使用LSTM进行文本分类

说明

之前写过用lstm模型做的文本分类，但是代码结构非常混乱。读过Bert源码后，决定模仿Bert的结构，自己重新写一遍使用lstm模型的代码。只作为熟悉tensorflow各个api与一个比较清楚的NLP模型结构的练手用，不求更高的准确率。

使用包含10个商品类别，60000+数据的，已标注正负情感的商品评论数据作为训练语料。原文件为csv格式，包含3个字段：cat（类别）、label（正负情感）、review（实际的评论文字）。其既可作为正负情感分类的二分类训练语料，也可以（勉强）作为商品类别分类的10分类训练语料。

数据取自https://github.com/SophonPlus/ChineseNlpCorpus，非常感谢

已按大约8：2的比例得到了训练集与测试集并将其转为了tsv文件。

使用结巴分词作为切词工具。

一、得到词典txt文件

由于我一直没有找到合适的中文词典文件，Bert中的词典文件又是以字作为最小单位的，故这里暂时只是对待训练的语料做切词、去重的处理，得到一个比较小的字典文件。

def create_vocab():

    raw_df = pd.read_csv(RAW_DATA)                          # 读原始文件为dataframe
    # 热水器有一条数据有问题，不要热水器的数据
    raw_df = raw_df[raw_df.cat != '热水器']

    raw_document = raw_df['review'].tolist()                # 原始语料（list形式）

    # 加载停用词列表
    # with open(STOPWORDS, 'r', encoding='utf-8') as s:
    #     stopwords = [word.strip() for word in s.readlines()]

    document_words = []                                     # 原始语料完成切词
    for sentence in raw_document:
        cut_sentence = [word for word in jieba.lcut(sentence)]
        document_words.extend(cut_sentence)
    vocab_list = set(document_words)

    with open(VOCAB, 'w', encoding='utf-8') as f:
        f.write('[PAD]' + '\n')
        f.write('[UNK]' + '\n')
        for vocab in vocab_list:
            f.write(vocab + '\n')
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二、Tokenization

这一块完全基于Bert源码，做了非常多的精简。只满足：to_unicode、读取词典、切词、词语转id、id转词语的基本功能。没什么好说的。

import collections
import tensorflow as tf
import jieba


def convert_to_unicode(text):
    """Converts `text` to Unicode (if it's not already), assuming utf-8 input."""
    if isinstance(text, str):
        return text
    elif isinstance(text, bytes):
        return text.decode("utf-8", "ignore")
    else:
        raise ValueError("Unsupported string type: %s" % (type(text)))


# 将词典中的词构成(词，index)的collections.OrderedDict形式
def load_vocab(vocab_file):
    """Loads a vocabulary file into a dictionary."""
    vocab = collections.OrderedDict()
    index = 0
    with tf.gfile.GFile(vocab_file, "r") as reader:
        while True:
            token = convert_to_unicode(reader.readline())
            if not token:
                break
            token = token.strip()
            vocab[token] = index
            index += 1
    return vocab


def convert_by_vocab(vocab, items):
    """Converts a sequence of [tokens|ids] using the vocab."""
    output = []
    for item in items:
        output.append(vocab.get(item, vocab['[UNK]']))
    return output


class FullTokenizer(object):
    """Runs end-to-end tokenziation."""

    def __init__(self, vocab_file):
        # 根据vocab文件，得到形如(词，index)的字典
        self.vocab = load_vocab(vocab_file)
        # 变成 index: 词 的形式
        self.inv_vocab = {v: k for k, v in self.vocab.items()}

    # 将句子变成词列表
    @staticmethod
    def tokenize(text):
        split_tokens = jieba.lcut(text)
        return split_tokens

    def convert_tokens_to_ids(self, tokens):
        return convert_by_vocab(self.vocab, tokens)

    def convert_ids_to_tokens(self, ids):
        return convert_by_vocab(self.inv_vocab, ids)
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三、建立模型结构

模型的mode参数可取train、eval、predict三类，取eval时只返回cost与accuracy，取predict时只返回logits。别的不用多说了。

import tensorflow as tf
import json
import six


class LstmConfig(object):

    def __init__(self,
                 vocab_size,                # 词典中的词数
                 hidden_size=128,
                 keep_prob=0.9,
                 embedding_keep_prob=0.9,   # 词向量不被dropout的比例
                 max_grad_norm=5,
                 num_of_classes=2,          # 分类数
                 num_of_layers=2,           # lstm网络层数
                 initializer_range=0.02):   # 初始化范围
        self.vocab_size = vocab_size
        self.hidden_size = hidden_size
        self.keep_prob = keep_prob
        self.embedding_keep_prob = embedding_keep_prob
        self.max_grad_norm = max_grad_norm
        self.num_of_classes = num_of_classes
        self.num_of_layers = num_of_layers
        self.initializer_range = initializer_range

    @classmethod
    def from_dict(cls, json_object):
        """Constructs a `BertConfig` from a Python dictionary of parameters."""
        config = LstmConfig(vocab_size=None)
        for (key, value) in six.iteritems(json_object):
            config.__dict__[key] = value
        return config

    @classmethod
    def from_json_file(cls, json_file):
        """Constructs a `BertConfig` from a json file of parameters."""
        with tf.gfile.GFile(json_file, "r") as reader:
            text = reader.read()
        return cls.from_dict(json.loads(text))


# 双向LSTM网络模型
class LstmModel(object):

    # 构建网格结构
    def __init__(self, config, mode):
        self.config = config
        self.embedding_keep_prob = config.embedding_keep_prob
        self.mode = mode
        output_keep_prob = config.keep_prob if mode == 'train' else 1.0

        # 词向量
        self.word_embedding = tf.get_variable('word_emb', shape=[config.vocab_size, config.hidden_size])

        # lstm网络结构
        # 前向网络变量
        lstm_cells_fw = [tf.nn.rnn_cell.DropoutWrapper(tf.nn.rnn_cell.BasicLSTMCell(config.hidden_size),
                                                       output_keep_prob=output_keep_prob)
                         for _ in range(config.num_of_layers)]
        self.lstm_fw = tf.nn.rnn_cell.MultiRNNCell(lstm_cells_fw)
        # 反向网络
        lstm_cells_bw = [tf.nn.rnn_cell.DropoutWrapper(tf.nn.rnn_cell.BasicLSTMCell(config.hidden_size),
                                                       output_keep_prob=output_keep_prob)
                         for _ in range(config.num_of_layers)]
        self.lstm_bw = tf.nn.rnn_cell.MultiRNNCell(lstm_cells_bw)

        # Softmax层变量
        self.weight = tf.get_variable('weight', [config.hidden_size * 2, config.num_of_classes])
        self.bias = tf.get_variable('bias', [config.num_of_classes])

    # 定义模型的前向计算图
    def forward(self, src_input, src_size, label):

        # 将输入的序号化单词转成词向量
        inputs = tf.nn.embedding_lookup(self.word_embedding, src_input)
        if self.mode == 'train':
            inputs = tf.nn.dropout(inputs, self.embedding_keep_prob)

        # LSTM网络计算
        with tf.variable_scope('lstm'):
            outputs, states = tf.nn.bidirectional_dynamic_rnn(self.lstm_fw,
                                                              self.lstm_bw,
                                                              inputs,
                                                              dtype=tf.float32,
                                                              sequence_length=src_size)
            final_outputs = tf.concat(outputs, 2)
            final_outputs = final_outputs[:, -1, :]
            # 取平均值
            # final_outputs = tf.reduce_mean(tf.concat(outputs, 2), 1)

        # 全连接层计算
        with tf.variable_scope('fc'):
            logits = tf.matmul(final_outputs, self.weight) + self.bias

        if self.mode == 'predict':
            return logits

        # 损失函数
        with tf.variable_scope('loss'):
            loss = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=label,
                                                                  logits=logits)
            cost = tf.reduce_mean(loss)

        # 准确率
        with tf.variable_scope('accuracy'):
            correct_prediction = tf.equal(tf.argmax(logits, 1), label)
            accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

        if self.mode == 'eval':
            return cost, accuracy

        # 定义反向操作
        trainable_variables = tf.trainable_variables()

        # 控制梯度大小，定义优化方法和训练步骤
        grads = tf.gradients(cost, trainable_variables)
        grads, _ = tf.clip_by_global_norm(grads, self.config.max_grad_norm)
        optimizer = tf.train.GradientDescentOptimizer(learning_rate=self.config.learning_rate)

        train_op = optimizer.apply_gradients(zip(grads, trainable_variables))
        return logits, cost, accuracy, train_op
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四、分类主程序

我命名为run_classifier，完全照搬Bert。

首先是tf.flag，用于导入参数

flags = tf.flags
FLAGS = flags.FLAGS

flags.DEFINE_integer("train_batch_size", 32, "Total batch size for training.")
flags.DEFINE_integer("eval_batch_size", 8, "Total batch size for eval.")
flags.DEFINE_integer("predict_batch_size", 8, "Total batch size for predict.")
flags.DEFINE_integer("num_train_epochs", 4, "Total epoches for train.")
flags.DEFINE_string(
    "data_dir", "E:/NLP/NLP_Deep_Learning_Summary/datasets",
    "The input data dir. Should contain the .tsv files (or other data files) for the task.")
flags.DEFINE_string("init_checkpoint", None, "Initial checkpoint")
flags.DEFINE_string("vocab_file", "./vocab.txt", "The vocabulary file.")
flags.DEFINE_string("output_file", "./model1", "The output file for trained model.")

flags.DEFINE_bool("do_train", True, "Whether to run training.")
flags.DEFINE_bool("do_eval", True, "Whether to run eval on the dev set.")
flags.DEFINE_bool("do_predict", False, "Whether to run the model in inference mode on the test set.")
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定义Example、Feature与DataProcessor类

为了从训练/测试数据中获得tfrecord文件，需要做Example与Feature类的处理。DataProcessor与Bert源码几乎相同，功能为从tsv文件中得到example类的数据。

tsv文件的各个字段为：index（id）、category（商品类别）、polarity（情感，0或1）、text（原始文字）。

class InputExample(object):
    """A single training/test example for simple sequence classification."""

    def __init__(self, guid, text, label=None):
        """Constructs a InputExample."""
        self.guid = guid
        self.text = text
        self.label = label
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由于使用的是lstm，其特征包含：每条语料原始的词id、原始的长度（即词数）与原始的分类标签。

class InputFeatures(object):
    def __init__(self, input_ids, input_size, label):
        self.input_ids = input_ids
        self.input_size = input_size
        self.label = label
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class DataProcessor(object):

    def get_train_examples(self, data_dir):
        lines = self._read_tsv(os.path.join(data_dir, "online_shopping_train.tsv"))
        return self._create_examples(lines, 'train')

    def get_dev_examples(self, data_dir):
        lines = self._read_tsv(os.path.join(data_dir, "online_shopping_dev.tsv"))
        return self._create_examples(lines, 'dev')

    def get_test_examples(self, data_dir):
        lines = self._read_tsv(os.path.join(data_dir, "online_shopping_test.tsv"))
        return self._create_examples(lines, 'test')

    @staticmethod
    def get_labels():
        return ["0", "1"]
        # return ['蒙牛', '水果', '洗发水', '平板', '酒店', '手机', '计算机', '书籍', '衣服', '热水器']

    @staticmethod
    def _create_examples(lines, set_type):
        examples = []
        for (i, line) in enumerate(lines):
            if i == 0:
                continue
            guid = "%s-%s" % (set_type, i)
            text = tokenization.convert_to_unicode(line[3])
            label = tokenization.convert_to_unicode(line[2])
            examples.append(
                InputExample(guid=guid, text=text, label=label))
        return examples

    @classmethod
    def _read_tsv(cls, input_file, quotechar=None):
        """Reads a tab separated value file."""
        with tf.gfile.Open(input_file, "r") as f:
            reader = csv.reader(f, delimiter="\t", quotechar=quotechar)
            lines = []
            for line in reader:
                lines.append(line)
            return lines
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得到tfrecord文件

也是模仿Bert的写法写的（甚至保留了打印前五条example）。

# 将一个example类的训练数据转成feature类
def convert_single_example(ex_index, example, tokenizer):
    text = example.text
    tokens = tokenizer.tokenize(text)
    input_ids = tokenizer.convert_tokens_to_ids(tokens)
    input_size = len(input_ids)
    label = int(example.label)
    # 打印前5条转换的记录
    if ex_index < 5:
        tf.logging.info("*** Example ***")
        tf.logging.info("guid: %s" % example.guid)
        tf.logging.info("input_ids: %s" % " ".join([str(x) for x in input_ids]))
        tf.logging.info("input_size: %s" % input_size)
        tf.logging.info("label: %s" % label)
    feature = InputFeatures(input_ids=input_ids, input_size=input_size, label=label)
    return feature


# 将准备喂入模型的数据存成tfrecord文件
def file_based_convert_examples_to_features(examples, tokenizer, output_file):
    writer = tf.python_io.TFRecordWriter(output_file)
    for (ex_index, example) in enumerate(examples):
        if ex_index % 10000 == 0:
            tf.logging.info("Writing example %d of %d" % (ex_index, len(examples)))
        feature = convert_single_example(ex_index, example, tokenizer)

        def create_int_feature(values):
            f = tf.train.Feature(int64_list=tf.train.Int64List(value=list(values)))
            return f

        features = collections.OrderedDict()
        features['input_ids'] = create_int_feature(feature.input_ids)
        features['input_size'] = create_int_feature([feature.input_size])
        features['label'] = create_int_feature([feature.label])

        tf_example = tf.train.Example(features=tf.train.Features(feature=features))
        writer.write(tf_example.SerializeToString())
    writer.close()
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读取并解析tfrecord文件

终于又有相对原创的代码了。这里在做batch的同时对input_ids做了padding的处理。效果即input_fn_builder，只是改了名字，调整了结构。

def file_based_dataset_loader(input_file, is_training, batch_size, num_epochs):
    # TfRecord的解析函数
    def parse_func(serialized_example):
        name_to_features = {
            "input_ids": tf.VarLenFeature(tf.int64),
            "input_size": tf.FixedLenFeature(shape=(1,), dtype=tf.int64),
            "label": tf.FixedLenFeature(shape=(1,), dtype=tf.int64),
        }
        parsed_example = tf.parse_single_example(serialized_example, features=name_to_features)
        parsed_example['input_ids'] = tf.sparse_tensor_to_dense(parsed_example['input_ids'])

        input_ids = parsed_example['input_ids']
        input_size = parsed_example['input_size']
        label = parsed_example['label']

        return input_ids, input_size, label

    dataset = tf.data.TFRecordDataset(input_file)
    dataset = dataset.map(parse_func)
    if is_training:
        dataset = dataset.repeat(num_epochs).shuffle(buffer_size=100)

    padded_shapes = (tf.TensorShape([None]),  # 语料数据，None即代表batch_size
                     tf.TensorShape([None]),  # 语料数据各个句子的原始长度
                     tf.TensorShape([None]))  # 标签数据，None即代表batch_size

    # 调用padded_batch方法进行batching操作
    batched_dataset = dataset.padded_batch(batch_size, padded_shapes)
    # dataset = dataset.batch(batch_size)
    return batched_dataset
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正式运行

模型运行的函数，单独拿出来。

def run_epoch(session, cost_op, accuracy_op, train_op, step):
    while True:
        try:
            cost, accuracy, _ = session.run([cost_op, accuracy_op, train_op])
            if step % 100 == 0:
                tf.logging.info('Steps: {0}, Loss value: {1}，Accuracy: {2}'.format(
                    step, cost, accuracy))
            step += 1
        except tf.errors.OutOfRangeError:
            break
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主程序。在config配置里直接写明了词典文件的词数。
另外，不管是在构建词典文件，处理待预测的句子，都没有做去停用词的步骤。

def main():

    config = modelling.LstmConfig(vocab_size=68355)
    tokenizer = tokenization.FullTokenizer(vocab_file=FLAGS.vocab_file)

    processor = DataProcessor()

    if FLAGS.do_train:

        # 定义训练用循环神经网络模型
        train_model = modelling.LstmModel(config, mode='train')

        train_examples = processor.get_train_examples(FLAGS.data_dir)

        num_train_steps = int(len(train_examples) / FLAGS.train_batch_size * FLAGS.num_train_epochs)

        train_file = r'./tmp/train.tfrecord'
        file_based_convert_examples_to_features(train_examples, tokenizer, train_file)

        # 获得训练数据
        train_data = file_based_dataset_loader(train_file,
                                               is_training=True,
                                               batch_size=FLAGS.train_batch_size,
                                               num_epochs=FLAGS.num_train_epochs)
        train_iterator = train_data.make_initializable_iterator()
        input_ids, input_sizes, labels = train_iterator.get_next()
        input_sizes = tf.reshape(input_sizes, shape=(-1,))
        labels = tf.reshape(labels, shape=(-1,))

        # 定义前向计算图，输入数据以张量的形式提供给forward函数
        _, cost_op, accuracy_op, train_op = train_model.forward(input_ids, input_sizes, labels)
        #
        # TensorFlow持久化类
        saver = tf.train.Saver()
        step = 0

        # 训练模型
        tf.logging.info("***** Running training *****")
        tf.logging.info("  Num examples = %d", len(train_examples))
        tf.logging.info("  Total training steps: {}".format(num_train_steps))
        with tf.Session() as sess:
            tf.global_variables_initializer().run()
            sess.run(train_iterator.initializer)
            run_epoch(sess, cost_op, accuracy_op, train_op, step)
            tf.logging.info("********* Training Step Finished *********")

            saver.save(sess, r'./model/lstm.ckpt')

    if FLAGS.do_eval:
        tf.reset_default_graph()
        eval_model = modelling.LstmModel(config, mode='eval')
        eval_examples = processor.get_dev_examples(FLAGS.data_dir)
        eval_file = r'./tmp/eval.tfrecord'
        file_based_convert_examples_to_features(eval_examples, tokenizer, eval_file)

        # 获得测试数据
        eval_data = file_based_dataset_loader(eval_file,
                                              is_training=False,
                                              batch_size=FLAGS.eval_batch_size,
                                              num_epochs=None)
        eval_iterator = eval_data.make_initializable_iterator()
        eval_input_ids, eval_input_sizes, eval_labels = eval_iterator.get_next()
        eval_input_sizes = tf.reshape(eval_input_sizes, shape=(-1,))
        eval_labels = tf.reshape(eval_labels, shape=(-1,))

        eval_cost_op, eval_accuracy_op = eval_model.forward(eval_input_ids, eval_input_sizes, eval_labels)

        # 测试模型
        eval_step = 0
        tf.logging.info("***** Running Eval *****")
        tf.logging.info("  Num examples = %d", len(eval_examples))
        with tf.Session() as sess_eval:
            tf.global_variables_initializer().run()
            sess_eval.run(eval_iterator.initializer)

            saver = tf.train.Saver()
            saver.restore(sess_eval, r'./model/lstm.ckpt')
            tf.logging.info("*** Restore finished ***")

            eval_cost = 0
            eval_correct = 0
            while True:
                try:
                    cost, accuracy = sess_eval.run([eval_cost_op, eval_accuracy_op])
                    eval_cost += cost / FLAGS.eval_batch_size
                    eval_correct += accuracy * FLAGS.eval_batch_size
                    eval_step += 1
                except tf.errors.OutOfRangeError:
                    break
            eval_cost = eval_cost / (len(eval_examples) / FLAGS.eval_batch_size)
            eval_accuracy = eval_correct / len(eval_examples)
        print('Eval Cost: {0}, Eval Accuracy: {1}'.format(eval_cost, eval_accuracy))
        with open(r'./model/eval_result.txt', 'w', encoding='utf-8') as f:
            f.write('Eval Cost: {0}, Eval Accuracy: {1}'.format(eval_cost, eval_accuracy))


if __name__ == '__main__':
    main()
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调用模型完成预测

重新写了convert_single_example方法。
由于只是少量单句的预测，因此没有做生成tfrecord并解析的处理，而是直接做了padding的处理。

import tensorflow as tf
import tokenization
import modelling
from run_classifier import InputExample, InputFeatures

# 将一个example类的训练数据转成feature类
def convert_single_example(example, tokenizer):
    text = example.text
    tokens = tokenizer.tokenize(text)
    input_ids = tokenizer.convert_tokens_to_ids(tokens)
    input_size = len(input_ids)
    feature = InputFeatures(input_ids=input_ids, input_size=input_size, label=None)
    return feature


def main(data):
    tokenizer = tokenization.FullTokenizer(vocab_file=r'./vocab.txt')
    config = modelling.LstmConfig(vocab_size=68355)

    input_ids = []
    input_sizes = []
    max_length = 0
    for index in range(len(data)):
        guid = 'test-%d' % index
        text = tokenization.convert_to_unicode(str(data[index]))
        data_example = InputExample(guid=guid, text=text, label=None)

        data_feature = convert_single_example(example=data_example, tokenizer=tokenizer)
        print(data_feature.input_ids)
        if len(data_feature.input_ids) > max_length:
            max_length = len(data_feature.input_ids)
        input_ids.append(data_feature.input_ids)
        input_sizes.append(data_feature.input_size)

    for input_id in input_ids:
        if len(input_id) < max_length:
            input_id.extend((max_length-len(input_id)) * [0])

    input_ids = tf.convert_to_tensor(input_ids)
    input_sizes = tf.convert_to_tensor(input_sizes)

    predict_model = modelling.LstmModel(config, mode='predict')
    output_op = predict_model.forward(input_ids, input_sizes, label=None)

    with tf.Session() as sess:
        saver = tf.train.Saver()
        saver.restore(sess, r'./model/lstm.ckpt')
        output = sess.run(output_op)
        print(output)
        print(tf.argmax(output, 1).eval())


if __name__ == '__main__':

    text = ['这个房间真的很棒，又舒服又便宜', '这个房间太差了，又贵又破，不推荐', '一句很普通很中立的话']
    main(text)
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结果

同样的训练集与测试集，在Bert上跑完的准确度是0.946，用lstm，在2个epoch上且没有调整学习率的情况下，跑完准确度有0.897。其中Bert模型在服务器上跑了三天两夜，lstm在本机cpu上跑了一个半小时。结果还可以。

To Do

tf.estimator，这个高级封装的api似乎很牛逼，Bert中也使用了这个api，要继续学习学习。牵扯到model_fn的构建。我理解应该是重新复构模型的结构。在modelling中应该只定义模型结构，对于前向传播的过程，都到model_fn中完成。但是具体的操作还需要进一步的了解。

不得不说，虽然这样的代码可能还是不算高级，但是已经比我之前自己写的初版，在结构上要清楚太多了。同时也对tf.dataset有了比较深的了解（之前的next_batch都是完全自己写的，这里用dataset的iterator就可以搞定）。やはり多读读牛逼的源码是很有用的。