学习笔记TF019:序列分类、IMDB影评分类

清醒疯子 发布于 3周前
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序列分类,预测整个输入序列的类别标签。情绪分析,预测用户撰写文字话题态度。预测选举结果或产品、电影评分。

国际电影数据库(International Movie Database)影评数据集。目标值二元,正面或负面。语言大量否定、反语、模糊,不能只看单词是否出现。构建词向量循环网络,逐个单词查看每条评论,最后单词话性值训练预测整条评论情绪分类器。

斯担福大学人工智能实验室的IMDB影评数据集: http://ai.stanford.edu/~amaas/data/sentiment/ 。压缩tar文档,正面负面评论从两个文件夹文本文件获取。利用正则表达式提取纯文本,字母全部转小写。

词向量嵌入表示,比独热编码词语语义更丰富。词汇表确定单词索引,找到正确词向量。序列填充相同长度,多个影评数据批量送入网络。

序列标注模型,传入两个占位符,一输入数据data或序列,二目标值target或情绪。传入配置参数params对象,优化器。

动态计算当前批数据序列长度。数据单个张量形式,各序列以最长影评长度补0。绝对值最大值缩减词向量。零向量,标量0。实型词向量,标量大于0实数。tf.sign()离散为0或1。结果沿时间步相加,得到序列长度。张量长度与批数据容量相同,标量表示序列长度。

使用params对象定义单元类型和单元数量。length属性指定向RNN提供批数据最多行数。获取每个序列最后活性值,送入softmax层。因每条影评长度不同,批数据每个序列RNN最后相关输出活性值有不同索引。在时间步维度(批数据形状sequencestime_stepswordvectors)建立索引。tf.gather()沿第1维建立索引。输出活性值形状sequences*timesteps*word_vectors前两维扁平化(flatten),添加序列长度。添加length-1,选择最后有效时间步。

梯度裁剪,梯度值限制在合理范围内。可用任何中分类有意义代价函数,模型输出可用所有类别概率分布。增加梯度裁剪(gradient clipping)改善学习结果,限制最大权值更新。RNN训练难度大,不同超参数搭配不当,权值极易发散。

TensorFlow支持优化器实例computegradients函数推演,修改梯度,applygradients函数应用权值变化。梯度分量小于-limit,设置-limit;梯度分量在于limit,设置limit。TensorFlow导数可取None,表示某个变量与代价函数没有关系,数学上应为零向量但None利于内部性能优化,只需传回None值。

影评逐个单词送入循环神经网络,每个时间步由词向量构成批数据。batched函数查找词向量,所有序列长度补齐。训练模型,定义超参数、加载数据集和词向量、经过预处理训练批数据运行模型。模型成功训练,取决网络结构、超参数、词向量质量。可从skip-gram模型word2vec项目(https://code.google.com/archive/p/word2vec/ )、斯坦福NLP研究组Glove模型(https://nlp.stanford.edu/projects/glove ),加载预训练词向量。

Kaggle 开放学习竞赛(https://kaggle.com/c/word2vec-nlp-tutorial ),IMDB影评数据,与他人比较预测结果。

import tarfile
import re

from helpers import download


class ImdbMovieReviews:

    DEFAULT_URL = 
    'http://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz'
    TOKEN_REGEX = re.compile(r'[A-Za-z]+|[!?.:,()]')

def __init__(self, cache_dir, url=None):
    self._cache_dir = cache_dir
    self._url = url or type(self).DEFAULT_URL

    def __iter__(self):
        filepath = download(self._url, self._cache_dir)
        with tarfile.open(filepath) as archive:
            for filename in archive.getnames():
                if filename.startswith('aclImdb/train/pos/'):
                    yield self._read(archive, filename), True
                elif filename.startswith('aclImdb/train/neg/'):
                    yield self._read(archive, filename), False

    def _read(self, archive, filename):
        with archive.extractfile(filename) as file_:
            data = file_.read().decode('utf-8')
            data = type(self).TOKEN_REGEX.findall(data)
            data = [x.lower() for x in data]
            return data

import bz2
import numpy as np


class Embedding:

    def __init__(self, vocabulary_path, embedding_path, length):
        self._embedding = np.load(embedding_path)
        with bz2.open(vocabulary_path, 'rt') as file_:
            self._vocabulary = {k.strip(): i for i, k in enumerate(file_)}
        self._length = length

    def __call__(self, sequence):
        data = np.zeros((self._length, self._embedding.shape[1]))
        indices = [self._vocabulary.get(x, 0) for x in sequence]
        embedded = self._embedding[indices]
        data[:len(sequence)] = embedded
        return data

    @property
    def dimensions(self):
        return self._embedding.shape[1]

import tensorflow as tf

from helpers import lazy_property


class SequenceClassificationModel:

    def __init__(self, data, target, params):
        self.data = data
        self.target = target
        self.params = params
        self.prediction
        self.cost
        self.error
        self.optimize

    @lazy_property
    def length(self):
        used = tf.sign(tf.reduce_max(tf.abs(self.data), reduction_indices=2))
        length = tf.reduce_sum(used, reduction_indices=1)
        length = tf.cast(length, tf.int32)
        return length

    @lazy_property
    def prediction(self):
        # Recurrent network.
        output, _ = tf.nn.dynamic_rnn(
            self.params.rnn_cell(self.params.rnn_hidden),
            self.data,
            dtype=tf.float32,
            sequence_length=self.length,
        )
        last = self._last_relevant(output, self.length)
        # Softmax layer.
        num_classes = int(self.target.get_shape()[1])
        weight = tf.Variable(tf.truncated_normal(
            [self.params.rnn_hidden, num_classes], stddev=0.01))
        bias = tf.Variable(tf.constant(0.1, shape=[num_classes]))
        prediction = tf.nn.softmax(tf.matmul(last, weight) + bias)
        return prediction

    @lazy_property
    def cost(self):
        cross_entropy = -tf.reduce_sum(self.target * tf.log(self.prediction))
        return cross_entropy

    @lazy_property
    def error(self):
        mistakes = tf.not_equal(
            tf.argmax(self.target, 1), tf.argmax(self.prediction, 1))
        return tf.reduce_mean(tf.cast(mistakes, tf.float32))

    @lazy_property
    def optimize(self):
        gradient = self.params.optimizer.compute_gradients(self.cost)
        try:
            limit = self.params.gradient_clipping
            gradient = [
                (tf.clip_by_value(g, -limit, limit), v)
                if g is not None else (None, v)
                for g, v in gradient]
        except AttributeError:
            print('No gradient clipping parameter specified.')
        optimize = self.params.optimizer.apply_gradients(gradient)
        return optimize

    @staticmethod
    def _last_relevant(output, length):
        batch_size = tf.shape(output)[0]
        max_length = int(output.get_shape()[1])
        output_size = int(output.get_shape()[2])
        index = tf.range(0, batch_size) * max_length + (length - 1)
        flat = tf.reshape(output, [-1, output_size])
        relevant = tf.gather(flat, index)
        return relevant

import tensorflow as tf

from helpers import AttrDict

from Embedding import Embedding
from ImdbMovieReviews import ImdbMovieReviews
from preprocess_batched import preprocess_batched
from SequenceClassificationModel import SequenceClassificationModel

IMDB_DOWNLOAD_DIR = './imdb'
WIKI_VOCAB_DIR = '../01_wikipedia/wikipedia'
WIKI_EMBED_DIR = '../01_wikipedia/wikipedia'


params = AttrDict(
    rnn_cell=tf.contrib.rnn.GRUCell,
    rnn_hidden=300,
    optimizer=tf.train.RMSPropOptimizer(0.002),
    batch_size=20,
)

reviews = ImdbMovieReviews(IMDB_DOWNLOAD_DIR)
length = max(len(x[0]) for x in reviews)

embedding = Embedding(
    WIKI_VOCAB_DIR + '/vocabulary.bz2',
    WIKI_EMBED_DIR + '/embeddings.npy', length)
batches = preprocess_batched(reviews, length, embedding, params.batch_size)

data = tf.placeholder(tf.float32, [None, length, embedding.dimensions])
target = tf.placeholder(tf.float32, [None, 2])
model = SequenceClassificationModel(data, target, params)

sess = tf.Session()
sess.run(tf.initialize_all_variables())
for index, batch in enumerate(batches):
    feed = {data: batch[0], target: batch[1]}
    error, _ = sess.run([model.error, model.optimize], feed)
    print('{}: {:3.1f}%'.format(index + 1, 100 * error))

参考资料: 《面向机器智能的TensorFlow实践》

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