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dssm_rnn.py
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dssm_rnn.py
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# coding=utf8
"""
python=3.5
TensorFlow=1.2.1
"""
import time
import numpy as np
import tensorflow as tf
import data_input
from config import Config
import random
random.seed(9102)
start = time.time()
# 是否加BN层
norm, epsilon = False, 0.001
# TRIGRAM_D = 21128
TRIGRAM_D = 100
# negative sample
NEG = 4
# query batch size
query_BS = 100
# batch size
BS = query_BS * NEG
# 读取数据
conf = Config()
data_train = data_input.get_data(conf.file_train)
data_vali = data_input.get_data(conf.file_vali)
# print(len(data_train['query']), query_BS, len(data_train['query']) / query_BS)
train_epoch_steps = int(len(data_train['query']) / query_BS) - 1
vali_epoch_steps = int(len(data_vali['query']) / query_BS) - 1
def variable_summaries(var, name):
"""Attach a lot of summaries to a Tensor."""
with tf.name_scope('summaries'):
mean = tf.reduce_mean(var)
tf.summary.scalar('mean/' + name, mean)
with tf.name_scope('stddev'):
stddev = tf.sqrt(tf.reduce_sum(tf.square(var - mean)))
tf.summary.scalar('sttdev/' + name, stddev)
tf.summary.scalar('max/' + name, tf.reduce_max(var))
tf.summary.scalar('min/' + name, tf.reduce_min(var))
tf.summary.histogram(name, var)
with tf.name_scope('input'):
# 预测时只用输入query即可，将其embedding为向量。
query_batch = tf.placeholder(tf.int32, shape=[None, None], name='query_batch')
doc_pos_batch = tf.placeholder(tf.int32, shape=[None, None], name='doc_positive_batch')
doc_neg_batch = tf.placeholder(tf.int32, shape=[None, None], name='doc_negative_batch')
query_seq_length = tf.placeholder(tf.int32, shape=[None], name='query_sequence_length')
pos_seq_length = tf.placeholder(tf.int32, shape=[None], name='pos_seq_length')
neg_seq_length = tf.placeholder(tf.int32, shape=[None], name='neg_sequence_length')
on_train = tf.placeholder(tf.bool)
drop_out_prob = tf.placeholder(tf.float32, name='drop_out_prob')
with tf.name_scope('word_embeddings_layer'):
# 这里可以加载预训练词向量
_word_embedding = tf.get_variable(name="word_embedding_arr", dtype=tf.float32,
shape=[conf.nwords, TRIGRAM_D])
query_embed = tf.nn.embedding_lookup(_word_embedding, query_batch, name='query_batch_embed')
doc_pos_embed = tf.nn.embedding_lookup(_word_embedding, doc_pos_batch, name='doc_positive_embed')
doc_neg_embed = tf.nn.embedding_lookup(_word_embedding, doc_neg_batch, name='doc_negative_embed')
with tf.name_scope('RNN'):
# Abandon bag of words, use GRU, you can use stacked gru
# query_l1 = add_layer(query_batch, TRIGRAM_D, L1_N, activation_function=None) # tf.nn.relu()
# doc_positive_l1 = add_layer(doc_positive_batch, TRIGRAM_D, L1_N, activation_function=None)
# doc_negative_l1 = add_layer(doc_negative_batch, TRIGRAM_D, L1_N, activation_function=None)
if conf.use_stack_rnn:
cell_fw = tf.contrib.rnn.GRUCell(conf.hidden_size_rnn, reuse=tf.AUTO_REUSE)
stacked_gru_fw = tf.contrib.rnn.MultiRNNCell([cell_fw], state_is_tuple=True)
cell_bw = tf.contrib.rnn.GRUCell(conf.hidden_size_rnn, reuse=tf.AUTO_REUSE)
stacked_gru_bw = tf.contrib.rnn.MultiRNNCell([cell_fw], state_is_tuple=True)
(output_fw, output_bw), (_, _) = tf.nn.bidirectional_dynamic_rnn(stacked_gru_fw, stacked_gru_bw)
# not ready, to be continue ...
else:
cell_fw = tf.contrib.rnn.GRUCell(conf.hidden_size_rnn, reuse=tf.AUTO_REUSE)
cell_bw = tf.contrib.rnn.GRUCell(conf.hidden_size_rnn, reuse=tf.AUTO_REUSE)
# query
(_, _), (query_output_fw, query_output_bw) = tf.nn.bidirectional_dynamic_rnn(cell_fw, cell_bw, query_embed,
sequence_length=query_seq_length,
dtype=tf.float32)
query_rnn_output = tf.concat([query_output_fw, query_output_bw], axis=-1)
query_rnn_output = tf.nn.dropout(query_rnn_output, drop_out_prob)
# doc_pos
(_, _), (doc_pos_output_fw, doc_pos_output_bw) = tf.nn.bidirectional_dynamic_rnn(cell_fw, cell_bw,
doc_pos_embed,
sequence_length=pos_seq_length,
dtype=tf.float32)
doc_pos_rnn_output = tf.concat([doc_pos_output_fw, doc_pos_output_bw], axis=-1)
doc_pos_rnn_output = tf.nn.dropout(doc_pos_rnn_output, drop_out_prob)
# doc_neg
(_, _), (doc_neg_output_fw, doc_neg_output_bw) = tf.nn.bidirectional_dynamic_rnn(cell_fw, cell_bw,
doc_neg_embed,
sequence_length=neg_seq_length,
dtype=tf.float32)
doc_neg_rnn_output = tf.concat([doc_neg_output_fw, doc_neg_output_bw], axis=-1)
doc_neg_rnn_output = tf.nn.dropout(doc_neg_rnn_output, drop_out_prob)
with tf.name_scope('Merge_Negative_Doc'):
# 合并负样本，tile可选择是否扩展负样本。
# doc_y = tf.tile(doc_positive_y, [1, 1])
doc_y = tf.tile(doc_pos_rnn_output, [1, 1])
for i in range(NEG):
for j in range(query_BS):
# slice(input_, begin, size)切片API
# doc_y = tf.concat([doc_y, tf.slice(doc_negative_y, [j * NEG + i, 0], [1, -1])], 0)
doc_y = tf.concat([doc_y, tf.slice(doc_neg_rnn_output, [j * NEG + i, 0], [1, -1])], 0)
with tf.name_scope('Cosine_Similarity'):
# Cosine similarity
# query_norm = sqrt(sum(each x^2))
query_norm = tf.tile(tf.sqrt(tf.reduce_sum(tf.square(query_rnn_output), 1, True)), [NEG + 1, 1])
# doc_norm = sqrt(sum(each x^2))
doc_norm = tf.sqrt(tf.reduce_sum(tf.square(doc_y), 1, True))
prod = tf.reduce_sum(tf.multiply(tf.tile(query_rnn_output, [NEG + 1, 1]), doc_y), 1, True)
norm_prod = tf.multiply(query_norm, doc_norm)
# cos_sim_raw = query * doc / (||query|| * ||doc||)
cos_sim_raw = tf.truediv(prod, norm_prod)
# gamma = 20
cos_sim = tf.transpose(tf.reshape(tf.transpose(cos_sim_raw), [NEG + 1, query_BS])) * 20
with tf.name_scope('Loss'):
# Train Loss
# 转化为softmax概率矩阵。
prob = tf.nn.softmax(cos_sim)
# 只取第一列，即正样本列概率。
hit_prob = tf.slice(prob, [0, 0], [-1, 1])
loss = -tf.reduce_sum(tf.log(hit_prob))
tf.summary.scalar('loss', loss)
with tf.name_scope('Training'):
# Optimizer
train_step = tf.train.AdamOptimizer(conf.learning_rate).minimize(loss)
# with tf.name_scope('Accuracy'):
# correct_prediction = tf.equal(tf.argmax(prob, 1), 0)
# accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
# tf.summary.scalar('accuracy', accuracy)
merged = tf.summary.merge_all()
with tf.name_scope('Test'):
average_loss = tf.placeholder(tf.float32)
loss_summary = tf.summary.scalar('average_loss', average_loss)
with tf.name_scope('Train'):
train_average_loss = tf.placeholder(tf.float32)
train_loss_summary = tf.summary.scalar('train_average_loss', train_average_loss)
def pull_batch(data_map, batch_id):
query_in = data_map['query'][batch_id * query_BS:(batch_id + 1) * query_BS]
query_len = data_map['query_len'][batch_id * query_BS:(batch_id + 1) * query_BS]
doc_positive_in = data_map['doc_pos'][batch_id * query_BS:(batch_id + 1) * query_BS]
doc_positive_len = data_map['doc_pos_len'][batch_id * query_BS:(batch_id + 1) * query_BS]
doc_negative_in = data_map['doc_neg'][batch_id * query_BS * NEG:(batch_id + 1) * query_BS * NEG]
doc_negative_len = data_map['doc_neg_len'][batch_id * query_BS * NEG:(batch_id + 1) * query_BS * NEG]
# query_in, doc_positive_in, doc_negative_in = pull_all(query_in, doc_positive_in, doc_negative_in)
return query_in, doc_positive_in, doc_negative_in, query_len, doc_positive_len, doc_negative_len
def feed_dict(on_training, data_set, batch_id, drop_prob):
query_in, doc_positive_in, doc_negative_in, query_seq_len, pos_seq_len, neg_seq_len = pull_batch(data_set,
batch_id)
query_len = len(query_in)
query_seq_len = [conf.max_seq_len] * query_len
pos_seq_len = [conf.max_seq_len] * query_len
neg_seq_len = [conf.max_seq_len] * query_len * NEG
return {query_batch: query_in, doc_pos_batch: doc_positive_in, doc_neg_batch: doc_negative_in,
on_train: on_training, drop_out_prob: drop_prob, query_seq_length: query_seq_len,
neg_seq_length: neg_seq_len, pos_seq_length: pos_seq_len}
# config = tf.ConfigProto() # log_device_placement=True)
# config.gpu_options.allow_growth = True
# if not config.gpu:
# config = tf.ConfigProto(device_count= {'GPU' : 0})
# 创建一个Saver对象，选择性保存变量或者模型。
saver = tf.train.Saver()
# with tf.Session(config=config) as sess:
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
train_writer = tf.summary.FileWriter(conf.summaries_dir + '/train', sess.graph)
start = time.time()
for epoch in range(conf.num_epoch):
batch_ids = [i for i in range(train_epoch_steps)]
random.shuffle(batch_ids)
for batch_id in batch_ids:
# print(batch_id)
sess.run(train_step, feed_dict=feed_dict(True, data_train, batch_id, 0.5))
end = time.time()
# train loss
epoch_loss = 0
for i in range(train_epoch_steps):
loss_v = sess.run(loss, feed_dict=feed_dict(False, data_train, i, 1))
epoch_loss += loss_v
epoch_loss /= (train_epoch_steps)
train_loss = sess.run(train_loss_summary, feed_dict={train_average_loss: epoch_loss})
train_writer.add_summary(train_loss, epoch + 1)
print("\nEpoch #%d | Train Loss: %-4.3f | PureTrainTime: %-3.3fs" %
(epoch, epoch_loss, end - start))
# test loss
start = time.time()
epoch_loss = 0
for i in range(vali_epoch_steps):
loss_v = sess.run(loss, feed_dict=feed_dict(False, data_vali, i, 1))
epoch_loss += loss_v
epoch_loss /= (vali_epoch_steps)
test_loss = sess.run(loss_summary, feed_dict={average_loss: epoch_loss})
train_writer.add_summary(test_loss, epoch + 1)
# test_writer.add_summary(test_loss, step + 1)
print("Epoch #%d | Test Loss: %-4.3f | Calc_LossTime: %-3.3fs" %
(epoch, epoch_loss, start - end))
# 保存模型
save_path = saver.save(sess, "model/model_1.ckpt")
print("Model saved in file: ", save_path)