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train_cnn_text_classification.py
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train_cnn_text_classification.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
author: Linjian Zhang
"""
import tensorflow as tf
import tensorflow.contrib.slim as slim
import os
import numpy as np
import shutil
from datetime import datetime
import re
from tensorflow.contrib import learn
dir0 = '20170720' # change it every time when training
lr_base = 1e-3 # 初始学习率
epoch_max = 200 # 最大epoch次数
epoch_save = 20 # 每#epoch保存一次模型
max_to_keep = 3 # 最多保存模型数目
batch_size = 64 # batch size
embedding_size = 128 # 词向量维度
########################################
data_file1 = 'data/rt-polaritydata/rt-polarity.pos'
data_file2 = 'data/rt-polaritydata/rt-polarity.neg'
# dir_restore = 'model/cnn_vo/20170705_1/model-30200'
net_name = 'cnn/'
dir_models = 'model/' + net_name
dir_logs = 'log/' + net_name
dir_model = dir_models + dir0
dir_log_train = dir_logs + dir0 + '_train'
dir_log_val = dir_logs + dir0 + '_val'
if not os.path.exists(dir_models):
os.mkdir(dir_models)
if not os.path.exists(dir_logs):
os.mkdir(dir_logs)
if os.path.exists(dir_model):
shutil.rmtree(dir_model)
if os.path.exists(dir_log_train):
shutil.rmtree(dir_log_train)
if os.path.exists(dir_log_val):
shutil.rmtree(dir_log_val)
os.mkdir(dir_model)
os.mkdir(dir_log_train)
os.mkdir(dir_log_val)
# ########################################
def clean_str(string):
"""
Tokenization/string cleaning for all datasets except for SST.
Original taken from https://github.com/yoonkim/CNN_sentence/blob/master/process_data.py
"""
string = re.sub(r"[^A-Za-z0-9(),!?\'\`]", " ", string)
string = re.sub(r"\'s", " \'s", string)
string = re.sub(r"\'ve", " \'ve", string)
string = re.sub(r"n\'t", " n\'t", string)
string = re.sub(r"\'re", " \'re", string)
string = re.sub(r"\'d", " \'d", string)
string = re.sub(r"\'ll", " \'ll", string)
string = re.sub(r",", " , ", string)
string = re.sub(r"!", " ! ", string)
string = re.sub(r"\(", " \( ", string)
string = re.sub(r"\)", " \) ", string)
string = re.sub(r"\?", " \? ", string)
string = re.sub(r"\s{2,}", " ", string)
return string.strip().lower()
def load_data(file1, file2):
# Load data from files
data1 = list(open(file1, "rb").readlines())
data1 = [s.strip() for s in data1]
number_pos = len(data1)
data2 = list(open(file2, "rb").readlines())
data2 = [s.strip() for s in data2]
# Split by words
data_total = data1 + data2
data_total = [clean_str(str(sent)) for sent in data_total]
# Generate labels
label1 = [[0, 1] for _ in data1]
label2 = [[1, 0] for _ in data2]
# Generate vocabulary
sequence_length = max([len(x.split(" ")) for x in data_total])
vocab_processor = learn.preprocessing.VocabularyProcessor(sequence_length)
data = np.array(list(vocab_processor.fit_transform(data_total)))
# 将text转化为index，并padding成相同长度，这里发现了一个小bug（同一个单词会有不同的index？）
# vocab_processor.save(os.path.join(dir_model, 'vocab')) # 保存vocabulary
data_pos = data[0:number_pos]
data_neg = data[number_pos:]
number_train_pos = int(0.9 * number_pos)
data_t = np.concatenate([data_pos[:number_train_pos], data_neg[:number_train_pos]], 0)
label_t = np.concatenate([label1[:number_train_pos], label2[:number_train_pos]], 0)
data_v = np.concatenate([data_pos[number_train_pos:], data_neg[number_train_pos:]], 0)
label_v = np.concatenate([label1[number_train_pos:], label2[number_train_pos:]], 0)
return data_t, label_t, data_v, label_v, vocab_processor
class Data(object):
def __init__(self, data, label, bs=batch_size, shuffle=True):
self.data = data
self.label = label
self.bs = bs
self.shuffle = shuffle
self.index = 0 # point at total_index
self.number = len(label)
self.total_index = range(self.number)
if self.shuffle:
self.total_index = np.random.permutation(self.total_index)
def next_batch(self):
start = self.index
self.index += self.bs
if self.index > self.number:
if self.shuffle:
self.total_index = np.random.permutation(self.total_index)
self.index = 0
start = self.index
self.index += self.bs
end = self.index
return self.data[self.total_index[start:end]], self.label[self.total_index[start:end]]
class Net(object):
def __init__(self, sequence_length, num_class, vocabulary_size):
self.x1 = tf.placeholder(tf.int32, [None, sequence_length], name='x1') # sentence
self.x2 = tf.placeholder(tf.int32, [None, num_class], name='x2') # label
self.x3 = tf.placeholder(tf.float32, [], name='x3') # lr
self.x4 = tf.placeholder(tf.float32, [], name='x4') # dropout
with tf.variable_scope('embedding'):
self.w_embed = tf.Variable(tf.random_uniform([vocabulary_size, embedding_size], -1.0, 1.0), name='w_embed')
self.embed = tf.nn.embedding_lookup(self.w_embed, self.x1) # [bs, 57, 128]
self.embed_expanded = tf.expand_dims(self.embed, -1) # [bs, 57, 128, 1]
with tf.variable_scope('conv'):
conv1_1 = slim.conv2d(self.embed_expanded, 128, [3, embedding_size], 1, padding='valid', scope='conv1_1') # [bs, 55, 1, 128]
pool1_1 = slim.max_pool2d(conv1_1, [sequence_length - 3 + 1, 1], 1, scope='pool1_1') # [bs, 1, 1, 128]
conv1_2 = slim.conv2d(self.embed_expanded, 128, [4, embedding_size], 1, padding='valid', scope='conv1_2')
pool1_2 = slim.max_pool2d(conv1_2, [sequence_length - 4 + 1, 1], 1, scope='pool1_2')
conv1_3 = slim.conv2d(self.embed_expanded, 128, [5, embedding_size], 1, padding='valid', scope='conv1_3')
pool1_3 = slim.max_pool2d(conv1_3, [sequence_length - 5 + 1, 1], 1, scope='pool1_3')
pool1 = tf.concat([pool1_1, pool1_2, pool1_3], axis=3, name='pool1') # [bs, 1, 1, 384]
pool1_flat = tf.reshape(pool1, [-1, 384], name='pool1_flat')
dropout1 = slim.dropout(pool1_flat, self.x4, scope='dropout1')
with tf.variable_scope('softmax'):
self.fc2 = slim.fully_connected(dropout1, num_class, activation_fn=None, scope='fc2')
# loss & accuracy
losses = tf.nn.softmax_cross_entropy_with_logits(logits=self.fc2, labels=self.x2, name='loss')
self.loss = tf.reduce_mean(losses) # 不能少！取均值
optimizer = tf.train.AdamOptimizer(self.x3)
self.train_op = slim.learning.create_train_op(self.loss, optimizer)
self.prediction = tf.argmax(self.fc2, 1, name='prediction')
correct_prediction = tf.equal(self.prediction, tf.argmax(self.x2, 1))
self.accuracy = tf.reduce_mean(tf.cast(correct_prediction, 'float'), name='accuracy')
# tensor board
loss_summary = tf.summary.scalar('loss', self.loss)
accuracy_summary = tf.summary.scalar('accuracy', self.accuracy)
self.summary_merge = tf.summary.merge([loss_summary, accuracy_summary])
# init & save configuration
self.init = tf.initialize_all_variables()
self.saver = tf.train.Saver(max_to_keep=max_to_keep)
# self.t_vars = tf.trainable_variables()
# self.variables_names = [v.name for v in self.t_vars] # turn on if you want to check the variables
# gpu configuration
self.tf_config = tf.ConfigProto()
# self.tf_config.gpu_options.allow_growth = True
# if use_gpu_1:
# self.tf_config.gpu_options.visible_device_list = '1'
def main(_):
# 1. 载入数据。将数据处理成网络需要的格式
data_t, label_t, data_v, label_v, vocab_processor = load_data(data_file1, data_file2)
# 2. 数据初始化及产生mini-batch数据
model_data_t = Data(data_t, label_t)
# 3. 定义graph
model = Net(sequence_length=data_t.shape[1],
num_class=label_t.shape[1],
vocabulary_size=len(vocab_processor.vocabulary_))
with tf.Session(config=model.tf_config) as sess:
writer_train = tf.summary.FileWriter(dir_log_train, sess.graph)
writer_val = tf.summary.FileWriter(dir_log_val, sess.graph)
sess.run(model.init)
for epoch in range(epoch_max):
lr = lr_base
iter_per_epoch = len(label_t) // batch_size
for iteration in range(iter_per_epoch):
global_iter = epoch * iter_per_epoch + iteration
x1_t, x2_t = model_data_t.next_batch()
feed_dict_t = dict()
feed_dict_t[model.x1] = x1_t
feed_dict_t[model.x2] = x2_t
feed_dict_t[model.x3] = lr
feed_dict_t[model.x4] = 0.5
sess.run(model.train_op, feed_dict_t)
# display
if not (iteration + 1) % 10:
summary_out_t, loss_out_t, acc_out_t = sess.run([model.summary_merge, model.loss, model.accuracy], feed_dict_t)
writer_train.add_summary(summary_out_t, global_iter + 1)
print('%s, epoch %03d/%03d, iter %04d/%04d, lr %.5f, loss: %.5f, accuracy: %.5f' %
(datetime.now(), epoch + 1, epoch_max, iteration + 1, iter_per_epoch, lr, loss_out_t, acc_out_t))
if not (iteration + 1) % 100:
feed_dict_v = dict()
feed_dict_v[model.x1] = data_v
feed_dict_v[model.x2] = label_v
feed_dict_v[model.x4] = 1.0
summary_out_v, loss_out_v, acc_out_v = sess.run([model.summary_merge, model.loss, model.accuracy], feed_dict_v)
writer_val.add_summary(summary_out_v, global_iter + 1)
print('****val loss: %.5f, accuracy: %.5f****' % (loss_out_v, acc_out_v))
# save
if not (global_iter + 1) % (epoch_save * iter_per_epoch):
model.saver.save(sess, (dir_model + '/model'), global_step=global_iter + 1)
if __name__ == '__main__':
tf.app.run()