def __init__(self,
num_symbols,
num_embed_units,
num_units,
num_layers,
embed,
entity_embed=None,
num_entities=0,
num_trans_units=100,
learning_rate=0.0001,
learning_rate_decay_factor=0.95,
max_gradient_norm=5.0,
num_samples=512,
max_length=60,
output_alignments=True,
use_lstm=False):
self.posts = tf.placeholder(tf.string, (None, None), 'enc_inps') # batch*len
self.posts_length = tf.placeholder(tf.int32, (None), 'enc_lens') # batch
self.responses = tf.placeholder(tf.string, (None, None), 'dec_inps') # batch*len
self.responses_length = tf.placeholder(tf.int32, (None), 'dec_lens') # batch
self.entities = tf.placeholder(tf.string, (None, None), 'entities') # batch
self.entity_masks = tf.placeholder(tf.string, (None, None), 'entity_masks') # batch
self.triples = tf.placeholder(tf.string, (None, None, 3), 'triples') # batch
self.posts_triple = tf.placeholder(tf.int32, (None, None, 1), 'enc_triples') # batch
self.responses_triple = tf.placeholder(tf.string, (None, None, 3), 'dec_triples') # batch
self.match_triples = tf.placeholder(tf.int32, (None, None), 'match_triples') # batch
encoder_batch_size, encoder_len = tf.unstack(tf.shape(self.posts))
triple_num = tf.shape(self.triples)[1]
#use_triples = tf.reduce_sum(tf.cast(tf.greater_equal(self.match_triples, 0), tf.float32), axis=-1)
one_hot_triples = tf.one_hot(self.match_triples, triple_num)
use_triples = tf.reduce_sum(one_hot_triples, axis=[2])
self.symbol2index = MutableHashTable(
key_dtype=tf.string,
value_dtype=tf.int64,
default_value=UNK_ID,
shared_name="in_table",
name="in_table",
checkpoint=True)
self.index2symbol = MutableHashTable(
key_dtype=tf.int64,
value_dtype=tf.string,
default_value='_UNK',
shared_name="out_table",
name="out_table",
checkpoint=True)
self.entity2index = MutableHashTable(
key_dtype=tf.string,
value_dtype=tf.int64,
default_value=NONE_ID,
shared_name="entity_in_table",
name="entity_in_table",
checkpoint=True)
self.index2entity = MutableHashTable(
key_dtype=tf.int64,
value_dtype=tf.string,
default_value='_NONE',
shared_name="entity_out_table",
name="entity_out_table",
checkpoint=True)
# build the vocab table (string to index)
self.posts_word_id = self.symbol2index.lookup(self.posts) # batch*len
self.posts_entity_id = self.entity2index.lookup(self.posts) # batch*len
#self.posts_word_id = tf.Print(self.posts_word_id, ['use_triples', use_triples, 'one_hot_triples', one_hot_triples], summarize=1e6)
self.responses_target = self.symbol2index.lookup(self.responses) #batch*len
batch_size, decoder_len = tf.shape(self.responses)[0], tf.shape(self.responses)[1]
self.responses_word_id = tf.concat([tf.ones([batch_size, 1], dtype=tf.int64)*GO_ID,
tf.split(self.responses_target, [decoder_len-1, 1], 1)[0]], 1) # batch*len
self.decoder_mask = tf.reshape(tf.cumsum(tf.one_hot(self.responses_length-1,
decoder_len), reverse=True, axis=1), [-1, decoder_len])
# build the embedding table (index to vector)
if embed is None:
# initialize the embedding randomly
self.embed = tf.get_variable('word_embed', [num_symbols, num_embed_units], tf.float32)
else:
# initialize the embedding by pre-trained word vectors
self.embed = tf.get_variable('word_embed', dtype=tf.float32, initializer=embed)
if entity_embed is None:
# initialize the embedding randomly
self.entity_trans = tf.get_variable('entity_embed', [num_entities, num_trans_units], tf.float32, trainable=False)
else:
# initialize the embedding by pre-trained word vectors
self.entity_trans = tf.get_variable('entity_embed', dtype=tf.float32, initializer=entity_embed, trainable=False)
self.entity_trans_transformed = tf.layers.dense(self.entity_trans, num_trans_units, activation=tf.tanh, name='trans_transformation')
padding_entity = tf.get_variable('entity_padding_embed', [7, num_trans_units], dtype=tf.float32, initializer=tf.zeros_initializer())
self.entity_embed = tf.concat([padding_entity, self.entity_trans_transformed], axis=0)
triples_embedding = tf.reshape(tf.nn.embedding_lookup(self.entity_embed, self.entity2index.lookup(self.triples)), [encoder_batch_size, triple_num, 3 * num_trans_units])
entities_word_embedding = tf.reshape(tf.nn.embedding_lookup(self.embed, self.symbol2index.lookup(self.entities)), [encoder_batch_size, -1, num_embed_units])
self.encoder_input = tf.nn.embedding_lookup(self.embed, self.posts_word_id) #batch*len*unit
self.decoder_input = tf.nn.embedding_lookup(self.embed, self.responses_word_id) #batch*len*unit
encoder_cell = MultiRNNCell([GRUCell(num_units) for _ in range(num_layers)])
decoder_cell = MultiRNNCell([GRUCell(num_units) for _ in range(num_layers)])
# rnn encoder
encoder_output, encoder_state = dynamic_rnn(encoder_cell, self.encoder_input,
self.posts_length, dtype=tf.float32, scope="encoder")
# get output projection function
output_fn, selector_fn, sequence_loss, sampled_sequence_loss, total_loss = output_projection_layer(num_units,
num_symbols, num_samples)
with tf.variable_scope('decoder'):
# get attention function
attention_keys_init, attention_values_init, attention_score_fn_init, attention_construct_fn_init \
= prepare_attention(encoder_output, 'bahdanau', num_units, imem=triples_embedding, output_alignments=output_alignments)#'luong', num_units)
decoder_fn_train = attention_decoder_fn_train(
encoder_state, attention_keys_init, attention_values_init,
attention_score_fn_init, attention_construct_fn_init, output_alignments=output_alignments, max_length=tf.reduce_max(self.responses_length))
self.decoder_output, _, alignments_ta = dynamic_rnn_decoder(decoder_cell, decoder_fn_train,
self.decoder_input, self.responses_length, scope="decoder_rnn")
if output_alignments:
self.alignments = tf.transpose(alignments_ta.stack(), perm=[1,0,2])
#self.alignments = tf.Print(self.alignments, [self.alignments], summarize=1e8)
self.decoder_loss, self.ppx_loss, self.sentence_ppx = total_loss(self.decoder_output, self.responses_target, self.decoder_mask, self.alignments, triples_embedding, use_triples, one_hot_triples)
self.sentence_ppx = tf.identity(self.sentence_ppx, 'ppx_loss')
#self.decoder_loss = tf.Print(self.decoder_loss, ['decoder_loss', self.decoder_loss], summarize=1e6)
else:
self.decoder_loss, self.sentence_ppx = sequence_loss(self.decoder_output,
self.responses_target, self.decoder_mask)
self.sentence_ppx = tf.identity(self.sentence_ppx, 'ppx_loss')
with tf.variable_scope('decoder', reuse=True):
# get attention function
attention_keys, attention_values, attention_score_fn, attention_construct_fn \
= prepare_attention(encoder_output, 'bahdanau', num_units, reuse=True, imem=triples_embedding, output_alignments=output_alignments)#'luong', num_units)
decoder_fn_inference = attention_decoder_fn_inference(
output_fn, encoder_state, attention_keys, attention_values,
attention_score_fn, attention_construct_fn, self.embed, GO_ID,
EOS_ID, max_length, num_symbols, imem=entities_word_embedding, selector_fn=selector_fn)
self.decoder_distribution, _, output_ids_ta = dynamic_rnn_decoder(decoder_cell,
decoder_fn_inference, scope="decoder_rnn")
if output_alignments:
output_len = tf.shape(self.decoder_distribution)[1]
output_ids = tf.transpose(output_ids_ta.gather(tf.range(output_len)))
word_ids = tf.cast(tf.clip_by_value(output_ids, 0, num_symbols), tf.int64)
entity_ids = tf.reshape(tf.clip_by_value(-output_ids, 0, num_symbols) + tf.reshape(tf.range(encoder_batch_size) * tf.shape(entities_word_embedding)[1], [-1, 1]), [-1])
entities = tf.reshape(tf.gather(tf.reshape(self.entities, [-1]), entity_ids), [-1, output_len])
words = self.index2symbol.lookup(word_ids)
self.generation = tf.where(output_ids > 0, words, entities, name='generation')
else:
self.generation_index = tf.argmax(self.decoder_distribution, 2)
self.generation = self.index2symbol.lookup(self.generation_index, name='generation')
# initialize the training process
self.learning_rate = tf.Variable(float(learning_rate),
trainable=False, dtype=tf.float32)
self.learning_rate_decay_op = self.learning_rate.assign(
self.learning_rate * learning_rate_decay_factor)
self.global_step = tf.Variable(0, trainable=False)
self.params = tf.global_variables()
# calculate the gradient of parameters
#opt = tf.train.GradientDescentOptimizer(self.learning_rate)
opt = tf.train.AdamOptimizer(learning_rate=learning_rate)
self.lr = opt._lr
gradients = tf.gradients(self.decoder_loss, self.params)
clipped_gradients, self.gradient_norm = tf.clip_by_global_norm(gradients,
max_gradient_norm)
self.update = opt.apply_gradients(zip(clipped_gradients, self.params),
global_step=self.global_step)
tf.summary.scalar('decoder_loss', self.decoder_loss)
for each in tf.trainable_variables():
tf.summary.histogram(each.name, each)
self.merged_summary_op = tf.summary.merge_all()
self.saver = tf.train.Saver(write_version=tf.train.SaverDef.V2,
max_to_keep=3, pad_step_number=True, keep_checkpoint_every_n_hours=1.0)
self.saver_epoch = tf.train.Saver(write_version=tf.train.SaverDef.V2, max_to_keep=1000, pad_step_number=True)
def __init__(
self,
num_symbols, # 词汇表size
num_embed_units, # 词嵌入size
num_units, # RNN 每层单元数
num_layers, # RNN 层数
embed, # 词嵌入
entity_embed=None, # 实体+关系的嵌入
num_entities=0, # 实体+关系的总个数
num_trans_units=100, # 实体嵌入的维度
memory_units=100,
learning_rate=0.0001, # 学习率
learning_rate_decay_factor=0.95, # 学习率衰退，并没有采用这种方式
max_gradient_norm=5.0, #
num_samples=500, # 样本个数，sampled softmax
max_length=60,
mem_use=True,
output_alignments=True,
use_lstm=False):
self.posts = tf.placeholder(tf.string, (None, None),
'enc_inps') # [batch_size, encoder_len]
self.posts_length = tf.placeholder(tf.int32, (None),
'enc_lens') # [batch_size]
self.responses = tf.placeholder(
tf.string, (None, None), 'dec_inps') # [batch_size, decoder_len]
self.responses_length = tf.placeholder(tf.int32, (None),
'dec_lens') # [batch_size]
self.entities = tf.placeholder(
tf.string, (None, None, None),
'entities') # [batch_size, triple_num, triple_len]
self.entity_masks = tf.placeholder(tf.string, (None, None),
'entity_masks') # 没用到
self.triples = tf.placeholder(
tf.string, (None, None, None, 3),
'triples') # [batch_size, triple_num, triple_len, 3]
self.posts_triple = tf.placeholder(
tf.int32, (None, None, 1),
'enc_triples') # [batch_size, encoder_len, 1]
self.responses_triple = tf.placeholder(
tf.string, (None, None, 3),
'dec_triples') # [batch_size, decoder_len, 3]
self.match_triples = tf.placeholder(
tf.int32, (None, None, None),
'match_triples') # [batch_size, decoder_len, triple_num]
# 编码器batch_size，编码器encoder_len
encoder_batch_size, encoder_len = tf.unstack(tf.shape(self.posts))
triple_num = tf.shape(self.triples)[1] # 知识图个数
triple_len = tf.shape(self.triples)[2] # 知识三元组个数
# 使用的知识三元组
one_hot_triples = tf.one_hot(
self.match_triples,
triple_len) # [batch_size, decoder_len, triple_num, triple_len]
# 用 1 标注了哪个时间步产生的回复用了知识三元组
use_triples = tf.reduce_sum(one_hot_triples,
axis=[2, 3]) # [batch_size, decoder_len]
# 词汇映射到index的hash table
self.symbol2index = MutableHashTable(
key_dtype=tf.string, # key张量的类型
value_dtype=tf.int64, # value张量的类型
default_value=UNK_ID, # 缺少key的默认值
shared_name=
"in_table", # If non-empty, this table will be shared under the given name across multiple sessions
name="in_table", # 操作名
checkpoint=True
) # if True, the contents of the table are saved to and restored from checkpoints. If shared_name is empty for a checkpointed table, it is shared using the table node name.
# index映射到词汇的hash table
self.index2symbol = MutableHashTable(key_dtype=tf.int64,
value_dtype=tf.string,
default_value='_UNK',
shared_name="out_table",
name="out_table",
checkpoint=True)
# 实体映射到index的hash table
self.entity2index = MutableHashTable(key_dtype=tf.string,
value_dtype=tf.int64,
default_value=NONE_ID,
shared_name="entity_in_table",
name="entity_in_table",
checkpoint=True)
# index映射到实体的hash table
self.index2entity = MutableHashTable(key_dtype=tf.int64,
value_dtype=tf.string,
default_value='_NONE',
shared_name="entity_out_table",
name="entity_out_table",
checkpoint=True)
self.posts_word_id = self.symbol2index.lookup(
self.posts) # [batch_size, encoder_len]
self.posts_entity_id = self.entity2index.lookup(
self.posts) # [batch_size, encoder_len]
self.responses_target = self.symbol2index.lookup(
self.responses) # [batch_size, decoder_len]
# 获得解码器的batch_size，decoder_len
batch_size, decoder_len = tf.shape(self.responses)[0], tf.shape(
self.responses)[1]
# 去掉responses_target的最后一列，给第一列加上GO_ID
self.responses_word_id = tf.concat([
tf.ones([batch_size, 1], dtype=tf.int64) * GO_ID,
tf.split(self.responses_target, [decoder_len - 1, 1], 1)[0]
], 1) # [batch_size, decoder_len]
# 得到response的mask
self.decoder_mask = tf.reshape(
tf.cumsum(tf.one_hot(self.responses_length - 1, decoder_len),
reverse=True,
axis=1), [-1, decoder_len]) # [batch_size, decoder_len]
# 初始化词嵌入和实体嵌入，传入了参数就直接赋值，没有的话就随机初始化
if embed is None:
self.embed = tf.get_variable('word_embed',
[num_symbols, num_embed_units],
tf.float32)
else:
self.embed = tf.get_variable('word_embed',
dtype=tf.float32,
initializer=embed)
if entity_embed is None: # 实体嵌入不随着模型的训练而更新
self.entity_trans = tf.get_variable(
'entity_embed', [num_entities, num_trans_units],
tf.float32,
trainable=False)
else:
self.entity_trans = tf.get_variable('entity_embed',
dtype=tf.float32,
initializer=entity_embed,
trainable=False)
# 将实体嵌入传入一个全连接层
self.entity_trans_transformed = tf.layers.dense(
self.entity_trans,
num_trans_units,
activation=tf.tanh,
name='trans_transformation')
# 添加['_NONE', '_PAD_H', '_PAD_R', '_PAD_T', '_NAF_H', '_NAF_R', '_NAF_T']这7个的嵌入
padding_entity = tf.get_variable('entity_padding_embed',
[7, num_trans_units],
dtype=tf.float32,
initializer=tf.zeros_initializer())
self.entity_embed = tf.concat(
[padding_entity, self.entity_trans_transformed], axis=0)
# triples_embedding: [batch_size, triple_num, triple_len, 3*num_trans_units] 知识图三元组的嵌入
triples_embedding = tf.reshape(
tf.nn.embedding_lookup(self.entity_embed,
self.entity2index.lookup(self.triples)),
[encoder_batch_size, triple_num, -1, 3 * num_trans_units])
# entities_word_embedding: [batch_size, triple_num*triple_len, num_embed_units] 知识图中用到的所有实体的嵌入
entities_word_embedding = tf.reshape(
tf.nn.embedding_lookup(self.embed,
self.symbol2index.lookup(self.entities)),
[encoder_batch_size, -1, num_embed_units])
# 分离知识图三元组的头、关系和尾 [batch_size, triple_num, triple_len, num_trans_units]
head, relation, tail = tf.split(triples_embedding,
[num_trans_units] * 3,
axis=3)
# 静态图注意力机制
with tf.variable_scope('graph_attention'):
# 将头尾连接起来 [batch_size, triple_num, triple_len, 2*num_trans_units]
head_tail = tf.concat([head, tail], axis=3)
# 将头尾送入全连接层 [batch_size, triple_num, triple_len, num_trans_units]
head_tail_transformed = tf.layers.dense(head_tail,
num_trans_units,
activation=tf.tanh,
name='head_tail_transform')
# 将关系送入全连接层 [batch_size, triple_num, triple_len, num_trans_units]
relation_transformed = tf.layers.dense(relation,
num_trans_units,
name='relation_transform')
# 求头尾和关系两个向量的内积，获得对三元组的注意力系数
e_weight = tf.reduce_sum(
relation_transformed * head_tail_transformed,
axis=3) # [batch_size, triple_num, triple_len]
alpha_weight = tf.nn.softmax(
e_weight) # [batch_size, triple_num, triple_len]
# tf.expand_dims 使 alpha_weight 维度+1 [batch_size, triple_num, triple_len, 1]
# 对第2个维度求和,由此产生静态图的向量表示
graph_embed = tf.reduce_sum(
tf.expand_dims(alpha_weight, 3) * head_tail,
axis=2) # [batch_size, triple_num, 2*num_trans_units]
"""graph_embed_input
1、首先一维的range列表[0, 1, 2... encoder_batch_size个]转化成三维的[encoder_batch_size, 1, 1]的矩阵
[[[0]], [[1]], [[2]],...]
2、然后tf.tile将矩阵的第1维复制encoder_len遍，变成[encoder_batch_size， encoder_len， 1]
[[[0],[0]...]],...]
3、与posts_triple: [batch_size, encoder_len, 1]在第2维上进行拼接，形成一个indices: [batch_size, encoder_len, 2]矩阵，
indices矩阵：
[
[[0 0], [0 0], [0 0], [0 0], [0 1], [0 0], [0 2], [0 0],...encoder_len],
[[1 0], [1 0], [1 0], [1 0], [1 1], [1 0], [1 2], [1 0],...encoder_len],
[[2 0], [2 0], [2 0], [2 0], [2 1], [2 0], [2 2], [2 0],...encoder_len]
,...batch_size
]
4、tf.gather_nd根据索引检索graph_embed: [batch_size, triple_num, 2*num_trans_units]再回填至indices矩阵
indices矩阵最后一个维度是2，例如有[0, 2]，表示这个时间步第1个batch用了第2个图，
则找到这个知识图的静态图向量填入到indices矩阵的[0, 2]位置最后得到结果维度
[encoder_batch_size, encoder_len, 2*num_trans_units]表示每个时间步用的静态图向量
"""
# graph_embed_input = tf.gather_nd(graph_embed, tf.concat(
# [tf.tile(tf.reshape(tf.range(encoder_batch_size, dtype=tf.int32), [-1, 1, 1]), [1, encoder_len, 1]),
# self.posts_triple],
# axis=2))
# 将responses_triple转化成实体嵌入 [batch_size, decoder_len, 300]，标识了response每个时间步用了哪个三元组的嵌入
# triple_embed_input = tf.reshape(
# tf.nn.embedding_lookup(self.entity_embed, self.entity2index.lookup(self.responses_triple)),
# [batch_size, decoder_len, 3 * num_trans_units])
post_word_input = tf.nn.embedding_lookup(
self.embed,
self.posts_word_id) # [batch_size, encoder_len, num_embed_units]
response_word_input = tf.nn.embedding_lookup(
self.embed, self.responses_word_id
) # [batch_size, decoder_len, num_embed_units]
# post_word_input和graph_embed_input拼接构成编码器输入 [batch_size, encoder_len, num_embed_units+2*num_trans_units]
# self.encoder_input = tf.concat([post_word_input, graph_embed_input], axis=2)
# response_word_input和triple_embed_input拼接构成解码器输入 [batch_size, decoder_len, num_embed_units+3*num_trans_units]
# self.decoder_input = tf.concat([response_word_input, triple_embed_input], axis=2)
encoder_cell = MultiRNNCell(
[GRUCell(num_units) for _ in range(num_layers)])
decoder_cell = MultiRNNCell(
[GRUCell(num_units) for _ in range(num_layers)])
# rnn encoder
# encoder_state: [num_layers, 2, batch_size, num_units] 编码器输出状态 LSTM GRU:[num_layers, batch_size, num_units]
encoder_output, encoder_state = tf.nn.dynamic_rnn(encoder_cell,
post_word_input,
self.posts_length,
dtype=tf.float32,
scope="encoder")
# self.encoder_state_shape = tf.shape(encoder_state)
########记忆网络 ###
response_encoder_cell = MultiRNNCell(
[GRUCell(num_units) for _ in range(num_layers)])
response_encoder_output, response_encoder_state = tf.nn.dynamic_rnn(
response_encoder_cell,
response_word_input,
self.responses_length,
dtype=tf.float32,
scope="response_encoder")
# graph_embed: [batch_size, triple_num, 2*num_trans_units] 静态图向量
# encoder_state: [num_layers, batch_size, num_units]
with tf.variable_scope("post_memory_network"):
# 将静态知识图转化成输入向量m
post_input = tf.layers.dense(graph_embed,
memory_units,
use_bias=False,
name="post_weight_a")
post_input = tf.tile(
tf.reshape(post_input,
(1, encoder_batch_size, triple_num, memory_units)),
multiples=(
num_layers, 1, 1,
1)) # [num_layers, batch_size, triple_num, memory_units]
# 将静态知识库转化成输出向量c
post_output = tf.layers.dense(graph_embed,
memory_units,
use_bias=False,
name="post_weight_c")
post_output = tf.tile(
tf.reshape(post_output,
(1, encoder_batch_size, triple_num, memory_units)),
multiples=(
num_layers, 1, 1,
1)) # [num_layers, batch_size, triple_num, memory_units]
# 将question转化成状态向量u
encoder_hidden_state = tf.reshape(
tf.concat(encoder_state,
axis=0), (num_layers, encoder_batch_size, num_units))
post_state = tf.layers.dense(encoder_hidden_state,
memory_units,
use_bias=False,
name="post_weight_b")
post_state = tf.tile(
tf.reshape(post_state,
(num_layers, encoder_batch_size, 1, memory_units)),
multiples=(
1, 1, triple_num,
1)) # [num_layers, batch_size, triple_num, memory_units]
# 概率p
post_p = tf.reshape(
tf.nn.softmax(tf.reduce_sum(post_state * post_input, axis=3)),
(num_layers, encoder_batch_size, triple_num,
1)) # [num_layers, batch_size, triple_num, 1]
# 输出o
post_o = tf.reduce_sum(
post_output * post_p,
axis=2) # [num_layers, batch_size, memory_units]
post_xstar = tf.concat(
[
tf.layers.dense(post_o,
memory_units,
use_bias=False,
name="post_weight_r"), encoder_state
],
axis=2) # [num_layers, batch_size, num_units+memory_units]
with tf.variable_scope("response_memory_network"):
# 将静态知识图转化成输入向量m
response_input = tf.layers.dense(graph_embed,
memory_units,
use_bias=False,
name="response_weight_a")
response_input = tf.tile(
tf.reshape(response_input,
(1, batch_size, triple_num, memory_units)),
multiples=(
num_layers, 1, 1,
1)) # [num_layers, batch_size, triple_num, memory_units]
# 将静态知识库转化成输出向量c
response_output = tf.layers.dense(graph_embed,
memory_units,
use_bias=False,
name="response_weight_c")
response_output = tf.tile(
tf.reshape(response_output,
(1, batch_size, triple_num, memory_units)),
multiples=(
num_layers, 1, 1,
1)) # [num_layers, batch_size, triple_num, memory_units]
# 将question转化成状态向量u
response_hidden_state = tf.reshape(
tf.concat(response_encoder_state, axis=0),
(num_layers, batch_size, num_units))
response_state = tf.layers.dense(response_hidden_state,
memory_units,
use_bias=False,
name="response_weight_b")
response_state = tf.tile(
tf.reshape(response_state,
(num_layers, batch_size, 1, memory_units)),
multiples=(
1, 1, triple_num,
1)) # [num_layers, batch_size, triple_num, memory_units]
# 概率p
response_p = tf.reshape(
tf.nn.softmax(
tf.reduce_sum(response_state * response_input, axis=3)),
(num_layers, batch_size, triple_num,
1)) # [num_layers, batch_size, triple_num, 1]
# 输出o
response_o = tf.reduce_sum(
response_output * response_p,
axis=2) # [num_layers, batch_size, memory_units]
response_ystar = tf.concat(
[
tf.layers.dense(response_o,
memory_units,
use_bias=False,
name="response_weight_r"),
response_encoder_state
],
axis=2) # [num_layers, batch_size, num_units+memory_units]
with tf.variable_scope("memory_network"):
memory_hidden_state = tf.layers.dense(tf.concat(
[post_xstar, response_ystar], axis=2),
num_units,
use_bias=False,
activation=tf.tanh,
name="output_weight")
memory_hidden_state = tf.reshape(
memory_hidden_state, (num_layers * batch_size, num_units))
# [num_layers, batch_size, num_units]
memory_hidden_state = tuple(
tf.split(memory_hidden_state, [batch_size] * num_layers,
axis=0))
# self.memory_hidden_state_shape = tf.shape(memory_hidden_state)
######## ###
output_fn, selector_fn, sequence_loss, sampled_sequence_loss, total_loss =\
output_projection_layer(num_units, num_symbols, num_samples)
########用于训练的decoder ###
with tf.variable_scope('decoder'):
attention_keys_init, attention_values_init, attention_score_fn_init, attention_construct_fn_init \
= prepare_attention(encoder_output,
'bahdanau',
num_units,
imem=(graph_embed, triples_embedding),
output_alignments=output_alignments and mem_use)
# 训练时处理每个时间步输出和下个时间步输入的函数
decoder_fn_train = attention_decoder_fn_train(
memory_hidden_state,
attention_keys_init,
attention_values_init,
attention_score_fn_init,
attention_construct_fn_init,
output_alignments=output_alignments and mem_use,
max_length=tf.reduce_max(self.responses_length))
self.decoder_output, _, alignments_ta = dynamic_rnn_decoder(
decoder_cell,
decoder_fn_train,
response_word_input,
self.responses_length,
scope="decoder_rnn")
if output_alignments:
self.alignments = tf.transpose(alignments_ta.stack(),
perm=[1, 0, 2, 3])
self.decoder_loss, self.ppx_loss, self.sentence_ppx = total_loss(
self.decoder_output, self.responses_target,
self.decoder_mask, self.alignments, triples_embedding,
use_triples, one_hot_triples)
self.sentence_ppx = tf.identity(self.sentence_ppx,
name='ppx_loss')
else:
self.decoder_loss = sequence_loss(self.decoder_output,
self.responses_target,
self.decoder_mask)
######## ###
########用于推导的decoder ###
with tf.variable_scope('decoder', reuse=True):
attention_keys, attention_values, attention_score_fn, attention_construct_fn \
= prepare_attention(encoder_output,
'bahdanau',
num_units,
reuse=True,
imem=(graph_embed, triples_embedding),
output_alignments=output_alignments and mem_use)
decoder_fn_inference = \
attention_decoder_fn_inference(output_fn,
memory_hidden_state,
attention_keys,
attention_values,
attention_score_fn,
attention_construct_fn,
self.embed,
GO_ID,
EOS_ID,
max_length,
num_symbols,
imem=(entities_word_embedding, # imem: ([batch_size,triple_num*triple_len,num_embed_units],
tf.reshape(triples_embedding, [encoder_batch_size, -1, 3*num_trans_units])), # [encoder_batch_size, triple_num*triple_len, 3*num_trans_units]) 实体词嵌入和三元组嵌入的元组
selector_fn=selector_fn)
# decoder_distribution: [batch_size, decoder_len, num_symbols]
# output_ids_ta: tensorarray: decoder_len [batch_size]
self.decoder_distribution, _, output_ids_ta = dynamic_rnn_decoder(
decoder_cell, decoder_fn_inference, scope="decoder_rnn")
output_len = tf.shape(self.decoder_distribution)[1] # decoder_len
output_ids = tf.transpose(
output_ids_ta.gather(
tf.range(output_len))) # [batch_size, decoder_len]
# 对output的值域行裁剪，因为存在负值表示用了实体词
word_ids = tf.cast(tf.clip_by_value(output_ids, 0, num_symbols),
tf.int64) # [batch_size, decoder_len]
# 计算的是实体词在entities中的实际位置 [batch_size, decoder_len]
# 1、tf.shape(entities_word_embedding)[1] = triple_num*triple_len
# 2、tf.range(encoder_batch_size): [batch_size]
# 3、tf.reshape(tf.range(encoder_batch_size) * tf.shape(entities_word_embedding)[1], [-1, 1]): [batch_size, 1] 实体词在entities中的基地址
# 4、tf.clip_by_value(-output_ids, 0, num_symbols): [batch_size, decoder_len] 实体词在entities中的偏移量
# 5、entity_ids: [batch_size, decoder_len] 实体词在entities中的实际位置
entity_ids = tf.reshape(
tf.clip_by_value(-output_ids, 0, num_symbols) + tf.reshape(
tf.range(encoder_batch_size) *
tf.shape(entities_word_embedding)[1], [-1, 1]), [-1])
# 计算的是所用的实体词 [batch_size, decoder_len]
# 1、entities: [batch_size, triple_num, triple_len]
# 2、tf.reshape(self.entities, [-1]): [batch_size*triple_num*triple_len]
# 3、tf.gather: [batch_size*decoder_len]
# 4、entities: [batch_size, decoder_len]
entities = tf.reshape(
tf.gather(tf.reshape(self.entities, [-1]), entity_ids),
[-1, output_len])
words = self.index2symbol.lookup(word_ids) # 将id转化为实际的词
# output_ids>0为bool张量，True的位置用words中该位置的词替换
self.generation = tf.where(output_ids > 0, words, entities)
self.generation = tf.identity(
self.generation,
name='generation') # [batch_size, decoder_len]
######## ###
# 初始化训练过程
self.learning_rate = tf.Variable(float(learning_rate),
trainable=False,
dtype=tf.float32)
# 并没有使用衰退的学习率
self.learning_rate_decay_op = self.learning_rate.assign(
self.learning_rate * learning_rate_decay_factor)
# 更新参数的次数
self.global_step = tf.Variable(0, trainable=False)
# 要训练的参数
self.params = tf.global_variables()
# 选择优化算法
opt = tf.train.AdamOptimizer(learning_rate=learning_rate)
self.lr = opt._lr
# 根据 decoder_loss 计算 params 梯度
gradients = tf.gradients(self.decoder_loss, self.params)
# 梯度裁剪
clipped_gradients, self.gradient_norm = tf.clip_by_global_norm(
gradients, max_gradient_norm)
self.update = opt.apply_gradients(zip(clipped_gradients, self.params),
global_step=self.global_step)
# 记录损失
tf.summary.scalar('decoder_loss', self.decoder_loss)
for each in tf.trainable_variables():
tf.summary.histogram(each.name, each) # 记录变量的训练情况
self.merged_summary_op = tf.summary.merge_all()
self.saver = tf.train.Saver(write_version=tf.train.SaverDef.V2,
max_to_keep=3,
pad_step_number=True,
keep_checkpoint_every_n_hours=1.0)
self.saver_epoch = tf.train.Saver(write_version=tf.train.SaverDef.V2,
max_to_keep=1000,
pad_step_number=True)
def __init__(
self,
num_symbols, # 词汇表size
num_embed_units, # 词嵌入size
num_units, # RNN 每层单元数
num_layers, # RNN 层数
embed, # 词嵌入
entity_embed=None, #
num_entities=0, #
num_trans_units=100, #
learning_rate=0.0001,
learning_rate_decay_factor=0.95, #
max_gradient_norm=5.0, #
num_samples=500, # 样本个数，sampled softmax
max_length=60,
mem_use=True,
output_alignments=True,
use_lstm=False):
self.posts = tf.placeholder(tf.string, (None, None),
'enc_inps') # batch_size * encoder_len
self.posts_length = tf.placeholder(tf.int32, (None),
'enc_lens') # batch_size
self.responses = tf.placeholder(tf.string, (None, None),
'dec_inps') # batch_size * decoder_len
self.responses_length = tf.placeholder(tf.int32, (None),
'dec_lens') # batch_size
self.entities = tf.placeholder(
tf.string, (None, None, None),
'entities') # batch_size * triple_num * triple_len
self.entity_masks = tf.placeholder(tf.string, (None, None),
'entity_masks') # 没用到
self.triples = tf.placeholder(
tf.string, (None, None, None, 3),
'triples') # batch_size * triple_num * triple_len * 3
self.posts_triple = tf.placeholder(
tf.int32, (None, None, 1),
'enc_triples') # batch_size * encoder_len
self.responses_triple = tf.placeholder(
tf.string, (None, None, 3),
'dec_triples') # batch_size * decoder_len * 3
self.match_triples = tf.placeholder(
tf.int32, (None, None, None),
'match_triples') # batch_size * decoder_len * triple_num
# 获得 encoder_batch_size ,编码器的 encoder_len
encoder_batch_size, encoder_len = tf.unstack(tf.shape(self.posts))
# 获得 triple_num
# 每个 post 包含的知识图个数（补齐过的）
triple_num = tf.shape(self.triples)[1]
# 获得 triple_len
# 每个知识图包含的关联实体个数（补齐过的）
triple_len = tf.shape(self.triples)[2]
# 使用的知识三元组
one_hot_triples = tf.one_hot(
self.match_triples,
triple_len) # batch_size * decoder_len * triple_num * triple_len
# 用 1 标注了哪个时间步产生的回复用了知识三元组
use_triples = tf.reduce_sum(one_hot_triples,
axis=[2, 3]) # batch_size * decoder_len
# 词汇映射到 index 的 hash table
self.symbol2index = MutableHashTable(
key_dtype=tf.string, # key张量的类型
value_dtype=tf.int64, # value张量的类型
default_value=UNK_ID, # 缺少key的默认值
shared_name=
"in_table", # If non-empty, this table will be shared under the given name across multiple sessions
name="in_table", # 操作名
checkpoint=True
) # if True, the contents of the table are saved to and restored from checkpoints. If shared_name is empty for a checkpointed table, it is shared using the table node name.
# index 映射到词汇的 hash table
self.index2symbol = MutableHashTable(key_dtype=tf.int64,
value_dtype=tf.string,
default_value='_UNK',
shared_name="out_table",
name="out_table",
checkpoint=True)
# 实体映射到 index 的 hash table
self.entity2index = MutableHashTable(key_dtype=tf.string,
value_dtype=tf.int64,
default_value=NONE_ID,
shared_name="entity_in_table",
name="entity_in_table",
checkpoint=True)
# index 映射到实体的 hash table
self.index2entity = MutableHashTable(key_dtype=tf.int64,
value_dtype=tf.string,
default_value='_NONE',
shared_name="entity_out_table",
name="entity_out_table",
checkpoint=True)
# 将 post 的 string 映射成词汇 id
self.posts_word_id = self.symbol2index.lookup(
self.posts) # batch_size * encoder_len
# 将 post 的 string 映射成实体 id
self.posts_entity_id = self.entity2index.lookup(
self.posts) # batch_size * encoder_len
# 将 response 的 string 映射成词汇 id
self.responses_target = self.symbol2index.lookup(
self.responses) # batch_size * decoder_len
# 获得解码器的 batch_size，decoder_len
batch_size, decoder_len = tf.shape(self.responses)[0], tf.shape(
self.responses)[1]
# 去掉 responses_target 的最后一列，给第一列加上 GO_ID
self.responses_word_id = tf.concat([
tf.ones([batch_size, 1], dtype=tf.int64) * GO_ID,
tf.split(self.responses_target, [decoder_len - 1, 1], 1)[0]
], 1) # batch_size * decoder_len
# 得到 response 的 mask
# 首先将回复的长度 one_hot 编码
# 然后横着从右向左累计求和，形成一个如果该位置在长度范围内，则为1，否则则为0的矩阵，最后一步 reshape 应该没有必要
self.decoder_mask = tf.reshape(
tf.cumsum(tf.one_hot(self.responses_length - 1, decoder_len),
reverse=True,
axis=1), [-1, decoder_len]) # batch_size * decoder_len
# 初始化 词嵌入 和 实体嵌入，传入了参数就直接赋值，没有的话就随机初始化
if embed is None:
self.embed = tf.get_variable('word_embed',
[num_symbols, num_embed_units],
tf.float32)
else:
self.embed = tf.get_variable('word_embed',
dtype=tf.float32,
initializer=embed)
if entity_embed is None:
self.entity_trans = tf.get_variable(
'entity_embed', [num_entities, num_trans_units],
tf.float32,
trainable=False)
else:
self.entity_trans = tf.get_variable('entity_embed',
dtype=tf.float32,
initializer=entity_embed,
trainable=False)
# 添加一个全连接层，输入是实体的嵌入，该层的 size=num_trans_units，激活函数是tanh
# 为什么还要用全连接层连一下？？？？？？
self.entity_trans_transformed = tf.layers.dense(
self.entity_trans,
num_trans_units,
activation=tf.tanh,
name='trans_transformation')
# 7 * num_trans_units 的全零初始化的数组
padding_entity = tf.get_variable('entity_padding_embed',
[7, num_trans_units],
dtype=tf.float32,
initializer=tf.zeros_initializer())
# 把 padding_entity 添加到 entity_trans_transformed 的最前，补了有什么用？？？？？？？？？？？？？
self.entity_embed = tf.concat(
[padding_entity, self.entity_trans_transformed], axis=0)
# tf.nn.embedding_lookup 以后维度会+1，所以通过reshape来取消这个多出来的维度
triples_embedding = tf.reshape(
tf.nn.embedding_lookup(self.entity_embed,
self.entity2index.lookup(self.triples)),
[encoder_batch_size, triple_num, -1, 3 * num_trans_units])
entities_word_embedding = tf.reshape(
tf.nn.embedding_lookup(self.embed,
self.symbol2index.lookup(self.entities)),
[encoder_batch_size, -1, num_embed_units
]) # [batch_size,triple_num*triple_len,num_embed_units]
# 把 head，relation，tail分割开来
head, relation, tail = tf.split(triples_embedding,
[num_trans_units] * 3,
axis=3)
# 静态图注意力机制
with tf.variable_scope('graph_attention'):
# 将头和尾连接起来
head_tail = tf.concat(
[head, tail],
axis=3) # batch_size * triple_num * triple_len * 200
# tanh(dot(W, head_tail))
head_tail_transformed = tf.layers.dense(
head_tail,
num_trans_units,
activation=tf.tanh,
name='head_tail_transform'
) # batch_size * triple_num * triple_len * 100
# dot(W, relation)
relation_transformed = tf.layers.dense(
relation, num_trans_units, name='relation_transform'
) # batch_size * triple_num * triple_len * 100
# 两个向量先元素乘，再求和，等于两个向量的内积
# dot(traspose(dot(W, relation)), tanh(dot(W, head_tail)))
e_weight = tf.reduce_sum(
relation_transformed * head_tail_transformed,
axis=3) # batch_size * triple_num * triple_len
# 图中每个三元组的 alpha 权值
alpha_weight = tf.nn.softmax(
e_weight) # batch_size * triple_num * triple_len
# tf.expand_dims 使 alpha_weight 维度+1 batch_size * triple_num * triple_len * 1
# 对第2个维度求和,由此产生每个图 100 维的图向量表示
graph_embed = tf.reduce_sum(
tf.expand_dims(alpha_weight, 3) * head_tail,
axis=2) # batch_size * triple_num * 100
"""
[0, 1, 2... encoder_batch_size] 转化成 encoder_batch_size * 1 * 1 的矩阵 [[[0]], [[1]], [[2]],...]
tf.tile 将矩阵的第 1 维进行扩展 encoder_batch_size * encoder_len * 1 [[[0],[0]...]],...]
与 posts_triple 在第 2 维度上进行拼接，形成 indices 矩阵
indices 矩阵：
[
[[0 0], [0 0], [0 0], [0 0], [0 1], [0 0], [0 2], [0 0],...encoder_len],
[[1 0], [1 0], [1 0], [1 0], [1 1], [1 0], [1 2], [1 0],...encoder_len],
[[2 0], [2 0], [2 0], [2 0], [2 1], [2 0], [2 2], [2 0],...encoder_len]
,...batch_size
]
tf.gather_nd 将 graph_embed 中根据上面矩阵提供的索引检索图向量，再回填至 indices 矩阵
encoder_batch_size * encoder_len * 100
"""
graph_embed_input = tf.gather_nd(
graph_embed,
tf.concat([
tf.tile(
tf.reshape(tf.range(encoder_batch_size, dtype=tf.int32),
[-1, 1, 1]), [1, encoder_len, 1]),
self.posts_triple
],
axis=2))
# 将 responses_triple 转化成实体嵌入 batch_size * decoder_len * 300
triple_embed_input = tf.reshape(
tf.nn.embedding_lookup(
self.entity_embed,
self.entity2index.lookup(self.responses_triple)),
[batch_size, decoder_len, 3 * num_trans_units])
# 将 posts_word_id 转化成词嵌入
post_word_input = tf.nn.embedding_lookup(
self.embed, self.posts_word_id) # batch_size * encoder_len * 300
# 将 responses_word_id 转化成词嵌入
response_word_input = tf.nn.embedding_lookup(
self.embed,
self.responses_word_id) # batch_size * decoder_len * 300
# post_word_input, graph_embed_input 在第二个维度上拼接
self.encoder_input = tf.concat(
[post_word_input, graph_embed_input],
axis=2) # batch_size * encoder_len * 400
# response_word_input, triple_embed_input 在第二个维度上拼接
self.decoder_input = tf.concat(
[response_word_input, triple_embed_input],
axis=2) # batch_size * decoder_len * 600
# 构造 deep RNN
encoder_cell = MultiRNNCell(
[GRUCell(num_units) for _ in range(num_layers)])
decoder_cell = MultiRNNCell(
[GRUCell(num_units) for _ in range(num_layers)])
# rnn encoder
encoder_output, encoder_state = dynamic_rnn(encoder_cell,
self.encoder_input,
self.posts_length,
dtype=tf.float32,
scope="encoder")
# 由于词汇表维度过大，所以输出的维度不可能和词汇表一样。通过 projection 函数，可以实现从低维向高维的映射
# 返回：输出函数，选择器函数，计算序列损失，采样序列损失，总体损失的函数
output_fn, selector_fn, sequence_loss, sampled_sequence_loss, total_loss = output_projection_layer(
num_units, num_symbols, num_samples)
# 用于训练的 decoder
with tf.variable_scope('decoder'):
# 得到注意力函数
# 准备注意力
# attention_keys_init: 注意力的 keys
# attention_values_init: 注意力的 values
# attention_score_fn_init: 计算注意力上下文的函数
# attention_construct_fn_init: 计算所有上下文拼接的函数
attention_keys_init, attention_values_init, attention_score_fn_init, attention_construct_fn_init \
= prepare_attention(encoder_output, 'bahdanau', num_units, imem=(graph_embed, triples_embedding), output_alignments=output_alignments and mem_use)#'luong', num_units)
# 返回训练时解码器每一个时间步对输入的处理函数
decoder_fn_train = attention_decoder_fn_train(
encoder_state,
attention_keys_init,
attention_values_init,
attention_score_fn_init,
attention_construct_fn_init,
output_alignments=output_alignments and mem_use,
max_length=tf.reduce_max(self.responses_length))
# 输出，最终状态，alignments 的 TensorArray
self.decoder_output, _, alignments_ta = dynamic_rnn_decoder(
decoder_cell,
decoder_fn_train,
self.decoder_input,
self.responses_length,
scope="decoder_rnn")
if output_alignments:
self.decoder_loss, self.ppx_loss, self.sentence_ppx = total_loss(
self.decoder_output, self.responses_target,
self.decoder_mask, self.alignments, triples_embedding,
use_triples, one_hot_triples)
self.sentence_ppx = tf.identity(
self.sentence_ppx,
name='ppx_loss') # 将 sentence_ppx 转化成一步操作
else:
self.decoder_loss = sequence_loss(self.decoder_output,
self.responses_target,
self.decoder_mask)
# 用于推导的 decoder
with tf.variable_scope('decoder', reuse=True):
# 得到注意力函数
attention_keys, attention_values, attention_score_fn, attention_construct_fn \
= prepare_attention(encoder_output, 'bahdanau', num_units, reuse=True, imem=(graph_embed, triples_embedding), output_alignments=output_alignments and mem_use)#'luong', num_units)
decoder_fn_inference = attention_decoder_fn_inference(
output_fn,
encoder_state,
attention_keys,
attention_values,
attention_score_fn,
attention_construct_fn,
self.embed,
GO_ID,
EOS_ID,
max_length,
num_symbols,
imem=(entities_word_embedding,
tf.reshape(
triples_embedding,
[encoder_batch_size, -1, 3 * num_trans_units])),
selector_fn=selector_fn)
# imem: ([batch_size,triple_num*triple_len,num_embed_units],[encoder_batch_size, triple_num*triple_len, 3*num_trans_units]) 实体次嵌入和三元组嵌入的元组
self.decoder_distribution, _, output_ids_ta = dynamic_rnn_decoder(
decoder_cell, decoder_fn_inference, scope="decoder_rnn")
output_len = tf.shape(self.decoder_distribution)[1] # decoder_len
output_ids = tf.transpose(
output_ids_ta.gather(
tf.range(output_len))) # [batch_size, decoder_len]
# 对 output 的值域行裁剪
word_ids = tf.cast(tf.clip_by_value(output_ids, 0, num_symbols),
tf.int64) # [batch_size, decoder_len]
# 计算的是采用的实体词在 entities 的位置
# 1、tf.shape(entities_word_embedding)[1] = triple_num*triple_len
# 2、tf.range(encoder_batch_size): [batch_size]
# 3、tf.reshape(tf.range(encoder_batch_size) * tf.shape(entities_word_embedding)[1], [-1, 1]): [batch_size, 1] 实体词在 entities 中的偏移量
# 4、tf.clip_by_value(-output_ids, 0, num_symbols): [batch_size, decoder_len] 实体词的相对位置
# 5、entity_ids: [batch_size * decoder_len] 加上偏移量之后在 entities 中的实际位置
entity_ids = tf.reshape(
tf.clip_by_value(-output_ids, 0, num_symbols) + tf.reshape(
tf.range(encoder_batch_size) *
tf.shape(entities_word_embedding)[1], [-1, 1]), [-1])
# 计算的是所用的实体词
# 1、entities: [batch_size, triple_num, triple_len]
# 2、tf.reshape(self.entities, [-1]): [batch_size * triple_num * triple_len]
# 3、tf.gather: [batch_size*decoder_len]
# 4、entities: [batch_size, output_len]
entities = tf.reshape(
tf.gather(tf.reshape(self.entities, [-1]), entity_ids),
[-1, output_len])
words = self.index2symbol.lookup(word_ids) # 将 id 转化为实际的词
# output_ids > 0 为 bool 张量，True 的位置用 words 中该位置的词替换
self.generation = tf.where(output_ids > 0, words, entities)
self.generation = tf.identity(self.generation, name='generation')
# 初始化训练过程
self.learning_rate = tf.Variable(float(learning_rate),
trainable=False,
dtype=tf.float32)
# ？？？
self.learning_rate_decay_op = self.learning_rate.assign(
self.learning_rate * learning_rate_decay_factor)
# 更新参数的次数
self.global_step = tf.Variable(0, trainable=False)
# 要训练的参数
self.params = tf.global_variables()
# 选择优化算法
opt = tf.train.AdamOptimizer(learning_rate=learning_rate)
self.lr = opt._lr
# 根据 decoder_loss 计算 params 梯度
gradients = tf.gradients(self.decoder_loss, self.params)
# 梯度裁剪
clipped_gradients, self.gradient_norm = tf.clip_by_global_norm(
gradients, max_gradient_norm)
self.update = opt.apply_gradients(zip(clipped_gradients, self.params),
global_step=self.global_step)
tf.summary.scalar('decoder_loss', self.decoder_loss)
for each in tf.trainable_variables():
tf.summary.histogram(each.name, each)
self.merged_summary_op = tf.summary.merge_all()
self.saver = tf.train.Saver(write_version=tf.train.SaverDef.V2,
max_to_keep=3,
pad_step_number=True,
keep_checkpoint_every_n_hours=1.0)
self.saver_epoch = tf.train.Saver(write_version=tf.train.SaverDef.V2,
max_to_keep=1000,
pad_step_number=True)
def __init__(self,
num_symbols,
num_embed_units,
num_units,
num_layers,
embed,
entity_embed=None,
num_entities=0,
num_trans_units=100,
learning_rate=0.0001,
learning_rate_decay_factor=0.95,
max_gradient_norm=5.0,
num_samples=500,
max_length=60,
mem_use=True,
output_alignments=True,
use_lstm=False):
# 输入数据占位定义
self.posts = tf.placeholder(tf.string, (None, None),
'enc_inps') # batch*len
self.posts_length = tf.placeholder(tf.int32, (None),
'enc_lens') # batch
self.responses = tf.placeholder(tf.string, (None, None),
'dec_inps') # batch*len
self.responses_length = tf.placeholder(tf.int32, (None),
'dec_lens') # batch
self.entities = tf.placeholder(tf.string, (None, None, None),
'entities') # batch
self.entity_masks = tf.placeholder(tf.string, (None, None),
'entity_masks') # batch
self.triples = tf.placeholder(tf.string, (None, None, None, 3),
'triples') # batch
self.posts_triple = tf.placeholder(tf.int32, (None, None, 1),
'enc_triples') # batch
self.responses_triple = tf.placeholder(tf.string, (None, None, 3),
'dec_triples') # batch
self.match_triples = tf.placeholder(tf.int32, (None, None, None),
'match_triples') # batch
encoder_batch_size, encoder_len = tf.unstack(tf.shape(self.posts))
triple_num = tf.shape(self.triples)[1]
triple_len = tf.shape(self.triples)[2]
one_hot_triples = tf.one_hot(self.match_triples, triple_len)
use_triples = tf.reduce_sum(one_hot_triples, axis=[2, 3])
# 构建词汇查询talbe (index to string, string to index)
self.symbol2index = MutableHashTable(key_dtype=tf.string,
value_dtype=tf.int64,
default_value=UNK_ID,
shared_name="in_table",
name="in_table",
checkpoint=True)
self.index2symbol = MutableHashTable(key_dtype=tf.int64,
value_dtype=tf.string,
default_value='_UNK',
shared_name="out_table",
name="out_table",
checkpoint=True)
self.entity2index = MutableHashTable(key_dtype=tf.string,
value_dtype=tf.int64,
default_value=NONE_ID,
shared_name="entity_in_table",
name="entity_in_table",
checkpoint=True)
self.index2entity = MutableHashTable(key_dtype=tf.int64,
value_dtype=tf.string,
default_value='_NONE',
shared_name="entity_out_table",
name="entity_out_table",
checkpoint=True)
self.posts_word_id = self.symbol2index.lookup(self.posts) # batch*len
self.posts_entity_id = self.entity2index.lookup(
self.posts) # batch*len
#self.posts_word_id = tf.Print(self.posts_word_id, ['use_triples', use_triples, 'one_hot_triples', one_hot_triples], summarize=1e6)
self.responses_target = self.symbol2index.lookup(
self.responses) # batch*len
batch_size, decoder_len = tf.shape(self.responses)[0], tf.shape(
self.responses)[1]
self.responses_word_id = tf.concat([
tf.ones([batch_size, 1], dtype=tf.int64) * GO_ID,
tf.split(self.responses_target, [decoder_len - 1, 1], 1)[0]
], 1) # batch*len
self.decoder_mask = tf.reshape(
tf.cumsum(tf.one_hot(self.responses_length - 1, decoder_len),
reverse=True,
axis=1), [-1, decoder_len])
# 构建词嵌入 table (index to vector)
if embed is None:
# 随机初始化词嵌入
self.embed = tf.get_variable('word_embed',
[num_symbols, num_embed_units],
tf.float32)
else:
# 使用预训练的词嵌入初始化 (pre-trained word vectors, GloVe or Word2Vec)
self.embed = tf.get_variable('word_embed',
dtype=tf.float32,
initializer=embed)
if entity_embed is None:
# 随机初始化词嵌入
self.entity_trans = tf.get_variable(
'entity_embed', [num_entities, num_trans_units],
tf.float32,
trainable=False)
else:
# 使用预训练的词嵌入初始化 (pre-trained word vectors, GloVe or Word2Vec)
self.entity_trans = tf.get_variable('entity_embed',
dtype=tf.float32,
initializer=entity_embed,
trainable=False)
self.entity_trans_transformed = tf.layers.dense(
self.entity_trans,
num_trans_units,
activation=tf.tanh,
name='trans_transformation')
padding_entity = tf.get_variable('entity_padding_embed',
[7, num_trans_units],
dtype=tf.float32,
initializer=tf.zeros_initializer())
self.entity_embed = tf.concat(
[padding_entity, self.entity_trans_transformed], axis=0)
triples_embedding = tf.reshape(
tf.nn.embedding_lookup(self.entity_embed,
self.entity2index.lookup(self.triples)),
[encoder_batch_size, triple_num, -1, 3 * num_trans_units])
entities_word_embedding = tf.reshape(
tf.nn.embedding_lookup(self.embed,
self.symbol2index.lookup(self.entities)),
[encoder_batch_size, -1, num_embed_units])
head, relation, tail = tf.split(triples_embedding,
[num_trans_units] * 3,
axis=3)
# 知识融合层的静态注意力
with tf.variable_scope('graph_attention'):
# 拼接head tail
head_tail = tf.concat([head, tail], axis=3)
# head tail合成一个向量
head_tail_transformed = tf.layers.dense(head_tail,
num_trans_units,
activation=tf.tanh,
name='head_tail_transform')
# relation 向量
relation_transformed = tf.layers.dense(relation,
num_trans_units,
name='relation_transform')
# relation 和 head_tail 计算注意力权重
e_weight = tf.reduce_sum(relation_transformed *
head_tail_transformed,
axis=3)
# 将注意力权重归一化
alpha_weight = tf.nn.softmax(e_weight)
# 将权重和head_tail进行加权求和
graph_embed = tf.reduce_sum(tf.expand_dims(alpha_weight, 3) *
head_tail,
axis=2)
graph_embed_input = tf.gather_nd(
graph_embed,
tf.concat([
tf.tile(
tf.reshape(tf.range(encoder_batch_size, dtype=tf.int32),
[-1, 1, 1]), [1, encoder_len, 1]),
self.posts_triple
],
axis=2))
triple_embed_input = tf.reshape(
tf.nn.embedding_lookup(
self.entity_embed,
self.entity2index.lookup(self.responses_triple)),
[batch_size, decoder_len, 3 * num_trans_units])
post_word_input = tf.nn.embedding_lookup(
self.embed, self.posts_word_id) # batch*len*unit
response_word_input = tf.nn.embedding_lookup(
self.embed, self.responses_word_id) # batch*len*unit
# 在输入语句中拼接注意力机制计算出来的图谱信息
self.encoder_input = tf.concat([post_word_input, graph_embed_input],
axis=2)
# 在输出语句中拼接所有图谱信息
self.decoder_input = tf.concat(
[response_word_input, triple_embed_input], axis=2)
# 编码器使用GRUCell， num_layers为网络层数
encoder_cell = MultiRNNCell(
[GRUCell(num_units) for _ in range(num_layers)])
# 解码器层使用GRUCell，num_layers为网络层数
decoder_cell = MultiRNNCell(
[GRUCell(num_units) for _ in range(num_layers)])
# RNN编码器的包装
encoder_output, encoder_state = dynamic_rnn(encoder_cell,
self.encoder_input,
self.posts_length,
dtype=tf.float32,
scope="encoder")
# get output projection function
output_fn, selector_fn, sequence_loss, sampled_sequence_loss, total_loss = output_projection_layer(
num_units, num_symbols, num_samples)
# 解码器
with tf.variable_scope('decoder'):
# 获取 attention 函数
attention_keys_init, attention_values_init, attention_score_fn_init, attention_construct_fn_init \
= prepare_attention(encoder_output, 'bahdanau', num_units, imem=(graph_embed, triples_embedding), output_alignments=output_alignments and mem_use) # 'luong', num_units)
decoder_fn_train = attention_decoder_fn_train(
encoder_state,
attention_keys_init,
attention_values_init,
attention_score_fn_init,
attention_construct_fn_init,
output_alignments=output_alignments and mem_use,
max_length=tf.reduce_max(self.responses_length))
self.decoder_output, _, alignments_ta = dynamic_rnn_decoder(
decoder_cell,
decoder_fn_train,
self.decoder_input,
self.responses_length,
scope="decoder_rnn")
if output_alignments:
self.alignments = tf.transpose(alignments_ta.stack(),
perm=[1, 0, 2, 3])
self.decoder_loss, self.ppx_loss, self.sentence_ppx = total_loss(
self.decoder_output, self.responses_target,
self.decoder_mask, self.alignments, triples_embedding,
use_triples, one_hot_triples)
self.sentence_ppx = tf.identity(self.sentence_ppx,
name='ppx_loss')
else:
self.decoder_loss = sequence_loss(self.decoder_output,
self.responses_target,
self.decoder_mask)
with tf.variable_scope('decoder', reuse=True):
# 获取 attention 函数
attention_keys, attention_values, attention_score_fn, attention_construct_fn \
= prepare_attention(encoder_output, 'bahdanau', num_units, reuse=True, imem=(graph_embed, triples_embedding), output_alignments=output_alignments and mem_use) # 'luong', num_units)
decoder_fn_inference = attention_decoder_fn_inference(
output_fn,
encoder_state,
attention_keys,
attention_values,
attention_score_fn,
attention_construct_fn,
self.embed,
GO_ID,
EOS_ID,
max_length,
num_symbols,
imem=(entities_word_embedding,
tf.reshape(
triples_embedding,
[encoder_batch_size, -1, 3 * num_trans_units])),
selector_fn=selector_fn)
self.decoder_distribution, _, output_ids_ta = dynamic_rnn_decoder(
decoder_cell, decoder_fn_inference, scope="decoder_rnn")
output_len = tf.shape(self.decoder_distribution)[1]
output_ids = tf.transpose(
output_ids_ta.gather(tf.range(output_len)))
word_ids = tf.cast(tf.clip_by_value(output_ids, 0, num_symbols),
tf.int64)
entity_ids = tf.reshape(
tf.clip_by_value(-output_ids, 0, num_symbols) + tf.reshape(
tf.range(encoder_batch_size) *
tf.shape(entities_word_embedding)[1], [-1, 1]), [-1])
entities = tf.reshape(
tf.gather(tf.reshape(self.entities, [-1]), entity_ids),
[-1, output_len])
words = self.index2symbol.lookup(word_ids)
# 生成用于输出的回复语句
self.generation = tf.where(output_ids > 0, words, entities)
self.generation = tf.identity(self.generation, name='generation')
# 训练参数初始化
self.learning_rate = tf.Variable(float(learning_rate),
trainable=False,
dtype=tf.float32)
self.learning_rate_decay_op = self.learning_rate.assign(
self.learning_rate * learning_rate_decay_factor)
self.global_step = tf.Variable(0, trainable=False)
self.params = tf.global_variables()
# 使用Adam优化器，计算高效、梯度平滑、参数调节简单
opt = tf.train.AdamOptimizer(learning_rate=learning_rate)
self.lr = opt._lr
gradients = tf.gradients(self.decoder_loss, self.params)
clipped_gradients, self.gradient_norm = tf.clip_by_global_norm(
gradients, max_gradient_norm)
self.update = opt.apply_gradients(zip(clipped_gradients, self.params),
global_step=self.global_step)
tf.summary.scalar('decoder_loss', self.decoder_loss)
for each in tf.trainable_variables():
tf.summary.histogram(each.name, each)
self.merged_summary_op = tf.summary.merge_all()
self.saver = tf.train.Saver(write_version=tf.train.SaverDef.V2,
max_to_keep=3,
pad_step_number=True,
keep_checkpoint_every_n_hours=1.0)
self.saver_epoch = tf.train.Saver(write_version=tf.train.SaverDef.V2,
max_to_keep=1000,
pad_step_number=True)