利用CNN进行多分类的文档分类

# coding: utf-8

import tensorflow as tf


class TCNNConfig(object):
    """CNN配置参数"""

    embedding_dim = 20  # 词向量维度
    seq_length = 100  # 序列长度
    num_classes = 73  # 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 = 0.8  # dropout保留比例
    learning_rate = 0.001  # 学习率

    batch_size = 128  # 每批训练大小
    num_epochs = 5  # 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))

#!/usr/bin/python
# -*- coding: utf-8 -*-

from __future__ import print_function

import os
import sys
import time
from datetime import timedelta

import numpy as np
import tensorflow as tf
from sklearn import metrics

from cnn_model import TCNNConfig, TextCNN
from data.cnews_loader import read_vocab, read_category, batch_iter, process_file, build_vocab

base_dir = 'data/'
train_dir = os.path.join(base_dir, 'train.txt')
test_dir = os.path.join(base_dir, 'test.txt')
val_dir = os.path.join(base_dir, 'test.txt')
vocab_dir = os.path.join(base_dir, 'bbb.txt')

save_dir = 'checkpoints/textcnn'
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__':


    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)


    # train()
    test()

# coding: utf-8

from __future__ import print_function

import os
import tensorflow as tf
import tensorflow.contrib.keras as kr
import time
from run_cnn import  get_time_dif
from cnn_model import TCNNConfig, TextCNN
from data.cnews_loader import read_category, read_vocab


base_dir = 'data/'
vocab_dir = os.path.join(base_dir, 'bbb.txt')

save_dir = 'checkpoints/textcnn'
save_path = os.path.join(save_dir, 'best_validation')  # 最佳验证结果保存路径


class CnnModel:
    def __init__(self):
        self.config = TCNNConfig()
        self.categories, self.cat_to_id = read_category()
        self.words, self.word_to_id = read_vocab(vocab_dir)
        self.config.vocab_size = len(self.words)
        self.model = TextCNN(self.config)

        self.session = tf.Session()
        self.session.run(tf.global_variables_initializer())
        saver = tf.train.Saver()
        saver.restore(sess=self.session, save_path=save_path)  # 读取保存的模型

    def predict(self, message):
        # 支持不论在python2还是python3下训练的模型都可以在2或者3的环境下运行
        content = message
        data = [self.word_to_id[x] for x in content if x in self.word_to_id]

        feed_dict = {
            self.model.input_x: kr.preprocessing.sequence.pad_sequences([data], self.config.seq_length),
            self.model.keep_prob: 1.0
        }

        y_pred_cls = self.session.run(self.model.y_pred_cls, feed_dict=feed_dict)
        return self.categories[y_pred_cls[0]]


if __name__ == '__main__':
    starttime = time.time()
    cnn_model = CnnModel()
    test_demo = [' 16-12-08 今年前11个月我国进出口总值21.83万亿元 ',
 '16-12-08 英国知识产权局局长一行访问黄埔海关（图',
 '16-12-08 厦门海关启动“互联网+自主报关”改革 ',
 '16-12-08 江门海关“宪法日” 普法到一线（图）',
 '16-12-08 27.5公斤“萌萌哒”果实种子闯关被截获（图）',
 '广州海关推动“主动披露” 体现执法“宽严相济”',
 '16-12-07 胡伟在湛江出席全国沿海沿边地区基层反走私综合治理现场会（图）',
 '16-12-07 锐意改革 高效服务 海关力助湛江书写蓝色经济梦想',
 '16-12-07 红其拉甫海关查获毒品海洛因4.8千克（图）']
    for i in test_demo:
        print(cnn_model.predict(i))
    print(get_time_dif(starttime))