|
- # encoding=utf-8
-
- """
- 基于清华大学语料库的中文文本分类
- Author:MaCan
- """
-
- from __future__ import absolute_import
- from __future__ import division
- from __future__ import print_function
-
- import collections
- import os
- import codecs
- import pickle
- import numpy as np
- import tensorflow as tf
-
- import sys
- # sys.path.append('..')
- # from bert_base.server.helper import get_logger
- from bert_base.bert import modeling
- from bert_base.bert import optimization
- from bert_base.bert import tokenization
-
- os.environ['CUDA_VISIBLE_DEVICES'] = '2'
-
- if os.name == 'nt':
- bert_path = 'C:\迅雷下载\chinese_L-12_H-768_A-12'
- root_path = r'C:\workspace\python\BERT_Base'
- else:
- bert_path = '/home/macan/ml/data/chinese_L-12_H-768_A-12'
- root_path = '/home/macan/ml/workspace/BERT_Base2'
-
- flags = tf.flags
- FLAGS = flags.FLAGS
-
- ## Required parameters
- flags.DEFINE_string("data_dir", os.path.join(os.path.join(root_path, 'data'), 'classification'),
- "The input data dir. Should contain the .tsv files (or other data files) for the task.")
-
- flags.DEFINE_string(
- "bert_config_file", os.path.join(bert_path, 'bert_config.json'),
- "The config json file corresponding to the pre-trained BERT model. "
- "This specifies the model architecture.")
-
- flags.DEFINE_string("task_name", None, "The name of the task to train.")
-
- flags.DEFINE_string("vocab_file", os.path.join(bert_path, 'vocab.txt'),
- "The vocabulary file that the BERT model was trained on.")
-
- flags.DEFINE_string(
- "output_dir", os.path.join(os.path.join(root_path, 'output'), 'classification'),
- "The output directory where the model checkpoints will be written.")
-
- ## Other parameters
-
- flags.DEFINE_string(
- "init_checkpoint", os.path.join(bert_path, 'bert_model.ckpt'),
- "Initial checkpoint (usually from a pre-trained BERT model).")
-
- flags.DEFINE_bool(
- "do_lower_case", True,
- "Whether to lower case the input text. Should be True for uncased "
- "models and False for cased models.")
-
- flags.DEFINE_integer(
- "max_seq_length", 202,
- "The maximum total input sequence length after WordPiece tokenization. "
- "Sequences longer than this will be truncated, and sequences shorter "
- "than this will be padded.")
-
- flags.DEFINE_bool('clean', True, 'remove the files which created by last training')
-
- 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", True,
- "Whether to run the model in inference mode on the test set.")
-
- 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_float("learning_rate", 5e-5, "The initial learning rate for Adam.")
-
- flags.DEFINE_float('dropout_keep_prob', 0.5, 'dropout probability')
- flags.DEFINE_float("num_train_epochs", 5.0,
- "Total number of training epochs to perform.")
-
- flags.DEFINE_float(
- "warmup_proportion", 0.1,
- "Proportion of training to perform linear learning rate warmup for. "
- "E.g., 0.1 = 10% of training.")
-
- flags.DEFINE_integer("save_checkpoints_steps",500,
- "How often to save the model checkpoint.")
-
- flags.DEFINE_integer("iterations_per_loop", 1000,
- "How many steps to make in each estimator call.")
-
- flags.DEFINE_integer('save_summary_steps', 500, 'summary steps')
-
- # logger = get_logger(os.path.join(FLAGS.output_dir, 'c.log'))
- import logging
- logging.basicConfig(level = logging.INFO,format = '%(asctime)s - %(name)s - %(levelname)s - %(message)s')
- logger = logging.getLogger(__name__)
-
- class RestoreHook(tf.train.SessionRunHook):
- def __init__(self, init_fn):
- self.init_fn = init_fn
-
- def after_create_session(self, session, coord=None):
- if session.run(tf.train.get_or_create_global_step()) == 0:
- self.init_fn(session)
-
- class InputExample(object):
- """A single training/test example for simple sequence classification."""
-
- def __init__(self, guid, text_a, text_b=None, label=None):
- """Constructs a InputExample.
- Args:
- guid: Unique id for the example.
- text_a: string. The untokenized text of the first sequence. For single
- sequence tasks, only this sequence must be specified.
- text_b: (Optional) string. The untokenized text of the second sequence.
- Only must be specified for sequence pair tasks.
- label: (Optional) string. The label of the example. This should be
- specified for train and dev examples, but not for test examples.
- """
- self.guid = guid
- self.text_a = text_a
- self.text_b = text_b
- self.label = label
-
-
- class InputFeatures(object):
- """A single set of features of data."""
-
- def __init__(self, input_ids, input_mask, segment_ids, label_id):
- self.input_ids = input_ids
- self.input_mask = input_mask
- self.segment_ids = segment_ids
- self.label_id = label_id
-
-
- class DataProcessor(object):
- """Base class for data converters for sequence classification data sets."""
-
- def get_train_examples(self, data_dir):
- """Gets a collection of `InputExample`s for the train set."""
- raise NotImplementedError()
-
- def get_dev_examples(self, data_dir):
- """Gets a collection of `InputExample`s for the dev set."""
- raise NotImplementedError()
-
- def get_test_examples(self, data_dir):
- """Gets a collection of `InputExample`s for prediction."""
- raise NotImplementedError()
-
- def get_labels(self):
- """Gets the list of labels for this data set."""
- raise NotImplementedError()
-
- @classmethod
- def _read_tsv(cls, input_file, quotechar=None):
- """Reads a tab separated value file."""
- with codecs.open(input_file, "r", encoding='utf-8') as f:
- lines = []
- for line in f:
- line = line.strip()
- if line == '':
- continue
- line = line.split('__\t')
- if len(line) == 2:
- line[0] = line[0].replace('__', '')
- lines.append(line)
- return lines
-
-
- class ThuProcessor(DataProcessor):
- """Processor for the Thu data set."""
-
- def __init__(self):
- self.labels = set()
-
- def get_train_examples(self, data_dir):
- """See base class."""
- return self._create_examples(
- self._read_tsv(os.path.join(data_dir, "train.txt")), 'train')
-
- def get_dev_examples(self, data_dir):
- return self._create_examples(
- self._read_tsv(os.path.join(data_dir, "dev.txt")), 'dev')
-
- def get_test_examples(self, data_dir):
- return self._create_examples(
- self._read_tsv(os.path.join(data_dir, "test.txt")), 'test')
-
- def get_labels(self):
- """在读取数据的时候,自动获取类别个数"""
- if not os.path.exists(os.path.join(FLAGS.output_dir, 'label_list.pkl')):
- with codecs.open(os.path.join(FLAGS.output_dir, 'label_list.pkl'), 'wb') as fd:
- pickle.dump(self.labels, fd)
- else:
- with codecs.open(os.path.join(FLAGS.output_dir, 'label_list.pkl'), 'rb') as fd:
- labels = pickle.load(fd)
- if len(labels) > len(self.labels):
- self.labels = labels
- return list(self.labels)
-
- def _create_examples(self, lines, set_type):
- examples = []
- np.random.shuffle(lines)
- for i, line in enumerate(lines):
- guid = '%s-%s' %(set_type, i)
- # if set_type == 'test':
- # text_a = tokenization.convert_to_unicode(line[1])
- # label = '0'
- # else:
- # text_a = tokenization.convert_to_unicode(line[1])
- # label = tokenization.convert_to_unicode(line[0])
- # self.labels.add(label)
- text_a = tokenization.convert_to_unicode(line[1])
- label = tokenization.convert_to_unicode(line[0])
- self.labels.add(label)
- examples.append(
- InputExample(guid=guid, text_a=text_a, label=label, text_b=None)
- )
- return examples
-
- def conver_single_example(ex_index, example, label_list, max_seq_length, tokenizer, mode):
- """
- 将一个训练样本转化为InputFeature,其中进行字符seg并且index化,和label的index转化
- :param ex_index:
- :param example:
- :param label_list:
- :param max_seq_length:
- :param tokenizer:
- :return:
- """
- # 1. 构建label->id的映射
- label_map = {}
- if os.path.exists(os.path.join(FLAGS.output_dir, 'label2id.pkl')):
- with codecs.open(os.path.join(FLAGS.output_dir, 'label2id.pkl'), 'rb') as fd:
- label_map = pickle.load(fd)
- else:
- for i, label in enumerate(label_list):
- label_map[label] = i
- with codecs.open(os.path.join(FLAGS.output_dir, 'label2id.pkl'), 'wb') as fd:
- pickle.dump(label_map, fd)
- # 不考虑seq pair 分类的情况
- tokens_a = tokenizer.tokenize(example.text_a)
-
- # 截断,因为有句首和句尾的标识符
- if len(tokens_a) > max_seq_length - 2:
- tokens_a = tokens_a[0:(max_seq_length-2)]
-
- tokens = []
- segment_ids = []
- tokens.append('[CLS]')
- segment_ids.append(0)
- for token in tokens_a:
- tokens.append(token)
- segment_ids.append(0)
- tokens.append('[SEP]')
- segment_ids.append(0)
- #将字符转化为id形式
- input_ids = tokenizer.convert_tokens_to_ids(tokens)
-
- input_mask = [1]*len(input_ids)
- #补全到max_seg_length
- while len(input_ids) < max_seq_length:
- input_ids.append(0)
- segment_ids.append(0)
- input_mask.append(0)
- if example.label is None:
- label_id = -1
- else:
- label_id = label_map[example.label]
- if ex_index < 2 and mode in ['train', 'dev']:
- logger.info("*** Example ***")
- logger.info("guid: %s" % (example.guid))
- logger.info("tokens: %s" % " ".join(
- [tokenization.printable_text(x) for x in tokens]))
- logger.info("input_ids: %s" % " ".join([str(x) for x in input_ids]))
- logger.info("input_mask: %s" % " ".join([str(x) for x in input_mask]))
- logger.info("segment_ids: %s" % " ".join([str(x) for x in segment_ids]))
- logger.info("label: %s (id = %d)" % (example.label, label_id))
-
- feature = InputFeatures(input_ids=input_ids, input_mask=input_mask, segment_ids=segment_ids, label_id=label_id)
- return feature
-
- def file_based_convert_examples_to_features(examples, label_list, max_seq_length, tokenizer, output_file, mode):
- """
- 将训练文件转化特征后,存储为tf_record格式,用于模型的读取
- :param examples:
- :param label_list:
- :param max_seq_length:
- :param tokenizer:
- :param output_file:
- :return:
- """
- writer = tf.python_io.TFRecordWriter(path=output_file)
- # 将每一个样本转化为idx特征,封装到map中后进行序列化存储为record
- for ex_index, example in enumerate(examples):
- if ex_index % 10000 == 0:
- logger.info('Writing example %d of %d' %(ex_index, len(examples)))
- feature = conver_single_example(ex_index, example, label_list, max_seq_length, tokenizer, mode)
- # 将输入数据转化为64位int 的list,这是必须的
- 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_mask'] = create_int_feature(feature.input_mask)
- features['segment_ids'] = create_int_feature(feature.segment_ids)
- features['label_ids'] = create_int_feature([feature.label_id])
- # 转化为Example 协议内存块
- tf_example = tf.train.Example(features=tf.train.Features(feature=features))
- writer.write(tf_example.SerializeToString())
-
- def file_based_input_fn_builder(input_file, seq_length, num_label, is_training, drop_remainder):
- """
-
- :param input_file:
- :param seq_length:
- :param is_training:
- :param drop_remainder: 是否丢弃较小的batch
- :return:
- """
- name_to_features = {
- "input_ids": tf.FixedLenFeature([seq_length], tf.int64),
- "input_mask": tf.FixedLenFeature([seq_length], tf.int64),
- "segment_ids": tf.FixedLenFeature([seq_length], tf.int64),
- "label_ids": tf.FixedLenFeature([], tf.int64),
- }
-
- def _decode_record(record, name_to_feature):
- # 解析一个record中的数据
- example = tf.parse_single_example(record, name_to_feature)
- for name in list(example.keys()):
- t = example[name]
- if t.dtype == tf.int64:
- t = tf.to_int32(t)
- example[name] = t
- return example
-
- def input_fn(params):
- """
- 模型输入函数
- :param params:
- :return:
- """
- batch_size = params['batch_size']
- d = tf.data.TFRecordDataset(input_file)
- if is_training:
- d = d.repeat()
- d = d.shuffle(buffer_size=200)
-
- # tf.data.experimental.map_and_batch will be deprecated, the replace methods like bellow
- d = d.apply(tf.contrib.data.map_and_batch(
- lambda record: _decode_record(record, name_to_features),
- batch_size=batch_size,
- drop_remainder=drop_remainder
- ))
- # d = d.apply(lambda record: _decode_record(record, name_to_features))
- # d = d.batch(batch_size=batch_size, drop_remainder=drop_remainder)
- return d
- return input_fn
-
-
- def create_model(bert_config, is_training, input_ids, input_mask, segment_ids, labels, num_labels):
- """
-
- :param bert_config:
- :param is_training:
- :param input_ids:
- :param input_mask:
- :param segment_ids:
- :param labels:
- :param num_labels:
- :param use_one_hot_embedding:
- :return:
- """
- # 通过传入的训练数据,进行representation
- model = modeling.BertModel(
- config=bert_config,
- is_training=is_training,
- input_ids=input_ids,
- input_mask=input_mask,
- token_type_ids=segment_ids,
- )
-
- embedding_layer = model.get_sequence_output()
- output_layer = model.get_pooled_output()
- hidden_size = output_layer.shape[-1].value
-
- # model = CNN_Classification(embedding_chars=embedding_layer,
- # labels=labels,
- # num_tags=num_labels,
- # sequence_length=FLAGS.max_seq_length,
- # embedding_dims=embedding_layer.shape[-1].value,
- # vocab_size=0,
- # filter_sizes=[3, 4, 5],
- # num_filters=3,
- # dropout_keep_prob=FLAGS.dropout_keep_prob,
- # l2_reg_lambda=0.001)
- # loss, predictions, probabilities = model.add_cnn_layer()
-
- output_weights = tf.get_variable(
- "output_weights", [num_labels, hidden_size],
- initializer=tf.truncated_normal_initializer(stddev=0.02))
-
- output_bias = tf.get_variable(
- "output_bias", [num_labels], initializer=tf.zeros_initializer())
-
- with tf.variable_scope("loss"):
- if is_training:
- # I.e., 0.1 dropout
- output_layer = tf.nn.dropout(output_layer, keep_prob=0.9)
-
- logits = tf.matmul(output_layer, output_weights, transpose_b=True)
- logits = tf.nn.bias_add(logits, output_bias)
- probabilities = tf.nn.softmax(logits, axis=-1)
- log_probs = tf.nn.log_softmax(logits, axis=-1)
-
- one_hot_labels = tf.one_hot(labels, depth=num_labels, dtype=tf.float32)
-
- per_example_loss = -tf.reduce_sum(one_hot_labels * log_probs, axis=-1)
- loss = tf.reduce_mean(per_example_loss)
- return (loss, per_example_loss, logits, probabilities)
-
- def model_fn_builder(bert_config, num_labels, init_checkpoint, learning_rate, num_train_steps,
- num_warmup_steps):
- """
-
- :param bert_config:
- :param num_labels:
- :param init_checkpoint:
- :param learning_rate:
- :param num_train_steps:
- :param num_warmup_steps:
- :param use_one_hot_embeddings:
- :return:
- """
- def model_fn(features, labels, mode, params):
- logger.info("*** Features ***")
- for name in sorted(features.keys()):
- logger.info(" name = %s, shape = %s" % (name, features[name].shape))
-
- input_ids = features["input_ids"]
- input_mask = features["input_mask"]
- segment_ids = features["segment_ids"]
- label_ids = features["label_ids"]
-
- is_training = (mode == tf.estimator.ModeKeys.TRAIN)
-
- (total_loss, per_example_loss, logits, probabilities) = create_model(
- bert_config, is_training, input_ids, input_mask, segment_ids, label_ids,
- num_labels)
-
- # resort variable from checkpoint file to init current graph
- tvars = tf.trainable_variables()
- initialized_variable_names = {}
- init_fn = None
- if init_checkpoint:
- (assignment_map, initialized_variable_names) = \
- modeling.get_assignment_map_from_checkpoint(tvars, init_checkpoint)
- tf.train.init_from_checkpoint(init_checkpoint, assignment_map)
- #variables_to_restore = tf.contrib.framework.get_model_variables()
- #init_fn = tf.contrib.framework.\
- # assign_from_checkpoint_fn(init_checkpoint,
- # variables_to_restore,
- # ignore_missing_vars=True)
-
- # 打印变量名称
- logger.info("**** Trainable Variables ****")
- for var in tvars:
- init_string = ""
- if var.name in initialized_variable_names:
- init_string = ", *INIT_FROM_CKPT*"
- logger.info(" name = %s, shape = %s%s", var.name, var.shape,
- init_string)
-
-
-
- output_spec = None
- if mode == tf.estimator.ModeKeys.TRAIN:
- train_op = optimization.create_optimizer(
- total_loss, learning_rate, num_train_steps, num_warmup_steps, use_tpu=False)
- output_spec = tf.estimator.EstimatorSpec(
- mode=mode,
- loss=total_loss,
- train_op=train_op
- )
- # training_hooks=[RestoreHook(init_fn)])
- elif mode == tf.estimator.ModeKeys.EVAL:
- def metric_fn(per_example_loss, label_ids, logits):
- predictions = tf.argmax(logits, axis=-1, output_type=tf.int32)
- accuracy = tf.metrics.accuracy(label_ids, predictions)
- loss = tf.metrics.mean(per_example_loss)
- return {
- "eval_accuracy": accuracy,
- "eval_loss": loss,
- }
- eval_metrics = metric_fn(per_example_loss, label_ids, logits)
- output_spec = tf.estimator.EstimatorSpec(
- mode=mode,
- loss=total_loss,
- eval_metric_ops=eval_metrics
- #evaluation_hooks=[RestoreHook(init_fn)]
- )
- else:
- output_spec = tf.estimator.EstimatorSpec(
- mode=mode, predictions=probabilities)
- return output_spec
- return model_fn
-
- def main(_):
- if not FLAGS.do_train and not FLAGS.do_eval and not FLAGS.do_predict:
- raise ValueError(
- "At least one of `do_train`, `do_eval` or `do_predict' must be True.")
-
- bert_config = modeling.BertConfig.from_json_file(FLAGS.bert_config_file)
-
- if FLAGS.max_seq_length > bert_config.max_position_embeddings:
- raise ValueError(
- "Cannot use sequence length %d because the BERT model "
- "was only trained up to sequence length %d" %
- (FLAGS.max_seq_length, bert_config.max_position_embeddings))
-
- if not os.path.exists(FLAGS.output_dir):
- os.makedirs(FLAGS.output_dir)
-
- processor = ThuProcessor()
- #定义分词器
- tokenizer = tokenization.FullTokenizer(
- vocab_file=FLAGS.vocab_file, do_lower_case=FLAGS.do_lower_case)
- # estimator 运行参数
- run_config = tf.estimator.RunConfig(
- model_dir=FLAGS.output_dir,
- save_summary_steps=FLAGS.save_summary_steps,
- save_checkpoints_steps=FLAGS.save_checkpoints_steps,
- keep_checkpoint_max=5,
- log_step_count_steps=500,
- session_config=tf.ConfigProto(log_device_placement=True)
- #session_config=tf.ConfigProto(log_device_placement=True,
- # device_count={'GPU': 1}))
- )
-
- train_examples = None
- num_train_steps = None
- num_warmup_steps = None
- if FLAGS.do_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)
- num_warmup_steps = int(num_train_steps * FLAGS.warmup_proportion)
-
- # get_labels() must be called after get_train_examoles or other examples
- label_list = processor.get_labels()
- logger.info('************ label_list=', ' '.join(label_list))
- model_fn = model_fn_builder(
- bert_config=bert_config,
- num_labels=len(label_list),
- init_checkpoint=FLAGS.init_checkpoint,
- learning_rate=FLAGS.learning_rate,
- num_train_steps=num_train_steps,
- num_warmup_steps=num_warmup_steps)
-
- # params是一个dict 里面的key是model_fn 里面用到的参数名称,value是对应的数据
- params = {
- 'batch_size': FLAGS.train_batch_size,
- }
-
- estimator = tf.estimator.Estimator(
- model_fn=model_fn,
- config=run_config,
- params=params,
- )
-
- if FLAGS.do_train:
- train_file = os.path.join(FLAGS.output_dir, "train.tf_record")
- file_based_convert_examples_to_features(
- train_examples, label_list, FLAGS.max_seq_length, tokenizer, train_file, 'train')
- logger.info("***** Running training *****")
- logger.info(" Num examples = %d", len(train_examples))
- logger.info(" Batch size = %d", FLAGS.train_batch_size)
- logger.info(" Num steps = %d", num_train_steps)
- train_input_fn = file_based_input_fn_builder(
- input_file=train_file,
- seq_length=FLAGS.max_seq_length,
- num_label=len(label_list),
- is_training=True,
- drop_remainder=True)
- estimator.train(input_fn=train_input_fn, max_steps=num_train_steps)
-
- if FLAGS.do_eval:
- eval_examples = processor.get_dev_examples(FLAGS.data_dir)
- eval_file = os.path.join(FLAGS.output_dir, "eval.tf_record")
- file_based_convert_examples_to_features(
- eval_examples, label_list, FLAGS.max_seq_length, tokenizer, eval_file, 'eval')
- logger.info("***** Running evaluation *****")
- logger.info(" Num examples = %d", len(eval_examples))
- logger.info(" Batch size = %d", FLAGS.eval_batch_size)
-
- eval_input_fn = file_based_input_fn_builder(
- input_file=eval_file,
- seq_length=FLAGS.max_seq_length,
- num_label=len(label_list),
- is_training=False,
- drop_remainder=False)
-
- result = estimator.evaluate(input_fn=eval_input_fn)
-
- output_eval_file = os.path.join(FLAGS.output_dir, "eval_results.txt")
- with tf.gfile.GFile(output_eval_file, "w") as writer:
- logger.info("***** Eval results *****")
- for key in sorted(result.keys()):
- logger.info(" %s = %s", key, str(result[key]))
- writer.write("%s = %s\n" % (key, str(result[key])))
-
- if FLAGS.do_predict:
- predict_examples = processor.get_test_examples(FLAGS.data_dir)
- predict_file = os.path.join(FLAGS.output_dir, "predict.tf_record")
- file_based_convert_examples_to_features(predict_examples, label_list,
- FLAGS.max_seq_length, tokenizer,
- predict_file, 'test')
-
- logger.info("***** Running prediction*****")
- logger.info(" Num examples = %d", len(predict_examples))
- logger.info(" Batch size = %d", FLAGS.predict_batch_size)
-
- predict_input_fn = file_based_input_fn_builder(
- input_file=predict_file,
- seq_length=FLAGS.max_seq_length,
- num_label=len(label_list),
- is_training=False,
- drop_remainder=False)
-
- result = estimator.predict(input_fn=predict_input_fn)
-
- output_predict_file = os.path.join(FLAGS.output_dir, "test_results.txt")
- with tf.gfile.GFile(output_predict_file, "w") as writer:
- logger.info("***** Predict results *****")
- for prediction in result:
- output_line = "\t".join(
- str(class_probability) for class_probability in prediction) + "\n"
- writer.write(output_line)
-
-
- def load_data():
- processer = ThuProcessor()
- example = processer.get_train_examples(FLAGS.data_dir)
- print()
-
-
- if __name__ == "__main__":
- # flags.mark_flag_as_required("data_dir")
- # flags.mark_flag_as_required("vocab_file")
- # flags.mark_flag_as_required("bert_config_file")
- # flags.mark_flag_as_required("output_dir")
- tf.app.run()
- # load_data()
-
-
-
-
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