舆情监控系统——step2.CNN-基于tensorFlow实现文本分类
中间经过了漫长的期末考试周，第二步拖了半个多月，终于把第二步做好了，使用了两种方法，现在我先主要介绍基于深度学习的方法GitHub代码点击此处。
数据集选择
一开始数据集大概每类300条，准确率只有86%左右，文本分类要求数据量足够，才能训练处合适的模型，我选择数据集的过程中经历了很多波折，最后使用的清华的THUCNews，我觉得是我能找到的最优的数据集了，关于数据集我专门写了一个博文，请点这里。
最后使用的数据格式如下，因为原始数据量太大了，只抽取了一部分。
抽取、整理数据
存放在text文件夹中，其中涉及两个模块：
- copyData.py: 用于从每个分类拷贝1400个文件。
- cnews_group.py：用于将多个文件整合到一个文件中。
从每个分类拷贝1400个文件
数据使用：
训练集: 1100*7
验证集: 100*7
测试集: 200*7
代码如下
import os
import glob
import shutil
import random
basePath = "/Users/alicelmx/Documents/实习/文本分类/基于深度学习/SogouData/ClassFile/"
newPath = "/Users/alicelmx/Documents/实习/文本分类/基于深度学习/text/"
listPath = list(map(lambda x:basePath+str(x)+"/",list(filter(lambda x:not str(x).startswith("."),os.listdir(basePath)))))
"""
训练集: 1100*7
验证集: 100*7
测试集: 200*7
"""
def copy(listPath,MAXCOUNT=1400):
for path in listPath:
newdir = newPath+ str(path).split("/")[-2]
print("====================")
print(newdir)
print("====================")
if not os.path.exists(newdir):
os.mkdir(newdir)
files=glob.glob(path+"*.txt")
if len(files) < MAXCOUNT:
resultlist = []
for i in range(MAXCOUNT):
resultlist.append(random.choice(files))
else:
resultlist = random.sample(files,MAXCOUNT)
for file in resultlist:
shutil.copy(file,newdir)
if __name__=='__main__':
copy(listPath)
print("抽取成功!")将多个文件整合到一个文件中
目标
创建sougou.train.txt（训练集1100*7）、sougou.test.txt（验证集100*7）、sougou.val.txt（测试集200*7），其中每一个文件包含每个类下的部分文件，存放于data文件夹中。
代码实现
#!/usr/bin/python
# -*- coding: utf-8 -*-
"""
将文本整合到 train、test、val 三个文件中
"""
import os
basePath = "/Users/alicelmx/Documents/实习/文本分类/基于深度学习/text/"
trainPath = "/Users/alicelmx/Documents/实习/文本分类/基于深度学习/data/"
def _read_file(filename):
"""读取一个文件并转换为一行"""
with open(filename, 'r', encoding='utf-8') as f:
return f.read().replace('\n', '').replace('\t', '').replace('\u3000', '')
def save_file(dirname):
"""
将多个文件整合并存到3个文件中
dirname: 原数据目录
文件内容格式: 类别\t内容
"""
f_train = open(trainPath+"sougou.train.txt",'w',encoding='utf-8')
f_test = open(trainPath + "sougou.test.txt", 'w', encoding='utf-8')
f_val = open(trainPath + "sougou.val.txt", 'w', encoding='utf-8')
for category in os.listdir(dirname):
catdir = os.path.join(dirname,category)
if not os.path.isdir(catdir):
continue
files = os.listdir(catdir)
print(len(files))
count = 0
for cur_file in files:
filename = os.path.join(catdir,cur_file)
content = _read_file(filename)
if count < 1100:
f_train.write(category+"\t"+content+"\n")
elif count < 1300:
f_test.write(category+"\t"+content+"\n")
else:
f_val.write(category + '\t' + content + '\n')
count += 1
print("===============")
print("finish:",category)
print("===============")
f_train.close()
f_test.close()
f_val.close()
if __name__=='__main__':
save_file(basePath)
print(len(open(trainPath+"sougou.train.txt", 'r', encoding='utf-8').readlines()))
print(len(open(trainPath + "sougou.test.txt", 'r', encoding='utf-8').readlines()))
print(len(open(trainPath + "sougou.val.txt", 'r', encoding='utf-8').readlines()))数据预处理
data/cnews_loader.py为数据的预处理文件。
- read_file(): 读取文件数据;
- build_vocab():构建词汇表，使用字符级的表示，这一函数会将词汇表存储下来，避免每一次重复处理;
- read_vocab():读取上一步存储的词汇表，转换为{词：id}表示; read_category(): 将分类目录固定，转换为{类别: id}表示;
- to_words(): 将一条由id表示的数据重新转换为文字;
完整代码
#!/usr/bin/python
# -*- coding: utf-8 -*-
from collections import Counter
import tensorflow.contrib.keras as kr
import numpy as np
import os
def open_file(filename, mode='r'):
"""
Commonly used file reader, change this to switch between python2 and python3.
mode: 'r' or 'w' for read or write
"""
return open(filename, mode, encoding='utf-8', errors='ignore')
def read_file(filename):
"""读取文件数据"""
contents, labels = [], []
with open_file(filename) as f:
for line in f:
try:
label, content = line.strip().split('\t')
contents.append(list(content))
labels.append(label)
except:
pass
return contents, labels
def build_vocab(train_dir, vocab_dir, vocab_size=5000):
"""根据训练集构建词汇表，存储"""
data_train, _ = read_file(train_dir)
all_data = []
for content in data_train:
all_data.extend(content)
counter = Counter(all_data)
count_pairs = counter.most_common(vocab_size - 1)
words, _ = list(zip(*count_pairs))
# 添加一个 来将所有文本pad为同一长度
words = ['' ] + list(words)
open_file(vocab_dir, mode='w').write('\n'.join(words) + '\n')
def read_vocab(vocab_dir):
"""读取词汇表"""
words = open_file(vocab_dir).read().strip().split('\n')
word_to_id = dict(zip(words, range(len(words))))
return words, word_to_id
def read_category():
"""读取分类目录，固定"""
categories = [ '财经','房产','股票','家居','科技','时政','娱乐' ]
cat_to_id = dict(zip(categories, range(len(categories))))
return categories, cat_to_id
def to_words(content, words):
"""将id表示的内容转换为文字"""
return ''.join(words[x] for x in content)
def process_file(filename, word_to_id, cat_to_id, max_length=600):
"""将文件转换为id表示"""
contents, labels = read_file(filename)
data_id, label_id = [], []
for i in range(len(contents)):
data_id.append([word_to_id[x] for x in contents[i] if x in word_to_id])
label_id.append(cat_to_id[labels[i]])
# 使用keras提供的pad_sequences来将文本pad为固定长度
x_pad = kr.preprocessing.sequence.pad_sequences(data_id, max_length)
y_pad = kr.utils.to_categorical(label_id) # 将标签转换为one-hot表示
return x_pad, y_pad
def batch_iter(x, y, batch_size=64):
"""生成批次数据"""
data_len = len(x)
num_batch = int((data_len - 1) / batch_size) + 1
indices = np.random.permutation(np.arange(data_len))
x_shuffle = x[indices]
y_shuffle = y[indices]
for i in range(num_batch):
start_id = i * batch_size
end_id = min((i + 1) * batch_size, data_len)
yield x_shuffle[start_id:end_id], y_shuffle[start_id:end_id]配置CNN卷积神经网络模型
见cnnModel.py文件
#!/usr/bin/python
# -*- coding: utf-8 -*-
import tensorflow as tf
class TCNNConfig(object):
"""CNN配置参数"""
embedding_dim = 64 # 词向量维度
seq_length = 600 # 序列长度
num_classes = 7 # 类别数
num_filters = 256 # 卷积核数目
kernel_size = 5 # 卷积核尺寸
vocab_size = 5000 # 词汇表达小
hidden_dim = 128 # 全连接层神经元
dropout_keep_prob = 0.5 # dropout保留比例
learning_rate = 1e-3 # 学习率
batch_size = 64 # 每批训练大小
num_epochs = 10 # 总迭代轮次
print_per_batch = 100 # 每多少轮输出一次结果
save_per_batch = 10 # 每多少轮存入tensorboard
class TextCNN(object):
"""文本分类，CNN模型"""
def __init__(self, config):
self.config = config
# 三个待输入的数据
self.input_x = tf.placeholder(tf.int32, [None, self.config.seq_length], name='input_x')
self.input_y = tf.placeholder(tf.float32, [None, self.config.num_classes], name='input_y')
self.keep_prob = tf.placeholder(tf.float32, name='keep_prob')
self.cnn()
def cnn(self):
"""CNN模型"""
# 词向量映射
with tf.device('/cpu:0'):
embedding = tf.get_variable('embedding', [self.config.vocab_size, self.config.embedding_dim])
embedding_inputs = tf.nn.embedding_lookup(embedding, self.input_x)
with tf.name_scope("cnn"):
# CNN layer
conv = tf.layers.conv1d(embedding_inputs, self.config.num_filters, self.config.kernel_size, name='conv')
# global max pooling layer
gmp = tf.reduce_max(conv, reduction_indices=[1], name='gmp')
with tf.name_scope("score"):
# 全连接层，后面接dropout以及relu激活
fc = tf.layers.dense(gmp, self.config.hidden_dim, name='fc1')
fc = tf.contrib.layers.dropout(fc, self.keep_prob)
fc = tf.nn.relu(fc)
# 分类器
self.logits = tf.layers.dense(fc, self.config.num_classes, name='fc2')
self.y_pred_cls = tf.argmax(tf.nn.softmax(self.logits), 1) # 预测类别
with tf.name_scope("optimize"):
# 损失函数，交叉熵
cross_entropy = tf.nn.softmax_cross_entropy_with_logits(logits=self.logits, labels=self.input_y)
self.loss = tf.reduce_mean(cross_entropy)
# 优化器
self.optim = tf.train.AdamOptimizer(learning_rate=self.config.learning_rate).minimize(self.loss)
with tf.name_scope("accuracy"):
# 准确率
correct_pred = tf.equal(tf.argmax(self.input_y, 1), self.y_pred_cls)
self.acc = tf.reduce_mean(tf.cast(correct_pred, tf.float32))
训练和验证
代码实现
见runCNN.py
若之前进行过训练，请把tensorboard/textcnn删除，避免TensorBoard多次训练结果重叠。
#!/usr/bin/python
# -*- coding: utf-8 -*-
from cnnModel import *
from data.sougouLoader1 import *
from sklearn import metrics
import sys
import time
from datetime import timedelta
base_dir = '/Users/alicelmx/Documents/实习/文本分类/基于深度学习/data/'
train_dir = os.path.join(base_dir, 'sougou.train.txt')
test_dir = os.path.join(base_dir, 'sougou.test.txt')
val_dir = os.path.join(base_dir, 'sougou.val.txt')
vocab_dir = os.path.join(base_dir, 'sougou.vocab.txt')
save_dir = '/Users/alicelmx/Documents/实习/文本分类/基于深度学习/测试结果/'
save_path = os.path.join(save_dir, 'best_validation') # 最佳验证结果保存路径
def get_time_dif(start_time):
"""获取已使用时间"""
end_time = time.time()
time_dif = end_time - start_time
return timedelta(seconds=int(round(time_dif)))
def feed_data(x_batch, y_batch, keep_prob):
feed_dict = {
model.input_x: x_batch,
model.input_y: y_batch,
model.keep_prob: keep_prob
}
return feed_dict
def evaluate(sess, x_, y_):
"""评估在某一数据上的准确率和损失"""
data_len = len(x_)
batch_eval = batch_iter(x_, y_, 128)
total_loss = 0.0
total_acc = 0.0
for x_batch, y_batch in batch_eval:
batch_len = len(x_batch)
feed_dict = feed_data(x_batch, y_batch, 1.0)
loss, acc = sess.run([model.loss, model.acc], feed_dict=feed_dict)
total_loss += loss * batch_len
total_acc += acc * batch_len
return total_loss / data_len, total_acc / data_len
def train():
print("Configuring TensorBoard and Saver...")
# 配置 Tensorboard，重新训练时，请将tensorboard文件夹删除，不然图会覆盖
tensorboard_dir = 'tensorboard/textcnn'
if not os.path.exists(tensorboard_dir):
os.makedirs(tensorboard_dir)
tf.summary.scalar("loss", model.loss)
tf.summary.scalar("accuracy", model.acc)
merged_summary = tf.summary.merge_all()
writer = tf.summary.FileWriter(tensorboard_dir)
# 配置 Saver
saver = tf.train.Saver()
if not os.path.exists(save_dir):
os.makedirs(save_dir)
print("Loading training and validation data...")
# 载入训练集与验证集
start_time = time.time()
x_train, y_train = process_file(train_dir, word_to_id, cat_to_id, config.seq_length)
x_val, y_val = process_file(val_dir, word_to_id, cat_to_id, config.seq_length)
time_dif = get_time_dif(start_time)
print("Time usage:", time_dif)
# 创建session
session = tf.Session()
session.run(tf.global_variables_initializer())
writer.add_graph(session.graph)
print('Training and evaluating...')
start_time = time.time()
total_batch = 0 # 总批次
best_acc_val = 0.0 # 最佳验证集准确率
last_improved = 0 # 记录上一次提升批次
require_improvement = 1000 # 如果超过1000轮未提升，提前结束训练
flag = False
for epoch in range(config.num_epochs):
print('Epoch:', epoch + 1)
batch_train = batch_iter(x_train, y_train, config.batch_size)
for x_batch, y_batch in batch_train:
feed_dict = feed_data(x_batch, y_batch, config.dropout_keep_prob)
if total_batch % config.save_per_batch == 0:
# 每多少轮次将训练结果写入tensorboard scalar
s = session.run(merged_summary, feed_dict=feed_dict)
writer.add_summary(s, total_batch)
if total_batch % config.print_per_batch == 0:
# 每多少轮次输出在训练集和验证集上的性能
feed_dict[model.keep_prob] = 1.0
loss_train, acc_train = session.run([model.loss, model.acc], feed_dict=feed_dict)
loss_val, acc_val = evaluate(session, x_val, y_val) # todo
if acc_val > best_acc_val:
# 保存最好结果
best_acc_val = acc_val
last_improved = total_batch
saver.save(sess=session, save_path=save_path)
improved_str = '*'
else:
improved_str = ''
time_dif = get_time_dif(start_time)
msg = 'Iter: {0:>6}, Train Loss: {1:>6.2}, Train Acc: {2:>7.2%},'\
+ ' Val Loss: {3:>6.2}, Val Acc: {4:>7.2%}, Time: {5} {6}'
print(msg.format(total_batch, loss_train, acc_train, loss_val, acc_val, time_dif, improved_str))
session.run(model.optim, feed_dict=feed_dict) # 运行优化
total_batch += 1
if total_batch - last_improved > require_improvement:
# 验证集正确率长期不提升，提前结束训练
print("No optimization for a long time, auto-stopping...")
flag = True
break # 跳出循环
if flag: # 同上
break
def test():
print("Loading test data...")
start_time = time.time()
x_test, y_test = process_file(test_dir, word_to_id, cat_to_id, config.seq_length)
session = tf.Session()
session.run(tf.global_variables_initializer())
saver = tf.train.Saver()
saver.restore(sess=session, save_path=save_path) # 读取保存的模型
print('Testing...')
loss_test, acc_test = evaluate(session, x_test, y_test)
msg = 'Test Loss: {0:>6.2}, Test Acc: {1:>7.2%}'
print(msg.format(loss_test, acc_test))
batch_size = 128
data_len = len(x_test)
num_batch = int((data_len - 1) / batch_size) + 1
y_test_cls = np.argmax(y_test, 1)
y_pred_cls = np.zeros(shape=len(x_test), dtype=np.int32) # 保存预测结果
for i in range(num_batch): # 逐批次处理
start_id = i * batch_size
end_id = min((i + 1) * batch_size, data_len)
feed_dict = {
model.input_x: x_test[start_id:end_id],
model.keep_prob: 1.0
}
y_pred_cls[start_id:end_id] = session.run(model.y_pred_cls, feed_dict=feed_dict)
# 评估
print("Precision, Recall and F1-Score...")
print(metrics.classification_report(y_test_cls, y_pred_cls, target_names=categories))
# 混淆矩阵
print("Confusion Matrix...")
cm = metrics.confusion_matrix(y_test_cls, y_pred_cls)
print(cm)
time_dif = get_time_dif(start_time)
print("Time usage:", time_dif)
if __name__ == '__main__':
if len(sys.argv) != 2 or sys.argv[1] not in ['train', 'test']:
raise ValueError("""usage: python run_cnn.py [train / test]""")
print('Configuring CNN model...')
config = TCNNConfig()
if not os.path.exists(vocab_dir): # 如果不存在词汇表，重建
build_vocab(train_dir, vocab_dir, config.vocab_size)
categories, cat_to_id = read_category()
words, word_to_id = read_vocab(vocab_dir)
config.vocab_size = len(words)
model = TextCNN(config)
if sys.argv[1] == 'train':
train()
else:
test()训练结果
python runCNN.py train
验证结果
python runCNN.py test
结果还行96.43%，至少达到我老板的需求了，可以交差了
总结
具体实现机制还很不明白，毕竟深度学习还没开始，打算还是先把机器学习搞搞在弄深度学习吧，都是大块头，在这先挖坑：
- python对文件读写
- 深度学习（CNN）
- tensorFlow框架
- 学完在复习下这段代码