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# coding=utf-8 # bert注解版 # raw author: Google # explain author：putdoor """BERT finetuning runner.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import collections import csv import os import modeling import random import optimization # 优化器 import tokenization # 令牌化 import tensorflow as tf flags = tf.flags FLAGS = flags.FLAGS # 用于支持命令行传递参数：如：python test_flags.py --model "My model" ## Required parameters # 这5项参数为必填参数，把None改为自己所需要 flags.DEFINE_string( "data_dir", "/home/work/my_model/bert/corpus/sentiment", # 此sentiment下应有三个文件，名称分别为:train.tsv, eval.tsv, test.tsv "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(os.path.dirname(os.path.abspath(__file__)), 'chinese_L-12_H-768_A-12/bert_config.json'), "The config json file corresponding to the pre-trained BERT model. " "This specifies the model architecture.") flags.DEFINE_string("task_name", "sentiment", "The name of the task to train.") # 自己随意起一个便于标识processor类的任务名,后续需填入main函数的字典 flags.DEFINE_string("vocab_file", os.path.join(os.path.dirname(os.path.abspath(__file__)), 'chinese_L-12_H-768_A-12/vocab.txt'), "The vocabulary file that the BERT model was trained on.") flags.DEFINE_string( "output_dir", "/home/work/my_model/output", "The output directory where the model checkpoints will be written.") # Other parameters flags.DEFINE_string( "init_checkpoint", None, "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", 128, "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("do_train", False, "Whether to run training.") flags.DEFINE_bool("do_eval", False, "Whether to run eval on the dev set.") flags.DEFINE_bool( "do_predict", False, "Whether to run the model in inference mode on the test set.") flags.DEFINE_integer("train_batch_size", 32, "Total batch size for training.") # mini-batch方式的梯度下降，每批处理32个样本 flags.DEFINE_integer("eval_batch_size", 8, "Total batch size for eval.") # batch为每批样本的数量，每批更新一次权重，使loss最小 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("num_train_epochs", 3.0, # 所有样本轮一次，即为一个epoch，增大此值，计算量增大，一个20000条数据的二分类问题，epochs=4大概要10分钟(16G 单GPU) "Total number of training epochs to perform.") flags.DEFINE_float( "warmup_proportion", 0.1, # 预热训练中，线性地增加学习率，详见注意力机制部分 "Proportion of training to perform linear learning rate warmup for. " "E.g., 0.1 = 10% of training.") flags.DEFINE_integer("save_checkpoints_steps", 1000, # 保存检查点时的步数，达到1000时，保存一次模型 "How often to save the model checkpoint.") flags.DEFINE_integer("iterations_per_loop", 1000, # 在每个estimator调用中执行多少步骤 "How many steps to make in each estimator call.") flags.DEFINE_bool("use_tpu", False, "Whether to use TPU or GPU/CPU.") # TPU config： tf.flags.DEFINE_string( "tpu_name", None, "The Cloud TPU to use for training. This should be either the name " "used when creating the Cloud TPU, or a grpc://ip.address.of.tpu:8470 " "url.") tf.flags.DEFINE_string( "tpu_zone", None, "[Optional] GCE zone where the Cloud TPU is located in. If not " "specified, we will attempt to automatically detect the GCE project from " "metadata.") tf.flags.DEFINE_string( "gcp_project", None, "[Optional] Project name for the Cloud TPU-enabled project. If not " "specified, we will attempt to automatically detect the GCE project from " "metadata.") tf.flags.DEFINE_string("master", None, "[Optional] TensorFlow master URL.") flags.DEFINE_integer( "num_tpu_cores", 8, "Only used if `use_tpu` is True. Total number of TPU cores to use.") class InputExample(object): # 每一行数据 to Inputexample对象 """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 PaddingInputExample(object): """当需要使用TPU训练时，eval和predict的数据需要是batch_size的整数倍，此类用于处理这类情况""" class InputFeatures(object): """A single set of features of data.""" def __init__(self, input_ids, # 输入部分：token embedding：表示词向量，第一个词是CLS，分隔词有SEP，是单词本身 input_mask, # 输入部分：position embedding：为了令transformer感知词与词之间的位置关系 segment_ids, # 输入部分：segment embedding：text_a与text_b的句子关系 label_id, # 输出部分：标签，对应Y is_real_example=True): self.input_ids = input_ids self.input_mask = input_mask self.segment_ids = segment_ids self.label_id = label_id self.is_real_example = is_real_example 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 tf.gfile.Open(input_file, "r") as f: reader = csv.reader(f, delimiter="\t", quotechar=quotechar) lines = [] for line in reader: lines.append(line) return lines class SentimentProcessor(DataProcessor): # 自定义的类，用于处理二分类问题 """Processor for the CoLA data set (GLUE version).""" def get_train_examples(self, data_dir): """See base class.""" return self._create_examples( self._read_tsv(os.path.join(data_dir, "train.tsv")), "train") def get_dev_examples(self, data_dir): """See base class.""" return self._create_examples( self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev") def get_test_examples(self, data_dir): """See base class.""" return self._create_examples( self._read_tsv(os.path.join(data_dir, "test.tsv")), "test") def get_labels(self): # 二分类问题返回的标签值为0，1 """See base class.""" return ["0", "1"] def _create_examples(self, lines, set_type): """Creates examples for the training and dev sets.""" examples = [] for (i, line) in enumerate(lines): # Only the test set has a header guid = "%s-%s" % (set_type, i) text_a = tokenization.convert_to_unicode(line[1]) label = tokenization.convert_to_unicode(line[0]) examples.append( InputExample(guid=guid, text_a=text_a, text_b=None, label=label)) return examples def convert_single_example(ex_index, example, label_list, max_seq_length, tokenizer): """Converts a single `InputExample` into a single `InputFeatures`.""" if isinstance(example, PaddingInputExample): # 相当于实例example为空，返回的数据 return InputFeatures( input_ids=[0] * max_seq_length, input_mask=[0] * max_seq_length, segment_ids=[0] * max_seq_length, label_id=0, is_real_example=False) label_map = {} for (i, label) in enumerate(label_list): # 标签映射 label_map[label] = i tokens_a = tokenizer.tokenize(example.text_a) tokens_b = None if example.text_b: tokens_b = tokenizer.tokenize(example.text_b) if tokens_b: # Modifies `tokens_a` and `tokens_b` in place so that the total # length is less than the specified length. # Account for [CLS], [SEP], [SEP] with "- 3" _truncate_seq_pair(tokens_a, tokens_b, max_seq_length - 3) # 截断序列对：将序列截断至最大允许长度 else: # Account for [CLS] and [SEP] with "- 2" if len(tokens_a) > max_seq_length - 2: tokens_a = tokens_a[0:(max_seq_length - 2)] # The convention in BERT is: # (a) For sequence pairs: # tokens: [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP] # type_ids: 0 0 0 0 0 0 0 0 1 1 1 1 1 1 # (b) For single sequences: # tokens: [CLS] the dog is hairy . [SEP] # type_ids: 0 0 0 0 0 0 0 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) if tokens_b: for token in tokens_b: tokens.append(token) segment_ids.append(1) tokens.append("[SEP]") segment_ids.append(1) # tokenizer ：是bert源码中提供的模块，其实主要作用就是将句子拆分成字，并且将字映射成id input_ids = tokenizer.convert_tokens_to_ids(tokens) input_mask = [1] * len(input_ids) # 暂时认为词与词之间的位置关系由索引决定就可以了[1,1,1...] --> index: 0,1,2... # Zero-pad up to the sequence length. while len(input_ids) < max_seq_length: input_ids.append(0) input_mask.append(0) segment_ids.append(0) assert len(input_ids) == max_seq_length assert len(input_mask) == max_seq_length assert len(segment_ids) == max_seq_length label_id = label_map[example.label] if ex_index < 5: tf.logging.info("*** Example ***") tf.logging.info("guid: %s" % (example.guid)) tf.logging.info("tokens: %s" % " ".join( [tokenization.printable_text(x) for x in tokens])) tf.logging.info("input_ids: %s" % " ".join([str(x) for x in input_ids])) tf.logging.info("input_mask: %s" % " ".join([str(x) for x in input_mask])) tf.logging.info("segment_ids: %s" % " ".join([str(x) for x in segment_ids])) tf.logging.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, is_real_example=True) return feature # 实现了俩功能：1.调用convert_single_example转化Input_example为Feature_example 2.转换为TFRecord格式，便于大型数据处理 def file_based_convert_examples_to_features( examples, label_list, max_seq_length, tokenizer, output_file): """Convert a set of `InputExample`s to a TFRecord file.""" # 写'train.tf_record'文件到output_dir下 # TFRecord内部采用二进制编码，加载快，对大型数据转换友好 # 此模块主要分为两个部分：1.TFRecord生成器, 2.Example模块 writer = tf.python_io.TFRecordWriter(output_file) # 最外层：是TFRecord生成器部分，内部需要传入tf_example for (ex_index, example) in enumerate(examples): if ex_index % 10000 == 0: tf.logging.info("Writing example %d of %d" % (ex_index, len(examples))) # 跟踪examples转换进度 # 把examples数据转化为features，用到前面的单次转换函数 feature = convert_single_example(ex_index, example, label_list, max_seq_length, tokenizer) # 这是Example模块 def create_int_feature(values): f = tf.train.Feature(int64_list=tf.train.Int64List(value=list(values))) return f features = collections.OrderedDict() # 其实就是一个字典 # 以下五句都是调用create_int_feature生成value往features字典中填充 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]) features["is_real_example"] = create_int_feature( [int(feature.is_real_example)]) tf_example = tf.train.Example(features=tf.train.Features(feature=features)) # 最外层是tf.train.Features()的实例，内层是feature的字典 writer.write(tf_example.SerializeToString()) writer.close() # 这是一个闭包，外层函数返回内层函数的引用，内层函数使用外层函数的参数 def file_based_input_fn_builder(input_file, seq_length, is_training, # 此input_file是TFRecord文件 drop_remainder): """Creates an `input_fn` closure to be passed to TPUEstimator.""" # 生成一个input_fn闭包传递给TPUEstimator name_to_features = { # 是tensorflow example协议中的一种解析，这里面，传入了shape和dtype "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), "is_real_example": tf.FixedLenFeature([], tf.int64), } def _decode_record(record, name_to_features): """Decodes a record to a TensorFlow example.""" example = tf.parse_single_example(record, name_to_features) # tf.Example only supports tf.int64, but the TPU only supports tf.int32. # So cast all int64 to int32. 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): """The actual input function.""" batch_size = params["batch_size"] # 对于训练，我们需要大量的并行的读取和洗牌 # 对于评估，我们不需要洗牌，并行的读取也无关紧要 d = tf.data.TFRecordDataset(input_file) if is_training: d = d.repeat() # 重复 d = d.shuffle(buffer_size=100) # 洗牌，缓冲区=100 d = d.apply( tf.contrib.data.map_and_batch( # 调用_decode_record函数：1.解析TFRecord为example 2.int64 to int32 lambda record: _decode_record(record, name_to_features), batch_size=batch_size, drop_remainder=drop_remainder)) return d return input_fn def _truncate_seq_pair(tokens_a, tokens_b, max_length): """Truncates a sequence pair in place to the maximum length.""" # 将序列对截断到最大长度max_length # This is a simple heuristic which will always truncate the longer sequence # one token at a time. This makes more sense than truncating an equal percent # of tokens from each, since if one sequence is very short then each token # that's truncated likely contains more information than a longer sequence. while True: total_length = len(tokens_a) + len(tokens_b) if total_length <= max_length: break if len(tokens_a) > len(tokens_b): tokens_a.pop() else: tokens_b.pop() # 做了两件事:1.使用modeling.py中的BerModel类创建模型 2.计算交叉熵损失loss def create_model(bert_config, is_training, input_ids, input_mask, segment_ids, # 创建分类器模型 labels, num_labels, use_one_hot_embeddings): """Creates a classification model.""" model = modeling.BertModel( config=bert_config, is_training=is_training, input_ids=input_ids, input_mask=input_mask, token_type_ids=segment_ids, use_one_hot_embeddings=use_one_hot_embeddings) # In the demo, we are doing a simple classification task on the entire # segment. # # If you want to use the token-level output, use model.get_sequence_output() # instead. output_layer = model.get_pooled_output() # 输出层 hidden_size = output_layer.shape[-1].value # 隐藏层大小 output_weights = tf.get_variable( # 输出层权重 "output_weights", [num_labels, hidden_size], initializer=tf.truncated_normal_initializer(stddev=0.02)) output_bias = tf.get_variable( # 输出层偏置 "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) # dropout：减小模型过拟合，保留90%的网络连接，随机drop 10% logits = tf.matmul(output_layer, output_weights, transpose_b=True) # w*x的矩阵乘法 logits = tf.nn.bias_add(logits, output_bias) # w*x + b probabilities = tf.nn.softmax(logits, axis=-1) # 把输出结果指数归一化映射到（0，1）区间 log_probs = tf.nn.log_softmax(logits, axis=-1) # 相当于对上式的每个值求log，落在 负无穷到0之间 # 标签one_hot编码，相当于增加标签维度，变稀疏化 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) # 交叉熵损失：交叉熵的值越小，两个概率分布就越接近 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, use_tpu, use_one_hot_embeddings): """Returns `model_fn` closure for TPUEstimator.""" # 返回给TPUEstimator闭包model_fn def model_fn(features, labels, mode, params): # pylint: disable=unused-argument """The `model_fn` for TPUEstimator.""" tf.logging.info("*** Features ***") for name in sorted(features.keys()): tf.logging.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_real_example = None if "is_real_example" in features: is_real_example = tf.cast(features["is_real_example"], dtype=tf.float32) # tf.cast()数据类型的转换 else: is_real_example = tf.ones(tf.shape(label_ids), dtype=tf.float32) # 生成所有数字为1的tensor is_training = (mode == tf.estimator.ModeKeys.TRAIN) # 1.创建bert的model 2.计算loss (total_loss, per_example_loss, logits, probabilities) = create_model( bert_config, is_training, input_ids, input_mask, segment_ids, label_ids, num_labels, use_one_hot_embeddings) tvars = tf.trainable_variables() initialized_variable_names = {} scaffold_fn = None if init_checkpoint: (assignment_map, initialized_variable_names ) = modeling.get_assignment_map_from_checkpoint(tvars, init_checkpoint) if use_tpu: def tpu_scaffold(): # 用于创建或收集训练模型通常需要的部件 tf.train.init_from_checkpoint(init_checkpoint, assignment_map) return tf.train.Scaffold() scaffold_fn = tpu_scaffold else: tf.train.init_from_checkpoint(init_checkpoint, assignment_map) tf.logging.info("**** Trainable Variables ****") for var in tvars: init_string = "" if var.name in initialized_variable_names: init_string = ", *INIT_FROM_CKPT*" tf.logging.info(" name = %s, shape = %s%s", var.name, var.shape, # 得到可用于训练的变量名 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) output_spec = tf.contrib.tpu.TPUEstimatorSpec( mode=mode, # 模式为训练模式 loss=total_loss, # 损失 train_op=train_op, # 操作 scaffold_fn=scaffold_fn) # 训练模型需要的部件 elif mode == tf.estimator.ModeKeys.EVAL: def metric_fn(per_example_loss, label_ids, logits, is_real_example): predictions = tf.argmax(logits, axis=-1, output_type=tf.int32) # 返回array中，最大值的索引 accuracy = tf.metrics.accuracy( labels=label_ids, predictions=predictions, weights=is_real_example) loss = tf.metrics.mean(values=per_example_loss, weights=is_real_example) return { "eval_accuracy": accuracy, "eval_loss": loss, } eval_metrics = (metric_fn, [per_example_loss, label_ids, logits, is_real_example]) output_spec = tf.contrib.tpu.TPUEstimatorSpec( mode=mode, # 模式为评估模式 loss=total_loss, eval_metrics=eval_metrics, # 此处内含：accuracy和loss scaffold_fn=scaffold_fn) else: output_spec = tf.contrib.tpu.TPUEstimatorSpec( mode=mode, # 此probabilities为softmax的计算的概率 predictions={"probabilities": probabilities}, scaffold_fn=scaffold_fn) return output_spec return model_fn def main(_): tf.logging.set_verbosity(tf.logging.INFO) processors = { "cola": ColaProcessor, # 填入自定义处理数据的类，必填项 "mnli": MnliProcessor, "mrpc": MrpcProcessor, "xnli": XnliProcessor, "sentiment": SentimentProcessor } tokenization.validate_case_matches_checkpoint(FLAGS.do_lower_case, # 验证实例，匹配检查点 FLAGS.init_checkpoint) 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) # 上面三个判断至少有一个为真执行下面语句 if FLAGS.max_seq_length > bert_config.max_position_embeddings: # 语句长度设置的比如为128，不应大于bert官方训练时的长度 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)) tf.gfile.MakeDirs(FLAGS.output_dir) # 此语句会创建output_dir目录，所以，只要指定，无需再单独创建 task_name = FLAGS.task_name.lower() # 传入的自定义类Sentiment名称小写化为sentiment if task_name not in processors: raise ValueError("Task not found: %s" % (task_name)) processor = processors[task_name]() # 类() --> 实例化 label_list = processor.get_labels() # 类.方法，此label_list为['0','1']类别标签集 tokenizer = tokenization.FullTokenizer( vocab_file=FLAGS.vocab_file, do_lower_case=FLAGS.do_lower_case) # 加载训练的中文词典，输入数据是否小写化 tpu_cluster_resolver = None # tpu集群处理 if FLAGS.use_tpu and FLAGS.tpu_name: # 使用tpu时创建tpu集群处理实例 tpu_cluster_resolver = tf.contrib.cluster_resolver.TPUClusterResolver( FLAGS.tpu_name, zone=FLAGS.tpu_zone, project=FLAGS.gcp_project) is_per_host = tf.contrib.tpu.InputPipelineConfig.PER_HOST_V2 # per_host：每主机，XLnet中num_core_per_host指的是每主机核数 run_config = tf.contrib.tpu.RunConfig( # tpu的运行配置 cluster=tpu_cluster_resolver, master=FLAGS.master, model_dir=FLAGS.output_dir, save_checkpoints_steps=FLAGS.save_checkpoints_steps, tpu_config=tf.contrib.tpu.TPUConfig( # tf.contrib模块是tf.nn的上一层的tf.layer的上层,主要提供一些图上的操作：如正则化，摘要操作。。。 iterations_per_loop=FLAGS.iterations_per_loop, # 在每个estimator调用中执行多少步，default中为1000步 num_shards=FLAGS.num_tpu_cores, # tpu核数，default为8 per_host_input_for_training=is_per_host)) train_examples = None num_train_steps = None num_warmup_steps = None if FLAGS.do_train: # 返回列表是一行一行的Inputexample对象，每行包括了guid，train_a,label.. train_examples = processor.get_train_examples(FLAGS.data_dir) # 训练的次数：(训练集的样本数/每批次大小)*训练几轮 num_train_steps = int( len(train_examples) / FLAGS.train_batch_size * FLAGS.num_train_epochs) # 举例：num_train_steps = (20000/32)*3 = 1800次， # 也就是权重更新，loss下降1800次 # 在预热学习中，线性地增加学习率 num_warmup_steps = int(num_train_steps * FLAGS.warmup_proportion) 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, use_tpu=FLAGS.use_tpu, use_one_hot_embeddings=FLAGS.use_tpu) # If TPU is not available, this will fall back to normal Estimator on CPU # or GPU. estimator = tf.contrib.tpu.TPUEstimator( # 构建估计器对象 use_tpu=FLAGS.use_tpu, model_fn=model_fn, config=run_config, train_batch_size=FLAGS.train_batch_size, eval_batch_size=FLAGS.eval_batch_size, predict_batch_size=FLAGS.predict_batch_size) if FLAGS.do_train: train_file = os.path.join(FLAGS.output_dir, "train.tf_record") # record：记录，档案 # 实现Input_example到Feature_example, TFRecord化 file_based_convert_examples_to_features( train_examples, label_list, FLAGS.max_seq_length, tokenizer, train_file) tf.logging.info("***** Running training *****") tf.logging.info(" Num examples = %d", len(train_examples)) tf.logging.info(" Batch size = %d", FLAGS.train_batch_size) tf.logging.info(" Num steps = %d", num_train_steps) # 调用此函数，完成：1.TFRecord to example 2.int64 to int32 train_input_fn = file_based_input_fn_builder( input_file=train_file, seq_length=FLAGS.max_seq_length, 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) num_actual_eval_examples = len(eval_examples) if FLAGS.use_tpu: # 不为整数倍时，填充 while len(eval_examples) % FLAGS.eval_batch_size != 0: eval_examples.append(PaddingInputExample()) 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) tf.logging.info("***** Running evaluation *****") # 数量 =（实际的数量，填充的数量） tf.logging.info(" Num examples = %d (%d actual, %d padding)", len(eval_examples), num_actual_eval_examples, len(eval_examples) - num_actual_eval_examples) tf.logging.info(" Batch size = %d", FLAGS.eval_batch_size) # This tells the estimator to run through the entire set. eval_steps = None # 遍历整个集合 # However, if running eval on the TPU, you will need to specify the # number of steps. if FLAGS.use_tpu: # 假定使用TPU时，前面整除处理已经成功，将得到eval_steps为整数值 assert len(eval_examples) % FLAGS.eval_batch_size == 0 eval_steps = int(len(eval_examples) // FLAGS.eval_batch_size) # 如果使用tpu的话，删除剩余的部分（可能是无法整除的部分） eval_drop_remainder = True if FLAGS.use_tpu else False # 1.TFRecord to example 2.int64 to int32 为estimator提供输入 eval_input_fn = file_based_input_fn_builder( input_file=eval_file, seq_length=FLAGS.max_seq_length, is_training=False, drop_remainder=eval_drop_remainder) result = estimator.evaluate(input_fn=eval_input_fn, steps=eval_steps) # 生成验证集评估指标数据 output_eval_file = os.path.join(FLAGS.output_dir, "eval_results.txt") with tf.gfile.GFile(output_eval_file, "w") as writer: tf.logging.info("***** Eval results *****") for key in sorted(result.keys()): tf.logging.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) num_actual_predict_examples = len(predict_examples) if FLAGS.use_tpu: while len(predict_examples) % FLAGS.predict_batch_size != 0: predict_examples.append(PaddingInputExample()) predict_file = os.path.join(FLAGS.output_dir, "predict.tf_record") # 1.调用convert_single_example转化Input_example为Feature_example # 2.转换为TFRecord格式，便于大型数据处理 file_based_convert_examples_to_features(predict_examples, label_list, FLAGS.max_seq_length, tokenizer, predict_file) tf.logging.info("***** Running prediction*****") tf.logging.info(" Num examples = %d (%d actual, %d padding)", len(predict_examples), num_actual_predict_examples, len(predict_examples) - num_actual_predict_examples) tf.logging.info(" Batch size = %d", FLAGS.predict_batch_size) predict_drop_remainder = True if FLAGS.use_tpu else False # 1.TFRecord to example 2.int64 to int32 为estimator提供输入 predict_input_fn = file_based_input_fn_builder( input_file=predict_file, seq_length=FLAGS.max_seq_length, is_training=False, drop_remainder=predict_drop_remainder) result = estimator.predict(input_fn=predict_input_fn) # 写测试集预测结果文件 output_predict_file = os.path.join(FLAGS.output_dir, "test_results.tsv") with tf.gfile.GFile(output_predict_file, "w") as writer: num_written_lines = 0 tf.logging.info("***** Predict results *****") for (i, prediction) in enumerate(result): # 写入probabilities的键值对，比如二分类：有预测为0的一列，预测为1的一列 probabilities = prediction["probabilities"] if i >= num_actual_predict_examples: break output_line = "\t".join( str(class_probability) # 例如：某一行class_probability为(0.98,0.02)->('0.98','0.02') for class_probability in probabilities) + "\n" writer.write(output_line) num_written_lines += 1 assert num_written_lines == num_actual_predict_examples if __name__ == "__main__": flags.mark_flag_as_required("data_dir") flags.mark_flag_as_required("task_name") 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()