学习笔记TF021:预测编码、字符级语言建模、ArXiv摘要

清醒疯子 发布于 2月前
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预测编码(predictive coding),向RNN输入大量序列,训练预测序列下一帧能力。语言建模(language modelling),预测一个句子中下一个单词的似然。生成文本,依据网络下一个单词分布抽样,训练结束,种子单词(seed word)送入RNN,观察预测的下一个单词,最可能单词输入RNN,重复,生成新内容。预测编码压缩训练网络任意序列所有重要信息。网络捕捉语法、语言规则,精确预测是语言下一个字符。

字符级语言建模,网络不仅学会构词,还学会拼写,网络输入维数更低,不必考虑未知单词,可以发明新单词。Andrew Karpathy 2015年应用RNN于字符级语言建模。https://github.com/karpathy/char-rnn 。

ArXiv.org托管计算机科学、数学、物理学、生物学等领域研究论文。提供基于Web可检索文献API。

依据给定搜索查询从ArXiv获取摘要,在构造方法,检查是否有旧摘要转储文件。有,直接使用,不调ArXiv API。执行新查询,删除或转移旧转储文件。可以优化检查已有文件与新类别、新关键词是否匹配。没有转储文件,调fetchall,生成行写入磁盘。

只在Machine Learning、Neural and Evolutionary Computing、Optimization and Control,搜索机器学习论文。只返回包含单词neural、network、deep元数据结果。

fetchall完成分页。每次查询,返回定量摘要,指定偏移量获到指定页结果。fetchpage传入指定页面尺寸参数。参数很大,尝试一次性得到全部结果,严重影响查询效率。页面获取容错性更强,减小ArXiv API负载。

抓取结果XML格式,BeautifulSoup库提取摘要。执行命令 sudo -H pip3 install beautifulsoup4 安装。查看文章 标签,读取

标签摘要文本。

定义任务,编写解析器获取数据集。预测编码模型,预测输入序列下一个字符,只有一个输入,构造方法sequence参数。参数对象,修改重要选项,复现实验。initial参数,默认值None,循环连接层初始内部活性值。TensorFlow隐状态初始化为零张量,语言模型采样时需要再定义。

数据处理,构造办玫数据、目标序列,引入时域差。时间步t,St输入,St+1输出。提供序列切片,切除第一帧或最后一帧。tf.slice切片运算,参数序列、各维起始索引元组、各维大小元组。sizes-1保持维度起始索引到终止索引所有元素不变。只关心第2维。

mask,尺寸batchsize*maxlength张量,分量非0即1,取决帧是否被使用。属性length沿时间轴对mask求和,得到每个序列长度。mask、length属性对数据序列合法,与目标序列长度相同,不在数据序列上计算,包含最后一帧,没有下一字母可预测。数据张量最后一帧切除,包含填序帧,不包含大多数序列实际最后一帧。用mask对代价函数掩膜处理。

同时获得预测和最后循环活性值。之前仅返回预测值。最后活性值有效生成序列。forward返回两个张量元组,prediction、state只是方便外部访问。

每个时间步,模型从词汇表预测下一字母。分类问题,采用交叉熵代价函数,计算字符预测错误率。logprob属性,刻画模型对数空间正确下一字母分配概率。变换到对数空间取均值负交叉熵。结果返回线性空间,得到混淆度(perplexity)。混淆度表示模型在每个时间步猜测选项数目。完美模型,混淆度1。每个类别输出相同概率模型,混淆度为n。如果下一字母零概率,混淆度会变无穷大。预测概率箝位在很小正数和1之间。

固定长度序列,结果tf.reducemean。变长序列,与掩码相乘,屏蔽填充帧,沿帧尺寸聚合,每帧只有一个元素集,tf.reducesum聚合各帧为一个标量。

序列实际长度取平均每个序列各帧。使用每个序列长度最大值和1,避免空序列除数为0。tf.reduce_mean取平均批数据样本。

已构建模块整合,数据集、预处理步聚、网络模型。打印混淆度度量,周期性保存训练进展。加载数据集,数据流图定义输入,预处理数据集训练模型,追踪对数几率,相邻两次训练epoch评价时间计算打印混淆度。initorloadsession,tf.train.Saver保存数据流图tf.Variable当前值到检查点文件。实际点检查(checkpointing)在evalution完成。寻找已有检查点文件另载。tf.train.getcheckpoint_state从检查点文件目录查找TensorFlow元数据文件。检查点文件,通过指定数字(epoch数)预先准备。加载检查点文件,Python正则表达式包re提取epoch数。

调用Training(get_params())()。20 epoch 1 小时。20 epochs*200 batches*100 examples*50 characters = 20M个字母。模型在混淆度1.5/字母时收敛。每个字母只需1.5位,可实现文本压缩。单词级语言模型,依据单词数取平均。乘以每个单词平均字符数。

利用训练好模型生成新的相似序列。从磁盘加载最新模型检查点,定义占位符,数据输入数据流图,生成新数据。

构造方法,创建预处理类实例,转化当前生成序列为NumPy向量,输入数据流图。占位符sequencec预留每批数据一个序列空间。序列长度为2。模型将除最后字符外所有字符作为输入,除首字符外所有字符作为目标。当前文本最后字符和序列任意第二字符输入模型。网络为第一字符预测结果,第二字符作目标值。获取循环神经网络最后活性值,初始化网络下次运行时状态。模型初始状态参数,使用过的GRUCell状态,尺寸rnnlayers*rnnunits向量。

call函数,采样文本序列逻辑。从一个采样种子开始,每次预测一个字符,当前文本送入网络。相同预处理类转换当前文本为填充NumPy块送入网络。批数据只有一个序列和一个输出帧,只关心索引[0, 0]预测结果。

_sample函数对softmaxl输出采样。选取序列最优预测,作为下一帧传入网络生成序列。实际不是只选择最可能下一帧,从RNN输出概率分布随机采样。高输出概率高单词更可能选中,输出概率低单词也可能被选中。

引入温度参数T,使softmax层输出分布预测更相似或更不同。在线性空间缩放输出,变换至指数空间并再次归一化。运用自然对数撤销。每个值除以选择温度值,得新应用softmax函数。

调用Sampling(get_params())('We', 500) 。捕捉数据内部统计依赖性。

import requests
import os
from bs4 import BeautifulSoup

from helpers import ensure_directory

class ArxivAbstracts:

    ENDPOINT = 'http://export.arxiv.org/api/query'
    PAGE_SIZE = 100

    def __init__(self, cache_dir, categories, keywords, amount=None):
        self.categories = categories
        self.keywords = keywords
        cache_dir = os.path.expanduser(cache_dir)
        ensure_directory(cache_dir)
        filename = os.path.join(cache_dir, 'abstracts.txt')
        if not os.path.isfile(filename):
            with open(filename, 'w') as file_:
                for abstract in self._fetch_all(amount):
                    file_.write(abstract + 'n')
        with open(filename) as file_:
            self.data = file_.readlines()

    def _fetch_all(self, amount):
        page_size = type(self).PAGE_SIZE
        count = self._fetch_count()
        if amount:
            count = min(count, amount)
        for offset in range(0, count, page_size):
            print('Fetch papers {}/{}'.format(offset + page_size, count))
            yield from self._fetch_page(page_size, count)

    def _fetch_page(self, amount, offset):
        url = self._build_url(amount, offset)
        response = requests.get(url)
        soup = BeautifulSoup(response.text)
        for entry in soup.findAll('entry'):
            text = entry.find('summary').text
            text = text.strip().replace('n', ' ')
            yield text

    def _fetch_count(self):
        url = self._build_url(0, 0)
        response = requests.get(url)
        soup = BeautifulSoup(response.text, 'lxml')
        count = int(soup.find('opensearch:totalresults').string)
        print(count, 'papers found')
        return count

    def _build_url(self, amount, offset):
        categories = ' OR '.join('cat:' + x for x in self.categories)
        keywords = ' OR '.join('all:' + x for x in self.keywords)
        url = type(self).ENDPOINT
        url += '?search_query=(({}) AND ({}))'.format(categories, keywords)
        url += '&max_results={}&offset={}'.format(amount, offset)
        return url

import random
import numpy as np

class Preprocessing:

    VOCABULARY = 
        " $%'()+,-./0123456789:;=?ABCDEFGHIJKLMNOPQRSTUVWXYZ" 
        "\^_abcdefghijklmnopqrstuvwxyz{|}"

    def __init__(self, texts, length, batch_size):
        self.texts = texts
        self.length = length
        self.batch_size = batch_size
        self.lookup = {x: i for i, x in enumerate(self.VOCABULARY)}

    def __call__(self, texts):
        batch = np.zeros((len(texts), self.length, len(self.VOCABULARY)))
        for index, text in enumerate(texts):
            text = [x for x in text if x in self.lookup]
            assert 2 <= len(text) <= self.length
            for offset, character in enumerate(text):
                code = self.lookup[character]
                batch[index, offset, code] = 1
        return batch

    def __iter__(self):
        windows = []
        for text in self.texts:
            for i in range(0, len(text) - self.length + 1, self.length // 2):
                windows.append(text[i: i + self.length])
        assert all(len(x) == len(windows[0]) for x in windows)
        while True:
            random.shuffle(windows)
            for i in range(0, len(windows), self.batch_size):
                batch = windows[i: i + self.batch_size]
                yield self(batch)

import tensorflow as tf
from helpers import lazy_property

class PredictiveCodingModel:

    def __init__(self, params, sequence, initial=None):
        self.params = params
        self.sequence = sequence
        self.initial = initial
        self.prediction
        self.state
        self.cost
        self.error
        self.logprob
        self.optimize

    @lazy_property
    def data(self):
        max_length = int(self.sequence.get_shape()[1])
        return tf.slice(self.sequence, (0, 0, 0), (-1, max_length - 1, -1))

    @lazy_property
    def target(self):
        return tf.slice(self.sequence, (0, 1, 0), (-1, -1, -1))

    @lazy_property
    def mask(self):
        return tf.reduce_max(tf.abs(self.target), reduction_indices=2)

    @lazy_property
    def length(self):
        return tf.reduce_sum(self.mask, reduction_indices=1)

    @lazy_property
    def prediction(self):
        prediction, _ = self.forward
        return prediction

    @lazy_property
    def state(self):
        _, state = self.forward
        return state

    @lazy_property
    def forward(self):
        cell = self.params.rnn_cell(self.params.rnn_hidden)
        cell = tf.nn.rnn_cell.MultiRNNCell([cell] * self.params.rnn_layers)
        hidden, state = tf.nn.dynamic_rnn(
            inputs=self.data,
            cell=cell,
            dtype=tf.float32,
            initial_state=self.initial,
            sequence_length=self.length)
        vocabulary_size = int(self.target.get_shape()[2])
        prediction = self._shared_softmax(hidden, vocabulary_size)
        return prediction, state

    @lazy_property
    def cost(self):
        prediction = tf.clip_by_value(self.prediction, 1e-10, 1.0)
        cost = self.target * tf.log(prediction)
        cost = -tf.reduce_sum(cost, reduction_indices=2)
        return self._average(cost)

    @lazy_property
    def error(self):
        error = tf.not_equal(
            tf.argmax(self.prediction, 2), tf.argmax(self.target, 2))
        error = tf.cast(error, tf.float32)
        return self._average(error)

    @lazy_property
    def logprob(self):
        logprob = tf.mul(self.prediction, self.target)
        logprob = tf.reduce_max(logprob, reduction_indices=2)
        logprob = tf.log(tf.clip_by_value(logprob, 1e-10, 1.0)) / tf.log(2.0)
        return self._average(logprob)

    @lazy_property
    def optimize(self):
        gradient = self.params.optimizer.compute_gradients(self.cost)
        if self.params.gradient_clipping:
            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]
        optimize = self.params.optimizer.apply_gradients(gradient)
        return optimize

    def _average(self, data):
        data *= self.mask
        length = tf.reduce_sum(self.length, 0)
        data = tf.reduce_sum(data, reduction_indices=1) / length
        data = tf.reduce_mean(data)
        return data

    def _shared_softmax(self, data, out_size):
        max_length = int(data.get_shape()[1])
        in_size = int(data.get_shape()[2])
        weight = tf.Variable(tf.truncated_normal(
            [in_size, out_size], stddev=0.01))
        bias = tf.Variable(tf.constant(0.1, shape=[out_size]))
        # Flatten to apply same weights to all time steps.
        flat = tf.reshape(data, [-1, in_size])
        output = tf.nn.softmax(tf.matmul(flat, weight) + bias)
        output = tf.reshape(output, [-1, max_length, out_size])
        return output

import os
import re
import tensorflow as tf
import numpy as np

from helpers import overwrite_graph
from helpers import ensure_directory
from ArxivAbstracts import ArxivAbstracts
from Preprocessing import Preprocessing
from PredictiveCodingModel import PredictiveCodingModel

class Training:

    @overwrite_graph
    def __init__(self, params, cache_dir, categories, keywords, amount=None):
        self.params = params
        self.texts = ArxivAbstracts(cache_dir, categories, keywords, amount).data
        self.prep = Preprocessing(
            self.texts, self.params.max_length, self.params.batch_size)
        self.sequence = tf.placeholder(
            tf.float32,
            [None, self.params.max_length, len(self.prep.VOCABULARY)])
        self.model = PredictiveCodingModel(self.params, self.sequence)
        self._init_or_load_session()

    def __call__(self):
        print('Start training')
        self.logprobs = []
        batches = iter(self.prep)
        for epoch in range(self.epoch, self.params.epochs + 1):
            self.epoch = epoch
            for _ in range(self.params.epoch_size):
                self._optimization(next(batches))
            self._evaluation()
        return np.array(self.logprobs)

    def _optimization(self, batch):
        logprob, _ = self.sess.run(
            (self.model.logprob, self.model.optimize),
            {self.sequence: batch})
        if np.isnan(logprob):
            raise Exception('training diverged')
        self.logprobs.append(logprob)

    def _evaluation(self):
        self.saver.save(self.sess, os.path.join(
            self.params.checkpoint_dir, 'model'), self.epoch)
        self.saver.save(self.sess, os.path.join(
            self.params.checkpoint_dir, 'model'), self.epoch)
        perplexity = 2 ** -(sum(self.logprobs[-self.params.epoch_size:]) /
                        self.params.epoch_size)
        print('Epoch {:2d} perplexity {:5.4f}'.format(self.epoch, perplexity))

    def _init_or_load_session(self):
        self.sess = tf.Session()
        self.saver = tf.train.Saver()
        checkpoint = tf.train.get_checkpoint_state(self.params.checkpoint_dir)
        if checkpoint and checkpoint.model_checkpoint_path:
            path = checkpoint.model_checkpoint_path
            print('Load checkpoint', path)
            self.saver.restore(self.sess, path)
            self.epoch = int(re.search(r'-(d+)$', path).group(1)) + 1
        else:
            ensure_directory(self.params.checkpoint_dir)
            print('Randomly initialize variables')
            self.sess.run(tf.initialize_all_variables())
            self.epoch = 1

from Training import Training
from get_params import get_params

Training(
    get_params(),
    cache_dir = './arxiv',
    categories = [
        'Machine Learning',
        'Neural and Evolutionary Computing',
        'Optimization'
    ],
    keywords = [
        'neural',
        'network',
        'deep'
    ]
    )()

import tensorflow as tf
import numpy as np

from helpers import overwrite_graph
from Preprocessing import Preprocessing
from PredictiveCodingModel import PredictiveCodingModel

class Sampling:

    @overwrite_graph
    def __init__(self, params):
        self.params = params
        self.prep = Preprocessing([], 2, self.params.batch_size)
        self.sequence = tf.placeholder(
            tf.float32, [1, 2, len(self.prep.VOCABULARY)])
        self.state = tf.placeholder(
            tf.float32, [1, self.params.rnn_hidden * self.params.rnn_layers])
        self.model = PredictiveCodingModel(
            self.params, self.sequence, self.state)
        self.sess = tf.Session()
        checkpoint = tf.train.get_checkpoint_state(self.params.checkpoint_dir)
        if checkpoint and checkpoint.model_checkpoint_path:
            tf.train.Saver().restore(
                self.sess, checkpoint.model_checkpoint_path)
        else:
           print('Sampling from untrained model.')
        print('Sampling temperature', self.params.sampling_temperature)

    def __call__(self, seed, length=100):
        text = seed
        state = np.zeros((1, self.params.rnn_hidden * self.params.rnn_layers))
        for _ in range(length):
            feed = {self.state: state}
            feed[self.sequence] = self.prep([text[-1] + '?'])
            prediction, state = self.sess.run(
                [self.model.prediction, self.model.state], feed)
            text += self._sample(prediction[0, 0])
        return text

    def _sample(self, dist):
        dist = np.log(dist) / self.params.sampling_temperature
        dist = np.exp(dist) / np.exp(dist).sum()
        choice = np.random.choice(len(dist), p=dist)
        choice = self.prep.VOCABULARY[choice]
        return choice

参考资料: 《面向机器智能的TensorFlow实践》

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