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,
num_embed_units,
num_units,
num_layers,
is_train,
vocab=None,
embed=None,
learning_rate=0.1,
learning_rate_decay_factor=0.95,
max_gradient_norm=5.0,
num_samples=512,
max_length=30,
use_lstm=True):
self.posts_1 = tf.placeholder(tf.string, shape=(None, None))
self.posts_2 = tf.placeholder(tf.string, shape=(None, None))
self.posts_3 = tf.placeholder(tf.string, shape=(None, None))
self.posts_4 = tf.placeholder(tf.string, shape=(None, None))
self.entity_1 = tf.placeholder(tf.string, shape=(None, None, None, 3))
self.entity_2 = tf.placeholder(tf.string, shape=(None, None, None, 3))
self.entity_3 = tf.placeholder(tf.string, shape=(None, None, None, 3))
self.entity_4 = tf.placeholder(tf.string, shape=(None, None, None, 3))
self.entity_mask_1 = tf.placeholder(tf.float32,
shape=(None, None, None))
self.entity_mask_2 = tf.placeholder(tf.float32,
shape=(None, None, None))
self.entity_mask_3 = tf.placeholder(tf.float32,
shape=(None, None, None))
self.entity_mask_4 = tf.placeholder(tf.float32,
shape=(None, None, None))
self.posts_length_1 = tf.placeholder(tf.int32, shape=(None))
self.posts_length_2 = tf.placeholder(tf.int32, shape=(None))
self.posts_length_3 = tf.placeholder(tf.int32, shape=(None))
self.posts_length_4 = tf.placeholder(tf.int32, shape=(None))
self.responses = tf.placeholder(tf.string, shape=(None, None))
self.responses_length = tf.placeholder(tf.int32, shape=(None))
self.epoch = tf.Variable(0, trainable=False, name='epoch')
self.epoch_add_op = self.epoch.assign(self.epoch + 1)
if is_train:
self.symbols = tf.Variable(vocab, trainable=False, name="symbols")
else:
self.symbols = tf.Variable(np.array(['.'] * num_symbols),
name="symbols")
self.symbol2index = HashTable(KeyValueTensorInitializer(
self.symbols,
tf.Variable(
np.array([i for i in range(num_symbols)], dtype=np.int32),
False)),
default_value=UNK_ID,
name="symbol2index")
self.posts_input_1 = self.symbol2index.lookup(self.posts_1)
self.posts_2_target = self.posts_2_embed = self.symbol2index.lookup(
self.posts_2)
self.posts_3_target = self.posts_3_embed = self.symbol2index.lookup(
self.posts_3)
self.posts_4_target = self.posts_4_embed = self.symbol2index.lookup(
self.posts_4)
self.responses_target = self.symbol2index.lookup(self.responses)
batch_size, decoder_len = tf.shape(self.posts_1)[0], tf.shape(
self.responses)[1]
self.posts_input_2 = tf.concat([
tf.ones([batch_size, 1], dtype=tf.int32) * GO_ID,
tf.split(self.posts_2_embed, [tf.shape(self.posts_2)[1] - 1, 1],
1)[0]
], 1)
self.posts_input_3 = tf.concat([
tf.ones([batch_size, 1], dtype=tf.int32) * GO_ID,
tf.split(self.posts_3_embed, [tf.shape(self.posts_3)[1] - 1, 1],
1)[0]
], 1)
self.posts_input_4 = tf.concat([
tf.ones([batch_size, 1], dtype=tf.int32) * GO_ID,
tf.split(self.posts_4_embed, [tf.shape(self.posts_4)[1] - 1, 1],
1)[0]
], 1)
self.responses_target = self.symbol2index.lookup(self.responses)
batch_size, decoder_len = tf.shape(self.posts_1)[0], tf.shape(
self.responses)[1]
self.responses_input = tf.concat([
tf.ones([batch_size, 1], dtype=tf.int32) * GO_ID,
tf.split(self.responses_target, [decoder_len - 1, 1], 1)[0]
], 1)
self.encoder_2_mask = tf.reshape(
tf.cumsum(tf.one_hot(self.posts_length_2 - 1,
tf.shape(self.posts_2)[1]),
reverse=True,
axis=1), [-1, tf.shape(self.posts_2)[1]])
self.encoder_3_mask = tf.reshape(
tf.cumsum(tf.one_hot(self.posts_length_3 - 1,
tf.shape(self.posts_3)[1]),
reverse=True,
axis=1), [-1, tf.shape(self.posts_3)[1]])
self.encoder_4_mask = tf.reshape(
tf.cumsum(tf.one_hot(self.posts_length_4 - 1,
tf.shape(self.posts_4)[1]),
reverse=True,
axis=1), [-1, tf.shape(self.posts_4)[1]])
self.decoder_mask = tf.reshape(
tf.cumsum(tf.one_hot(self.responses_length - 1, decoder_len),
reverse=True,
axis=1), [-1, decoder_len])
if embed is None:
self.embed = tf.get_variable('embed',
[num_symbols, num_embed_units],
tf.float32)
else:
self.embed = tf.get_variable('embed',
dtype=tf.float32,
initializer=embed)
self.encoder_input_1 = tf.nn.embedding_lookup(self.embed,
self.posts_input_1)
self.encoder_input_2 = tf.nn.embedding_lookup(self.embed,
self.posts_input_2)
self.encoder_input_3 = tf.nn.embedding_lookup(self.embed,
self.posts_input_3)
self.encoder_input_4 = tf.nn.embedding_lookup(self.embed,
self.posts_input_4)
self.decoder_input = tf.nn.embedding_lookup(self.embed,
self.responses_input)
entity_embedding_1 = tf.reshape(
tf.nn.embedding_lookup(self.embed,
self.symbol2index.lookup(self.entity_1)),
[
batch_size,
tf.shape(self.entity_1)[1],
tf.shape(self.entity_1)[2], 3 * num_embed_units
])
entity_embedding_2 = tf.reshape(
tf.nn.embedding_lookup(self.embed,
self.symbol2index.lookup(self.entity_2)),
[
batch_size,
tf.shape(self.entity_2)[1],
tf.shape(self.entity_2)[2], 3 * num_embed_units
])
entity_embedding_3 = tf.reshape(
tf.nn.embedding_lookup(self.embed,
self.symbol2index.lookup(self.entity_3)),
[
batch_size,
tf.shape(self.entity_3)[1],
tf.shape(self.entity_3)[2], 3 * num_embed_units
])
entity_embedding_4 = tf.reshape(
tf.nn.embedding_lookup(self.embed,
self.symbol2index.lookup(self.entity_4)),
[
batch_size,
tf.shape(self.entity_4)[1],
tf.shape(self.entity_4)[2], 3 * num_embed_units
])
head_1, relation_1, tail_1 = tf.split(entity_embedding_1,
[num_embed_units] * 3,
axis=3)
head_2, relation_2, tail_2 = tf.split(entity_embedding_2,
[num_embed_units] * 3,
axis=3)
head_3, relation_3, tail_3 = tf.split(entity_embedding_3,
[num_embed_units] * 3,
axis=3)
head_4, relation_4, tail_4 = tf.split(entity_embedding_4,
[num_embed_units] * 3,
axis=3)
with tf.variable_scope('graph_attention'):
#[batch_size, max_reponse_length, max_triple_num, 2*embed_units]
head_tail_1 = tf.concat([head_1, tail_1], axis=3)
#[batch_size, max_reponse_length, max_triple_num, embed_units]
head_tail_transformed_1 = tf.layers.dense(
head_tail_1,
num_embed_units,
activation=tf.tanh,
name='head_tail_transform')
#[batch_size, max_reponse_length, max_triple_num, embed_units]
relation_transformed_1 = tf.layers.dense(relation_1,
num_embed_units,
name='relation_transform')
#[batch_size, max_reponse_length, max_triple_num]
e_weight_1 = tf.reduce_sum(relation_transformed_1 *
head_tail_transformed_1,
axis=3)
#[batch_size, max_reponse_length, max_triple_num]
alpha_weight_1 = tf.nn.softmax(e_weight_1)
#[batch_size, max_reponse_length, embed_units]
graph_embed_1 = tf.reduce_sum(
tf.expand_dims(alpha_weight_1, 3) *
(tf.expand_dims(self.entity_mask_1, 3) * head_tail_1),
axis=2)
with tf.variable_scope('graph_attention', reuse=True):
head_tail_2 = tf.concat([head_2, tail_2], axis=3)
head_tail_transformed_2 = tf.layers.dense(
head_tail_2,
num_embed_units,
activation=tf.tanh,
name='head_tail_transform')
relation_transformed_2 = tf.layers.dense(relation_2,
num_embed_units,
name='relation_transform')
e_weight_2 = tf.reduce_sum(relation_transformed_2 *
head_tail_transformed_2,
axis=3)
alpha_weight_2 = tf.nn.softmax(e_weight_2)
graph_embed_2 = tf.reduce_sum(
tf.expand_dims(alpha_weight_2, 3) *
(tf.expand_dims(self.entity_mask_2, 3) * head_tail_2),
axis=2)
with tf.variable_scope('graph_attention', reuse=True):
head_tail_3 = tf.concat([head_3, tail_3], axis=3)
head_tail_transformed_3 = tf.layers.dense(
head_tail_3,
num_embed_units,
activation=tf.tanh,
name='head_tail_transform')
relation_transformed_3 = tf.layers.dense(relation_3,
num_embed_units,
name='relation_transform')
e_weight_3 = tf.reduce_sum(relation_transformed_3 *
head_tail_transformed_3,
axis=3)
alpha_weight_3 = tf.nn.softmax(e_weight_3)
graph_embed_3 = tf.reduce_sum(
tf.expand_dims(alpha_weight_3, 3) *
(tf.expand_dims(self.entity_mask_3, 3) * head_tail_3),
axis=2)
with tf.variable_scope('graph_attention', reuse=True):
head_tail_4 = tf.concat([head_4, tail_4], axis=3)
head_tail_transformed_4 = tf.layers.dense(
head_tail_4,
num_embed_units,
activation=tf.tanh,
name='head_tail_transform')
relation_transformed_4 = tf.layers.dense(relation_4,
num_embed_units,
name='relation_transform')
e_weight_4 = tf.reduce_sum(relation_transformed_4 *
head_tail_transformed_4,
axis=3)
alpha_weight_4 = tf.nn.softmax(e_weight_4)
graph_embed_4 = tf.reduce_sum(
tf.expand_dims(alpha_weight_4, 3) *
(tf.expand_dims(self.entity_mask_4, 3) * head_tail_4),
axis=2)
if use_lstm:
cell = MultiRNNCell([LSTMCell(num_units)] * num_layers)
else:
cell = MultiRNNCell([GRUCell(num_units)] * num_layers)
output_fn, sampled_sequence_loss = output_projection_layer(
num_units, num_symbols, num_samples)
encoder_output_1, encoder_state_1 = dynamic_rnn(cell,
self.encoder_input_1,
self.posts_length_1,
dtype=tf.float32,
scope="encoder")
attention_keys_1, attention_values_1, attention_score_fn_1, attention_construct_fn_1 \
= attention_decoder_fn.prepare_attention(graph_embed_1, encoder_output_1, 'luong', num_units)
decoder_fn_train_1 = attention_decoder_fn.attention_decoder_fn_train(
encoder_state_1,
attention_keys_1,
attention_values_1,
attention_score_fn_1,
attention_construct_fn_1,
max_length=tf.reduce_max(self.posts_length_2))
encoder_output_2, encoder_state_2, alignments_ta_2 = dynamic_rnn_decoder(
cell,
decoder_fn_train_1,
self.encoder_input_2,
self.posts_length_2,
scope="decoder")
self.alignments_2 = tf.transpose(alignments_ta_2.stack(),
perm=[1, 0, 2])
self.decoder_loss_2 = sampled_sequence_loss(encoder_output_2,
self.posts_2_target,
self.encoder_2_mask)
with variable_scope.variable_scope('', reuse=True):
attention_keys_2, attention_values_2, attention_score_fn_2, attention_construct_fn_2 \
= attention_decoder_fn.prepare_attention(graph_embed_2, encoder_output_2, 'luong', num_units)
decoder_fn_train_2 = attention_decoder_fn.attention_decoder_fn_train(
encoder_state_2,
attention_keys_2,
attention_values_2,
attention_score_fn_2,
attention_construct_fn_2,
max_length=tf.reduce_max(self.posts_length_3))
encoder_output_3, encoder_state_3, alignments_ta_3 = dynamic_rnn_decoder(
cell,
decoder_fn_train_2,
self.encoder_input_3,
self.posts_length_3,
scope="decoder")
self.alignments_3 = tf.transpose(alignments_ta_3.stack(),
perm=[1, 0, 2])
self.decoder_loss_3 = sampled_sequence_loss(
encoder_output_3, self.posts_3_target, self.encoder_3_mask)
attention_keys_3, attention_values_3, attention_score_fn_3, attention_construct_fn_3 \
= attention_decoder_fn.prepare_attention(graph_embed_3, encoder_output_3, 'luong', num_units)
decoder_fn_train_3 = attention_decoder_fn.attention_decoder_fn_train(
encoder_state_3,
attention_keys_3,
attention_values_3,
attention_score_fn_3,
attention_construct_fn_3,
max_length=tf.reduce_max(self.posts_length_4))
encoder_output_4, encoder_state_4, alignments_ta_4 = dynamic_rnn_decoder(
cell,
decoder_fn_train_3,
self.encoder_input_4,
self.posts_length_4,
scope="decoder")
self.alignments_4 = tf.transpose(alignments_ta_4.stack(),
perm=[1, 0, 2])
self.decoder_loss_4 = sampled_sequence_loss(
encoder_output_4, self.posts_4_target, self.encoder_4_mask)
attention_keys, attention_values, attention_score_fn, attention_construct_fn \
= attention_decoder_fn.prepare_attention(graph_embed_4, encoder_output_4, 'luong', num_units)
if is_train:
with variable_scope.variable_scope('', reuse=True):
decoder_fn_train = attention_decoder_fn.attention_decoder_fn_train(
encoder_state_4,
attention_keys,
attention_values,
attention_score_fn,
attention_construct_fn,
max_length=tf.reduce_max(self.responses_length))
self.decoder_output, _, alignments_ta = dynamic_rnn_decoder(
cell,
decoder_fn_train,
self.decoder_input,
self.responses_length,
scope="decoder")
self.alignments = tf.transpose(alignments_ta.stack(),
perm=[1, 0, 2])
self.decoder_loss = sampled_sequence_loss(
self.decoder_output, self.responses_target,
self.decoder_mask)
self.params = tf.trainable_variables()
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)
#opt = tf.train.GradientDescentOptimizer(self.learning_rate)
opt = tf.train.MomentumOptimizer(self.learning_rate, 0.9)
gradients = tf.gradients(
self.decoder_loss + self.decoder_loss_2 + self.decoder_loss_3 +
self.decoder_loss_4, 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)
else:
with variable_scope.variable_scope('', reuse=True):
decoder_fn_inference = attention_decoder_fn.attention_decoder_fn_inference(
output_fn, encoder_state_4, attention_keys,
attention_values, attention_score_fn,
attention_construct_fn, self.embed, GO_ID, EOS_ID,
max_length, num_symbols)
self.decoder_distribution, _, alignments_ta = dynamic_rnn_decoder(
cell, decoder_fn_inference, scope="decoder")
output_len = tf.shape(self.decoder_distribution)[1]
self.alignments = tf.transpose(
alignments_ta.gather(tf.range(output_len)), [1, 0, 2])
self.generation_index = tf.argmax(
tf.split(self.decoder_distribution, [2, num_symbols - 2],
2)[1], 2) + 2 # for removing UNK
self.generation = tf.nn.embedding_lookup(self.symbols,
self.generation_index,
name="generation")
self.params = tf.trainable_variables()
self.saver = tf.train.Saver(tf.global_variables(),
write_version=tf.train.SaverDef.V2,
max_to_keep=10,
pad_step_number=True,
keep_checkpoint_every_n_hours=1.0)
def __init__(self,
num_symbols,
num_embed_units,
num_units,
num_layers,
beam_size,
embed,
learning_rate=0.5,
remove_unk=False,
learning_rate_decay_factor=0.95,
max_gradient_norm=5.0,
num_samples=512,
max_length=8,
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
# 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.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)
# build the vocab table (string to index)
self.posts_input = self.symbol2index.lookup(self.posts) # batch*len
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_input = 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('embed',
[num_symbols, num_embed_units],
tf.float32)
else:
# initialize the embedding by pre-trained word vectors
self.embed = tf.get_variable('embed',
dtype=tf.float32,
initializer=embed)
self.encoder_input = tf.nn.embedding_lookup(
self.embed, self.posts_input) #batch*len*unit
self.decoder_input = tf.nn.embedding_lookup(self.embed,
self.responses_input)
if use_lstm:
cell = MultiRNNCell([LSTMCell(num_units)] * num_layers)
else:
cell = MultiRNNCell([GRUCell(num_units)] * num_layers)
# rnn encoder
encoder_output, encoder_state = dynamic_rnn(cell,
self.encoder_input,
self.posts_length,
dtype=tf.float32,
scope="encoder")
# get output projection function
output_fn, sampled_sequence_loss = output_projection_layer(
num_units, num_symbols, num_samples)
# get attention function
attention_keys, attention_values, attention_score_fn, attention_construct_fn \
= attention_decoder_fn.prepare_attention(encoder_output, 'luong', num_units)
with tf.variable_scope('decoder'):
decoder_fn_train = attention_decoder_fn.attention_decoder_fn_train(
encoder_state, attention_keys, attention_values,
attention_score_fn, attention_construct_fn)
self.decoder_output, _, _ = dynamic_rnn_decoder(
cell,
decoder_fn_train,
self.decoder_input,
self.responses_length,
scope="decoder_rnn")
self.decoder_loss = sampled_sequence_loss(self.decoder_output,
self.responses_target,
self.decoder_mask)
with tf.variable_scope('decoder', reuse=True):
decoder_fn_inference = attention_decoder_fn.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)
self.decoder_distribution, _, _ = dynamic_rnn_decoder(
cell, decoder_fn_inference, scope="decoder_rnn")
self.generation_index = tf.argmax(
tf.split(self.decoder_distribution, [2, num_symbols - 2],
2)[1], 2) + 2 # for removing UNK
self.generation = self.index2symbol.lookup(self.generation_index,
name='generation')
with tf.variable_scope('decoder', reuse=True):
decoder_fn_beam_inference = attention_decoder_fn_beam_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, beam_size, remove_unk)
_, _, self.context_state = dynamic_rnn_decoder(
cell, decoder_fn_beam_inference, scope="decoder_rnn")
(log_beam_probs, beam_parents, beam_symbols, result_probs,
result_parents, result_symbols) = self.context_state
self.beam_parents = tf.transpose(tf.reshape(
beam_parents.stack(), [max_length + 1, -1, beam_size]),
[1, 0, 2],
name='beam_parents')
self.beam_symbols = tf.transpose(
tf.reshape(beam_symbols.stack(),
[max_length + 1, -1, beam_size]), [1, 0, 2])
self.beam_symbols = self.index2symbol.lookup(tf.cast(
self.beam_symbols, tf.int64),
name="beam_symbols")
self.result_probs = tf.transpose(tf.reshape(
result_probs.stack(), [max_length + 1, -1, beam_size * 2]),
[1, 0, 2],
name='result_probs')
self.result_symbols = tf.transpose(
tf.reshape(result_symbols.stack(),
[max_length + 1, -1, beam_size * 2]), [1, 0, 2])
self.result_parents = tf.transpose(tf.reshape(
result_parents.stack(), [max_length + 1, -1, beam_size * 2]),
[1, 0, 2],
name='result_parents')
self.result_symbols = self.index2symbol.lookup(
tf.cast(self.result_symbols, tf.int64), name='result_symbols')
self.params = tf.trainable_variables()
# calculate the gradient of parameters
opt = tf.train.GradientDescentOptimizer(self.learning_rate)
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)
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)
# Exporter for serving
self.model_exporter = exporter.Exporter(self.saver)
inputs = {"enc_inps:0": self.posts, "enc_lens:0": self.posts_length}
outputs = {
"beam_symbols": self.beam_symbols,
"beam_parents": self.beam_parents,
"result_probs": self.result_probs,
"result_symbols": self.result_symbols,
"result_parents": self.result_parents
}
self.model_exporter.init(tf.get_default_graph().as_graph_def(),
named_graph_signatures={
"inputs":
exporter.generic_signature(inputs),
"outputs":
exporter.generic_signature(outputs)
})
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_qwords, #modify
num_embed_units,
num_units,
num_layers,
is_train,
vocab=None,
embed=None,
question_data=True,
learning_rate=0.5,
learning_rate_decay_factor=0.95,
max_gradient_norm=5.0,
num_samples=512,
max_length=30,
use_lstm=False):
self.posts = tf.placeholder(tf.string, shape=(None, None)) # batch*len
self.posts_length = tf.placeholder(tf.int32, shape=(None)) # batch
self.responses = tf.placeholder(tf.string, shape=(None, None)) # batch*len
self.responses_length = tf.placeholder(tf.int32, shape=(None)) # batch
self.keyword_tensor = tf.placeholder(tf.float32, shape=(None, 3, None)) #(batch * len) * 3 * numsymbol
self.word_type = tf.placeholder(tf.int32, shape=(None)) #(batch * len)
# build the vocab table (string to index)
if is_train:
self.symbols = tf.Variable(vocab, trainable=False, name="symbols")
else:
self.symbols = tf.Variable(np.array(['.']*num_symbols), name="symbols")
self.symbol2index = HashTable(KeyValueTensorInitializer(self.symbols,
tf.Variable(np.array([i for i in range(num_symbols)], dtype=np.int32), False)),
default_value=UNK_ID, name="symbol2index")
self.posts_input = self.symbol2index.lookup(self.posts) # batch*len
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_input = tf.concat([tf.ones([batch_size, 1], dtype=tf.int32)*GO_ID,
tf.split(self.responses_target, [decoder_len-1, 1], 1)[0]], 1) # batch*len
#delete the last column of responses_target) and add 'GO at the front of it.
self.decoder_mask = tf.reshape(tf.cumsum(tf.one_hot(self.responses_length-1,
decoder_len), reverse=True, axis=1), [-1, decoder_len]) # bacth * len
print "embedding..."
# build the embedding table (index to vector)
if embed is None:
# initialize the embedding randomly
self.embed = tf.get_variable('embed', [num_symbols, num_embed_units], tf.float32)
else:
print len(vocab), len(embed), len(embed[0])
print embed
# initialize the embedding by pre-trained word vectors
self.embed = tf.get_variable('embed', dtype=tf.float32, initializer=embed)
self.encoder_input = tf.nn.embedding_lookup(self.embed, self.posts_input) #batch*len*unit
self.decoder_input = tf.nn.embedding_lookup(self.embed, self.responses_input)
print "embedding finished"
if use_lstm:
cell = MultiRNNCell([LSTMCell(num_units)] * num_layers)
else:
cell = MultiRNNCell([GRUCell(num_units)] * num_layers)
# rnn encoder
encoder_output, encoder_state = dynamic_rnn(cell, self.encoder_input,
self.posts_length, dtype=tf.float32, scope="encoder")
# get output projection function
output_fn, sampled_sequence_loss = output_projection_layer(num_units,
num_symbols, num_qwords, num_samples, question_data)
print "encoder_output.shape:", encoder_output.get_shape()
# get attention function
attention_keys, attention_values, attention_score_fn, attention_construct_fn \
= attention_decoder_fn.prepare_attention(encoder_output, 'luong', num_units)
# get decoding loop function
decoder_fn_train = attention_decoder_fn.attention_decoder_fn_train(encoder_state,
attention_keys, attention_values, attention_score_fn, attention_construct_fn)
decoder_fn_inference = attention_decoder_fn.attention_decoder_fn_inference(output_fn,
self.keyword_tensor,
encoder_state, attention_keys, attention_values, attention_score_fn,
attention_construct_fn, self.embed, GO_ID, EOS_ID, max_length, num_symbols)
if is_train:
# rnn decoder
self.decoder_output, _, _ = dynamic_rnn_decoder(cell, decoder_fn_train,
self.decoder_input, self.responses_length, scope="decoder")
# calculate the loss of decoder
# self.decoder_output = tf.Print(self.decoder_output, [self.decoder_output])
self.decoder_loss, self.log_perplexity = sampled_sequence_loss(self.decoder_output,
self.responses_target, self.decoder_mask, self.keyword_tensor, self.word_type)
# building graph finished and get all parameters
self.params = tf.trainable_variables()
for item in tf.trainable_variables():
print item.name, item.get_shape()
# 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)
# calculate the gradient of parameters
opt = tf.train.GradientDescentOptimizer(self.learning_rate)
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)
else:
# rnn decoder
self.decoder_distribution, _, _ = dynamic_rnn_decoder(cell, decoder_fn_inference,
scope="decoder")
print("self.decoder_distribution.shape():",self.decoder_distribution.get_shape())
self.decoder_distribution = tf.Print(self.decoder_distribution, ["distribution.shape()", tf.reduce_sum(self.decoder_distribution)])
# generating the response
self.generation_index = tf.argmax(tf.split(self.decoder_distribution,
[2, num_symbols-2], 2)[1], 2) + 2 # for removing UNK
self.generation = tf.nn.embedding_lookup(self.symbols, self.generation_index)
self.params = tf.trainable_variables()
self.saver = tf.train.Saver(tf.global_variables(), write_version=tf.train.SaverDef.V2,
max_to_keep=3, pad_step_number=True, keep_checkpoint_every_n_hours=1.0)
def __init__(self,
num_symbols,
num_embed_units,
num_units,
is_train,
vocab=None,
content_pos=None,
rhetoric_pos = None,
embed=None,
learning_rate=0.1,
learning_rate_decay_factor=0.9995,
max_gradient_norm=5.0,
max_length=30,
latent_size=128,
use_lstm=False,
num_classes=3,
full_kl_step=80000,
mem_slot_num=4,
mem_size=128):
self.ori_sents = tf.placeholder(tf.string, shape=(None, None))
self.ori_sents_length = tf.placeholder(tf.int32, shape=(None))
self.rep_sents = tf.placeholder(tf.string, shape=(None, None))
self.rep_sents_length = tf.placeholder(tf.int32, shape=(None))
self.labels = tf.placeholder(tf.float32, shape=(None, num_classes))
self.use_prior = tf.placeholder(tf.bool)
self.global_t = tf.placeholder(tf.int32)
self.content_mask = tf.reduce_sum(tf.one_hot(content_pos, num_symbols, 1.0, 0.0), axis = 0)
self.rhetoric_mask = tf.reduce_sum(tf.one_hot(rhetoric_pos, num_symbols, 1.0, 0.0), axis = 0)
topic_memory = tf.zeros(name="topic_memory", dtype=tf.float32,
shape=[None, mem_slot_num, mem_size])
w_topic_memory = tf.get_variable(name="w_topic_memory", dtype=tf.float32,
initializer=tf.random_uniform([mem_size, mem_size], -0.1, 0.1))
# build the vocab table (string to index)
if is_train:
self.symbols = tf.Variable(vocab, trainable=False, name="symbols")
else:
self.symbols = tf.Variable(np.array(['.']*num_symbols), name="symbols")
self.symbol2index = HashTable(KeyValueTensorInitializer(self.symbols,
tf.Variable(np.array([i for i in range(num_symbols)], dtype=np.int32), False)),
default_value=UNK_ID, name="symbol2index")
self.ori_sents_input = self.symbol2index.lookup(self.ori_sents)
self.rep_sents_target = self.symbol2index.lookup(self.rep_sents)
batch_size, decoder_len = tf.shape(self.rep_sents)[0], tf.shape(self.rep_sents)[1]
self.rep_sents_input = tf.concat([tf.ones([batch_size, 1], dtype=tf.int32)*GO_ID,
tf.split(self.rep_sents_target, [decoder_len-1, 1], 1)[0]], 1)
self.decoder_mask = tf.reshape(tf.cumsum(tf.one_hot(self.rep_sents_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('embed', [num_symbols, num_embed_units], tf.float32)
else:
# initialize the embedding by pre-trained word vectors
self.embed = tf.get_variable('embed', dtype=tf.float32, initializer=embed)
self.pattern_embed = tf.get_variable('pattern_embed', [num_classes, num_embed_units], tf.float32)
self.encoder_input = tf.nn.embedding_lookup(self.embed, self.ori_sents_input)
self.decoder_input = tf.nn.embedding_lookup(self.embed, self.rep_sents_input)
if use_lstm:
cell_fw = LSTMCell(num_units)
cell_bw = LSTMCell(num_units)
cell_dec = LSTMCell(2*num_units)
else:
cell_fw = GRUCell(num_units)
cell_bw = GRUCell(num_units)
cell_dec = GRUCell(2*num_units)
# origin sentence encoder
with variable_scope.variable_scope("encoder"):
encoder_output, encoder_state = tf.nn.bidirectional_dynamic_rnn(cell_fw, cell_bw, self.encoder_input,
self.ori_sents_length, dtype=tf.float32)
post_sum_state = tf.concat(encoder_state, 1)
encoder_output = tf.concat(encoder_output, 2)
# response sentence encoder
with variable_scope.variable_scope("encoder", reuse = True):
decoder_state, decoder_last_state = tf.nn.bidirectional_dynamic_rnn(cell_fw, cell_bw, self.decoder_input,
self.rep_sents_length, dtype=tf.float32)
response_sum_state = tf.concat(decoder_last_state, 1)
# recognition network
with variable_scope.variable_scope("recog_net"):
recog_input = tf.concat([post_sum_state, response_sum_state], 1)
recog_mulogvar = tf.contrib.layers.fully_connected(recog_input, latent_size * 2, activation_fn=None, scope="muvar")
recog_mu, recog_logvar = tf.split(recog_mulogvar, 2, axis=1)
# prior network
with variable_scope.variable_scope("prior_net"):
prior_fc1 = tf.contrib.layers.fully_connected(post_sum_state, latent_size * 2, activation_fn=tf.tanh, scope="fc1")
prior_mulogvar = tf.contrib.layers.fully_connected(prior_fc1, latent_size * 2, activation_fn=None, scope="muvar")
prior_mu, prior_logvar = tf.split(prior_mulogvar, 2, axis=1)
latent_sample = tf.cond(self.use_prior,
lambda: sample_gaussian(prior_mu, prior_logvar),
lambda: sample_gaussian(recog_mu, recog_logvar))
# classifier
with variable_scope.variable_scope("classifier"):
classifier_input = latent_sample
pattern_fc1 = tf.contrib.layers.fully_connected(classifier_input, latent_size, activation_fn=tf.tanh, scope="pattern_fc1")
self.pattern_logits = tf.contrib.layers.fully_connected(pattern_fc1, num_classes, activation_fn=None, scope="pattern_logits")
self.label_embedding = tf.matmul(self.labels, self.pattern_embed)
output_fn, my_sequence_loss = output_projection_layer(2*num_units, num_symbols, latent_size, num_embed_units, self.content_mask, self.rhetoric_mask)
attention_keys, attention_values, attention_score_fn, attention_construct_fn = my_attention_decoder_fn.prepare_attention(encoder_output, 'luong', 2*num_units)
with variable_scope.variable_scope("dec_start"):
temp_start = tf.concat([post_sum_state, self.label_embedding, latent_sample], 1)
dec_fc1 = tf.contrib.layers.fully_connected(temp_start, 2*num_units, activation_fn=tf.tanh, scope="dec_start_fc1")
dec_fc2 = tf.contrib.layers.fully_connected(dec_fc1, 2*num_units, activation_fn=None, scope="dec_start_fc2")
if is_train:
# rnn decoder
topic_memory = self.update_memory(topic_memory, encoder_output)
extra_info = tf.concat([self.label_embedding, latent_sample, topic_memory], 1)
decoder_fn_train = my_attention_decoder_fn.attention_decoder_fn_train(dec_fc2,
attention_keys, attention_values, attention_score_fn, attention_construct_fn, extra_info)
self.decoder_output, _, _ = my_seq2seq.dynamic_rnn_decoder(cell_dec, decoder_fn_train,
self.decoder_input, self.rep_sents_length, scope = "decoder")
# calculate the loss
self.decoder_loss = my_loss.sequence_loss(logits = self.decoder_output,
targets = self.rep_sents_target, weights = self.decoder_mask,
extra_information = latent_sample, label_embedding = self.label_embedding, softmax_loss_function = my_sequence_loss)
temp_klloss = tf.reduce_mean(gaussian_kld(recog_mu, recog_logvar, prior_mu, prior_logvar))
self.kl_weight = tf.minimum(tf.to_float(self.global_t)/full_kl_step, 1.0)
self.klloss = self.kl_weight * temp_klloss
temp_labels = tf.argmax(self.labels, 1)
self.classifierloss = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(logits=self.pattern_logits, labels=temp_labels))
self.loss = self.decoder_loss + self.klloss + self.classifierloss # need to anneal the kl_weight
# building graph finished and get all parameters
self.params = tf.trainable_variables()
# 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)
# calculate the gradient of parameters
opt = tf.train.MomentumOptimizer(self.learning_rate, 0.9)
gradients = tf.gradients(self.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)
else:
# rnn decoder
topic_memory = self.update_memory(topic_memory, encoder_output)
extra_info = tf.concat([self.label_embedding, latent_sample, topic_memory], 1)
decoder_fn_inference = my_attention_decoder_fn.attention_decoder_fn_inference(output_fn,
dec_fc2, attention_keys, attention_values, attention_score_fn,
attention_construct_fn, self.embed, GO_ID, EOS_ID, max_length, num_symbols, extra_info)
self.decoder_distribution, _, _ = my_seq2seq.dynamic_rnn_decoder(cell_dec, decoder_fn_inference, scope="decoder")
self.generation_index = tf.argmax(tf.split(self.decoder_distribution,
[2, num_symbols-2], 2)[1], 2) + 2 # for removing UNK
self.generation = tf.nn.embedding_lookup(self.symbols, self.generation_index)
self.params = tf.trainable_variables()
self.saver = tf.train.Saver(tf.global_variables(), write_version=tf.train.SaverDef.V2,
max_to_keep=3, pad_step_number=True, keep_checkpoint_every_n_hours=1.0)
def __init__(self,
num_items,
num_embed_units,
num_units,
num_layers,
embed=None,
learning_rate=1e-4,
action_num=10,
learning_rate_decay_factor=0.95,
max_gradient_norm=5.0,
use_lstm=True):
self.epoch = tf.Variable(0, trainable=False, name='agn/epoch')
self.epoch_add_op = self.epoch.assign(self.epoch + 1)
self.sessions_input = tf.placeholder(tf.int32, shape=(None, None))
self.rec_lists = tf.placeholder(tf.int32, shape=(None, None, None))
self.rec_mask = tf.placeholder(tf.float32, shape=(None, None, None))
self.aims_idx = tf.placeholder(tf.int32, shape=(None, None))
self.sessions_length = tf.placeholder(tf.int32, shape=(None))
self.reward = tf.placeholder(tf.float32, shape=(None))
if embed is None:
self.embed = tf.get_variable(
'agn/embed', [num_items, num_embed_units],
tf.float32,
initializer=tf.truncated_normal_initializer(0, 1))
else:
self.embed = tf.get_variable('agn/embed',
dtype=tf.float32,
initializer=embed)
batch_size, encoder_length, rec_length = tf.shape(
self.sessions_input)[0], tf.shape(
self.sessions_input)[1], tf.shape(self.rec_lists)[2]
encoder_mask = tf.reshape(
tf.cumsum(tf.one_hot(self.sessions_length - 2, encoder_length),
reverse=True,
axis=1), [-1, encoder_length])
# [batch_size, length]
self.sessions_target = tf.concat([
self.sessions_input[:, 1:],
tf.ones([batch_size, 1], dtype=tf.int32) * PAD_ID
], 1)
# [batch_size, length, embed_units]
self.encoder_input = tf.nn.embedding_lookup(self.embed,
self.sessions_input)
# [batch_size, length, rec_length]
self.aims = tf.one_hot(self.aims_idx, rec_length)
if use_lstm:
cell = MultiRNNCell(
[LSTMCell(num_units) for _ in range(num_layers)])
else:
cell = MultiRNNCell(
[GRUCell(num_units) for _ in range(num_layers)])
# Training
with tf.variable_scope("agn"):
output_fn, sampled_sequence_loss = output_projection_layer(
num_units, num_items)
self.encoder_output, self.encoder_state = dynamic_rnn(
cell,
self.encoder_input,
self.sessions_length,
dtype=tf.float32,
scope="encoder")
tmp_dim_1 = tf.tile(
tf.reshape(tf.range(batch_size), [batch_size, 1, 1, 1]),
[1, encoder_length, rec_length, 1])
tmp_dim_2 = tf.tile(
tf.reshape(tf.range(encoder_length),
[1, encoder_length, 1, 1]),
[batch_size, 1, rec_length, 1])
# [batch_size, length, rec_length, 3]
gather_idx = tf.concat(
[tmp_dim_1, tmp_dim_2,
tf.expand_dims(self.rec_lists, 3)], 3)
# [batch_size, length, num_items], [batch_size*length]
y_prob, local_loss, total_size = sampled_sequence_loss(
self.encoder_output, self.sessions_target, encoder_mask)
# Compute recommendation rank given rec_list
# [batch_size, length, num_items]
y_prob = tf.reshape(y_prob, [batch_size, encoder_length, num_items]) * \
tf.concat([tf.zeros([batch_size, encoder_length, 2], dtype=tf.float32),
tf.ones([batch_size, encoder_length, num_items-2], dtype=tf.float32)], 2)
# [batch_size, length, rec_len]
ini_prob = tf.reshape(tf.gather_nd(y_prob, gather_idx),
[batch_size, encoder_length, rec_length])
# [batch_size, length, rec_len]
mul_prob = ini_prob * self.rec_mask
# [batch_size, length, action_num]
_, self.index = tf.nn.top_k(mul_prob, k=action_num)
# [batch_size, length, metric_num]
_, self.metric_index = tf.nn.top_k(mul_prob,
k=(FLAGS['metric'].value + 1))
self.loss = tf.reduce_sum(
tf.reshape(self.reward, [-1]) * local_loss) / total_size
# Inference
with tf.variable_scope("agn", reuse=True):
# tf.get_variable_scope().reuse_variables()
self.lstm_state = tf.placeholder(tf.float32,
shape=(2, 2, None, num_units))
self.ini_state = (tf.contrib.rnn.LSTMStateTuple(
self.lstm_state[0, 0, :, :], self.lstm_state[0, 1, :, :]),
tf.contrib.rnn.LSTMStateTuple(
self.lstm_state[1, 0, :, :],
self.lstm_state[1, 1, :, :]))
# [batch_size, length, num_units]
self.encoder_output_predict, self.encoder_state_predict = dynamic_rnn(
cell,
self.encoder_input,
self.sessions_length,
initial_state=self.ini_state,
dtype=tf.float32,
scope="encoder")
# [batch_size, num_units]
self.final_output_predict = tf.reshape(
self.encoder_output_predict[:, -1, :], [-1, num_units])
# [batch_size, num_items]
self.rec_logits = output_fn(self.final_output_predict)
# [batch_size, action_num]
_, self.rec_index = tf.nn.top_k(
self.rec_logits[:, len(_START_VOCAB):], action_num)
self.rec_index += len(_START_VOCAB)
def gumbel_max(inp, alpha, beta):
# assert len(tf.shape(inp)) == 2
g = tf.random_uniform(tf.shape(inp), 0.0001, 0.9999)
g = -tf.log(-tf.log(g))
inp_g = tf.nn.softmax(
(tf.nn.log_softmax(inp / 1.0) + g * alpha) * beta)
return inp_g
# [batch_size, action_num]
_, self.random_rec_index = tf.nn.top_k(
gumbel_max(self.rec_logits[:, len(_START_VOCAB):], 1, 1),
action_num)
self.random_rec_index += len(_START_VOCAB)
# 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.trainable_variables()
gradients = tf.gradients(self.loss, self.params)
clipped_gradients, self.gradient_norm = tf.clip_by_global_norm(
gradients, max_gradient_norm)
self.update = tf.train.AdamOptimizer(
self.learning_rate).apply_gradients(zip(clipped_gradients,
self.params),
global_step=self.global_step)
self.saver = tf.train.Saver(tf.global_variables(),
write_version=tf.train.SaverDef.V2,
max_to_keep=100,
pad_step_number=True,
keep_checkpoint_every_n_hours=1.0)
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,
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)
