摘要：本文为BERT模型的run_classifier.py的详细注释，便于了解这个微调脚本究竟做了什么？模型可调参数有哪些？自己写一个processor的类，需要注意哪些？想要运行这个脚本，需要传入哪些参数？尽在本文详细注释！
# 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()
版权声明：本文为weixin_43320501原创文章，遵循 CC 4.0 BY-SA 版权协议，转载请附上原文出处链接和本声明。