计算误差，train_op通过cost来更新权重 _, loss = sess.run( [train_op, cost], { input_data: sources_batch, targets: targets_batch, lr: learning_rate, target_sequence_length: targets_lengths, source_sequence_length: sources_lengths}) # 共77批 if batch_i % display_step == 0: # 计算validation loss # 验证时不需要更新权重 validation_loss = sess.run( [cost], { input_data: valid_sources_batch, targets: valid_targets_batch, lr: learning_rate, target_sequence_length: valid_targets_lengths, source_sequence_length: valid_sources_lengths}) print('Epoch {:>3}/{} Batch {:>4}/{} - Training Loss: {:>6.3f} - Validation loss: {:>6.3f}' .format(epoch_i, epochs, batch_i, len(train_source) // batch_size, loss, validation_loss[0])) # 保存模型 saver = tf.train.Saver() saver.save(sess, os.path.join(save_path, checkpoint)) print('Model Trained and Saved')## 预测def source_to_seq(text): ''' 对源数据进行转换 ''' sequence_length = 7 return [source_letter_to_int.get(word, source_letter_to_int['
                   
                    ']) for word in text] + [ source_letter_to_int['
                    
                     ']] * (sequence_length - len(text))def predict(): os.environ["CUDA_VISIBLE_DEVICES"] = "-1" # 输入一个单词 input_word = 'common' text = source_to_seq(input_word) checkpoint = "/trained_model.ckpt" loaded_graph = tf.Graph() with tf.Session(graph=loaded_graph) as sess: # 加载模型 modelPath =save_path+checkpoint print(modelPath) loader = tf.train.import_meta_graph(modelPath + '.meta') loader.restore(sess, modelPath) input_data = loaded_graph.get_tensor_by_name('inputs:0') logits = loaded_graph.get_tensor_by_name('predictions:0') source_sequence_length = loaded_graph.get_tensor_by_name('source_sequence_length:0') target_sequence_length = loaded_graph.get_tensor_by_name('target_sequence_length:0') answer_logits = sess.run(logits, { input_data: [text] * batch_size, target_sequence_length: [len(input_word)] * batch_size, source_sequence_length: [len(input_word)] * batch_size})[0] pad = source_letter_to_int["
                     
                      "] print('原始输入:', input_word) print('\nSource') print(' Word 编号: {}'.format([i for i in text])) print(' Input Words: {}'.format(" ".join([source_int_to_letter[i] for i in text]))) print('\nTarget') print(' Word 编号: {}'.format([i for i in answer_logits if i != pad])) print(' Response Words: {}'.format(" ".join([target_int_to_letter[i] for i in answer_logits if i != pad])))if __name__ == '__main__': # train or predict flag = 'train' if flag == 'train': train() else: predict()