学习笔记TF020:序列标注、手写小写字母OCR数据集、双向RNN

序列标注(sequence labelling)，输入序列每一帧预测一个类别。OCR(Optical Character Recognition 光学字符识别)。

MIT口语系统研究组Rob Kassel收集，斯坦福大学人工智能实验室Ben Taskar预处理OCR数据集(http://ai.stanford.edu/~btaskar/ocr/ )，包含大量单独手写小写字母，每个样本对应16X8像素二值图像。字线组合序列，序列对应单词。6800个，长度不超过14字母的单词。gzip压缩，内容用Tab分隔文本文件。Python csv模块直接读取。文件每行一个归一化字母属性，ID号、标签、像素值、下一字母ID号等。

下一字母ID值排序，按照正确顺序读取每个单词字母。收集字母，直到下一个ID对应字段未被设置为止。读取新序列。读取完目标字母及数据像素，用零图像填充序列对象，能纳入两个较大目标字母所有像素数据NumPy数组。

时间步之间共享softmax层。数据和目标数组包含序列，每个目标字母对应一个图像帧。RNN扩展，每个字母输出添加softmax分类器。分类器对每帧数据而非整个序列评估预测结果。计算序列长度。一个softmax层添加到所有帧：或者为所有帧添加几个不同分类器，或者令所有帧共享同一个分类器。共享分类器，权值在训练中被调整次数更多，训练单词每个字母。一个全连接层权值矩阵维数batch_size*in_size*out_size。现需要在两个输入维度batch_size、sequence_steps更新权值矩阵。令输入(RNN输出活性值)扁平为形状batch_size*sequence_steps*in_size。权值矩阵变成较大的批数据。结果反扁平化(unflatten)。

代价函数，序列每一帧有预测目标对，在相应维度平均。依据张量长度(序列最大长度)归一化的tf.reduce_mean无法使用。需要按照实际序列长度归一化，手工调用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，依据序列实际长度计算均值。tf.reduce_mean对批数据所有单词取均值。

TensorFlow自动导数计算，可使用序列分类相同优化运算，只需要代入新代价函数。对所有RNN梯度裁剪，防止训练发散，避免负面影响。

训练模型，get_sataset下载手写体图像，预处理，小写字母独热编码向量。随机打乱数据顺序，分偏划分训练集、测试集。

单词相邻字母存在依赖关系(或互信息)，RNN保存同一单词全部输入信息到隐含活性值。前几个字母分类，网络无大量输入推断额外信息，双向RNN(bidirectional RNN)克服缺陷。
两个RNN观测输入序列，一个按照通常顺序从左端读取单词，另一个按照相反顺序从右端读取单词。每个时间步得到两个输出活性值。送入共享softmax层前，拼接。分类器从每个字母获取完整单词信息。tf.modle.rnn.bidirectional_rnn已实现。

实现双向RNN。划分预测属性到两个函数，只关注较少内容。_shared_softmax函数，传入函数张量data推断输入尺寸。复用其他架构函数，相同扁平化技巧在所有时间步共享同一个softmax层。rnn.dynamic_rnn创建两个RNN。
序列反转，比实现新反向传递RNN运算容易。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，默认值RNN。两个参数不同RNN，需要不同域。
反转序列送入后向RNN，网络输出反转，和前向输出对齐。沿RNN神经元输出维度拼接两个张量，返回。双向RNN模型性能更优。

import gzipimport csvimport numpy as npfrom helpers import downloadclass OcrDataset:    URL = 'http://ai.stanford.edu/~btaskar/ocr/letter.data.gz'    def __init__(self, cache_dir):        path = download(type(self).URL, cache_dir)        lines = self._read(path)        data, target = self._parse(lines)        self.data, self.target = self._pad(data, target)    @staticmethod    def _read(filepath):        with gzip.open(filepath, 'rt') as file_:            reader = csv.reader(file_, delimiter='\t')            lines = list(reader)            return lines    @staticmethod    def _parse(lines):        lines = sorted(lines, key=lambda x: int(x[0]))        data, target = [], []        next_ = None        for line in lines:            if not next_:                data.append([])                target.append([])            else:                assert next_ == int(line[0])            next_ = int(line[2]) if int(line[2]) > -1 else None            pixels = np.array([int(x) for x in line[6:134]])            pixels = pixels.reshape((16, 8))            data[-1].append(pixels)            target[-1].append(line[1])        return data, target    @staticmethod    def _pad(data, target):        max_length = max(len(x) for x in target)        padding = np.zeros((16, 8))        data = [x + ([padding] * (max_length - len(x))) for x in data]        target = [x + ([''] * (max_length - len(x))) for x in target]        return np.array(data), np.array(target)import tensorflow as tffrom helpers import lazy_propertyclass SequenceLabellingModel:    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):        output, _ = tf.nn.dynamic_rnn(            tf.nn.rnn_cell.GRUCell(self.params.rnn_hidden),            self.data,            dtype=tf.float32,            sequence_length=self.length,        )        # Softmax layer.        max_length = int(self.target.get_shape()[1])        num_classes = int(self.target.get_shape()[2])        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]))        # Flatten to apply same weights to all time steps.        output = tf.reshape(output, [-1, self.params.rnn_hidden])        prediction = tf.nn.softmax(tf.matmul(output, weight) + bias)        prediction = tf.reshape(prediction, [-1, max_length, num_classes])        return prediction    @lazy_property    def cost(self):        # Compute cross entropy for each frame.        cross_entropy = self.target * tf.log(self.prediction)        cross_entropy = -tf.reduce_sum(cross_entropy, reduction_indices=2)        mask = tf.sign(tf.reduce_max(tf.abs(self.target), reduction_indices=2))        cross_entropy *= mask        # Average over actual sequence lengths.        cross_entropy = tf.reduce_sum(cross_entropy, reduction_indices=1)        cross_entropy /= tf.cast(self.length, tf.float32)        return tf.reduce_mean(cross_entropy)    @lazy_property    def error(self):        mistakes = tf.not_equal(            tf.argmax(self.target, 2), tf.argmax(self.prediction, 2))        mistakes = tf.cast(mistakes, tf.float32)        mask = tf.sign(tf.reduce_max(tf.abs(self.target), reduction_indices=2))        mistakes *= mask        # Average over actual sequence lengths.        mistakes = tf.reduce_sum(mistakes, reduction_indices=1)        mistakes /= tf.cast(self.length, tf.float32)        return tf.reduce_mean(mistakes)    @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 optimizeimport randomimport tensorflow as tfimport numpy as npfrom helpers import AttrDictfrom OcrDataset import OcrDatasetfrom SequenceLabellingModel import SequenceLabellingModelfrom batched import batchedparams = AttrDict(    rnn_cell=tf.nn.rnn_cell.GRUCell,    rnn_hidden=300,    optimizer=tf.train.RMSPropOptimizer(0.002),    gradient_clipping=5,    batch_size=10,    epochs=5,    epoch_size=50)def get_dataset():    dataset = OcrDataset('./ocr')    # Flatten images into vectors.    dataset.data = dataset.data.reshape(dataset.data.shape[:2] + (-1,))    # One-hot encode targets.    target = np.zeros(dataset.target.shape + (26,))    for index, letter in np.ndenumerate(dataset.target):        if letter:            target[index][ord(letter) - ord('a')] = 1    dataset.target = target    # Shuffle order of examples.    order = np.random.permutation(len(dataset.data))    dataset.data = dataset.data[order]    dataset.target = dataset.target[order]    return dataset# Split into training and test data.dataset = get_dataset()split = int(0.66 * len(dataset.data))train_data, test_data = dataset.data[:split], dataset.data[split:]train_target, test_target = dataset.target[:split], dataset.target[split:]# Compute graph._, length, image_size = train_data.shapenum_classes = train_target.shape[2]data = tf.placeholder(tf.float32, [None, length, image_size])target = tf.placeholder(tf.float32, [None, length, num_classes])model = SequenceLabellingModel(data, target, params)batches = batched(train_data, train_target, params.batch_size)sess = tf.Session()sess.run(tf.initialize_all_variables())for index, batch in enumerate(batches):    batch_data = batch[0]    batch_target = batch[1]    epoch = batch[2]    if epoch >= params.epochs:        break    feed = {data: batch_data, target: batch_target}    error, _ = sess.run([model.error, model.optimize], feed)    print('{}: {:3.6f}%'.format(index + 1, 100 * error))test_feed = {data: test_data, target: test_target}test_error, _ = sess.run([model.error, model.optimize], test_feed)print('Test error: {:3.6f}%'.format(100 * error))import tensorflow as tffrom helpers import lazy_propertyclass BidirectionalSequenceLabellingModel:    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):        output = self._bidirectional_rnn(self.data, self.length)        num_classes = int(self.target.get_shape()[2])        prediction = self._shared_softmax(output, num_classes)        return prediction    def _bidirectional_rnn(self, data, length):        length_64 = tf.cast(length, tf.int64)        forward, _ = tf.nn.dynamic_rnn(            cell=self.params.rnn_cell(self.params.rnn_hidden),            inputs=data,            dtype=tf.float32,            sequence_length=length,            scope='rnn-forward')        backward, _ = tf.nn.dynamic_rnn(        cell=self.params.rnn_cell(self.params.rnn_hidden),        inputs=tf.reverse_sequence(data, length_64, seq_dim=1),        dtype=tf.float32,        sequence_length=self.length,        scope='rnn-backward')        backward = tf.reverse_sequence(backward, length_64, seq_dim=1)        output = tf.concat(2, [forward, backward])        return output    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    @lazy_property    def cost(self):        # Compute cross entropy for each frame.        cross_entropy = self.target * tf.log(self.prediction)        cross_entropy = -tf.reduce_sum(cross_entropy, reduction_indices=2)        mask = tf.sign(tf.reduce_max(tf.abs(self.target), reduction_indices=2))        cross_entropy *= mask        # Average over actual sequence lengths.        cross_entropy = tf.reduce_sum(cross_entropy, reduction_indices=1)        cross_entropy /= tf.cast(self.length, tf.float32)        return tf.reduce_mean(cross_entropy)    @lazy_property    def error(self):        mistakes = tf.not_equal(            tf.argmax(self.target, 2), tf.argmax(self.prediction, 2))        mistakes = tf.cast(mistakes, tf.float32)        mask = tf.sign(tf.reduce_max(tf.abs(self.target), reduction_indices=2))        mistakes *= mask        # Average over actual sequence lengths.        mistakes = tf.reduce_sum(mistakes, reduction_indices=1)        mistakes /= tf.cast(self.length, tf.float32)        return tf.reduce_mean(mistakes)    @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

参考资料：
《面向机器智能的TensorFlow实践》

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