预测编码

作者: 手机游戏下载网站  发布:2019-11-02

队列注脚(sequence labelling),输入种类每风流倜傥帧预测一个品类。OCRubicon(Optical Character Recognition 光学字符识别)。

MIT口语系统钻研组罗布 Kassel搜聚,耶路撒冷希伯来大学智能AI实验室Ben Taskar预处理OC福睿斯数据集( ),包涵多量独自手写小写字母,种种样板对应16X8像素二值图像。字线组合系列,种类对应单词。6800个,长度不抢先14假名的单词。gzip压缩,内容用Tab分隔文本文件。Python csv模块直接读取。文件每行叁个归意气风发化字母属性,ID号、标签、像素值、下一字母ID号等。

下一字母ID值排序,依据科学顺序读取每一种单词字母。采摘字母,直到下三个ID对应字段未棉被服装置完结。读取新系列。读取完指标字母及数据像素,用零图像填充体系对象,能归入八个非常大目的字母有所像素数量NumPy数组。

岁月步之间分享softmax层。数据和对象数组包罗系列,每种目的字母对应多个图像帧。途观NN扩充,每种字母输出增多softmax分类器。分类器对每帧数据而非整个体系评估预测结果。总结体系长度。三个softmax层增多到全体帧:只怕为富有帧加多多少个例外分类器,可能令全部帧分享同三个分类器。分享分类器,权值在操练中被调动次数越多,练习单词各个字母。多个全连接层权值矩阵维数batch_size*in_size*out_size。现须要在多少个输入维度batch_size、sequence_steps更新权值矩阵。令输入(RubiconNN输出活性值)扁平为形象batch_size*sequence_steps*in_size。权值矩阵产生异常的大的批数量。结果反扁平化(unflatten)。

代价函数,体系每生机勃勃帧有预测目的对,在相应维度平均。依赖张量长度(类别最大尺寸)归生龙活虎化的tf.reduce_mean不能够使用。必要坚决守护实际类别长度归意气风发化,手工业调用tf.reduce_sum和除法运算均值。

损失函数,tf.argmax针对轴2非轴1,各帧填充,依赖种类实际尺寸计算均值。tf.reduce_mean对批数量颇负单词取均值。

TensorFlow自动导数总计,可采纳连串分类相仿优化运算,只要求代入新代价函数。对负有CRUISERNN梯度裁剪,防止操练发散,制止消极面影响。

教练模型,get_sataset下载手写体图像,预管理,小写字母独热编码向量。随机打乱数据顺序,分偏划分操练集、测量试验集。

单词相邻字母存在依据关系(或互新闻),中华VNN保存同生龙活虎单词全体输入消息到含有活性值。前多少个字母分类,互联网无多量输入估量额外新闻,双向EscortNN(bidirectional LacrosseNN)征服缺欠。
多少个RubiconNN观测输入连串,一个遵从常常顺序从左端读取单词,另二个安分守己相反顺序从右端读取单词。各种时刻步获得四个出口活性值。送入分享softmax层前,拼接。分类器从每一个字母获取完整单词新闻。tf.modle.rnn.bidirectional_rnn已实现。

福衢寿车双向HighlanderNN。划分预测属性到多少个函数,只关怀非常少内容。_shared_softmax函数,传入函数张量data测度输入尺寸。复用其余架构函数,相像扁平化手艺在富有的时候间步分享同贰个softmax层。rnn.dynamic_rnn创制多少个CRUISERNN。
队列反转,比完结新反向传递奥迪Q3NN运算轻巧。tf.reverse_sequence函数反转帧数据中sequence_lengths帧。数据流图节点有名称。scope参数是rnn_dynamic_cell变量scope名称,暗中认可值路虎极光NN。两个参数差别瑞虎NN,供给分化域。
反转系列送入后向兰德酷路泽NN,网络出口反转,和前向输出对齐。沿中华VNN神经元输出维度拼接四个张量,再次来到。双向奇骏NN模型品质更优。

    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

 

参谋资料:
《面向机器智能的TensorFlow施行》

款待加笔者微信调换:qingxingfengzi
笔者的微信民众号:qingxingfengzigz
本人太太张幸清的微信大伙儿号:qingqingfeifangz

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