TensorFlow-最基本的seq2seq写法

以此为核心，加上各种改进技巧，造就强大的seq2seq！Action!

import tensorflow as tf


class Seq2seq(object):
    def __init__(self, config, w2i_target):
        self.seq_inputs = tf.placeholder(shape=(config.batch_size, None), dtype=tf.int32, name='seq_inputs')
        self.seq_inputs_length = tf.placeholder(shape=(config.batch_size,), dtype=tf.int32, name='seq_inputs_length')
        self.seq_targets = tf.placeholder(shape=(config.batch_size, None), dtype=tf.int32, name='seq_targets')
        self.seq_targets_length = tf.placeholder(shape=(config.batch_size,), dtype=tf.int32, name='seq_targets_length')

        with tf.variable_scope("encoder"):
            encoder_embedding = tf.Variable(tf.random_uniform([config.source_vocab_size, config.embedding_dim]),
                                            dtype=tf.float32, name='encoder_embedding')
            #tf.nn.embedding_lookup的用法主要是选取一个张量里面索引对应的元素
            encoder_inputs_embedded = tf.nn.embedding_lookup(encoder_embedding, self.seq_inputs)
            encoder_cell = tf.nn.rnn_cell.GRUCell(config.hidden_dim) #定义GRU单元个数，num_units表示rnn cell中神经元个数
            encoder_outputs, encoder_state = tf.nn.dynamic_rnn(cell=encoder_cell, inputs=encoder_inputs_embedded,
                                                               sequence_length=self.seq_inputs_length, dtype=tf.float32,
                                                               time_major=False)

        tokens_go = tf.ones([config.batch_size], dtype=tf.int32) * w2i_target["_GO"]
        # 把期望输出往后挪一下，然后前面加一个“_GO”的标记，比如期望输出序列是：“你瞅啥”，改完后为“_GO你瞅啥”
        decoder_inputs = tf.concat([tf.reshape(tokens_go, [-1, 1]), self.seq_targets[:, :-1]], 1)

        with tf.variable_scope("decoder"):
            decoder_embedding = tf.Variable(tf.random_uniform([config.target_vocab_size, config.embedding_dim]),
                                            dtype=tf.float32, name='decoder_embedding')
            # tf.nn.embedding_lookup的用法主要是选取一个张量里面索引对应的元素
            decoder_inputs_embedded = tf.nn.embedding_lookup(decoder_embedding, decoder_inputs)
            decoder_cell = tf.nn.rnn_cell.GRUCell(config.hidden_dim)
            decoder_outputs, decoder_state = tf.nn.dynamic_rnn(cell=decoder_cell, inputs=decoder_inputs_embedded,
                                                               initial_state=encoder_state,
                                                               sequence_length=self.seq_targets_length,dtype=tf.float32,
                                                               time_major=False)
        # 全连接层  相当于添加一个层
        decoder_logits = tf.layers.dense(decoder_outputs.rnn_output, config.target_vocab_size)
        self.out = tf.argmax(decoder_logits, 2)  #返回某个维度最大值的位置